Sample records for single gene model
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Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells
Danaher, Patrick; Finak, Greg; Krouse, Michael; Wang, Alice; Webster, Philippa; Beechem, Joseph; Gottardo, Raphael
2014-01-01
Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome. PMID:25032992
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Direct observation of frequency modulated transcription in single cells using light activation
Larson, Daniel R; Fritzsch, Christoph; Sun, Liang; Meng, Xiuhau; Lawrence, David S; Singer, Robert H
2013-01-01
Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus. DOI: http://dx.doi.org/10.7554/eLife.00750.001 PMID:24069527
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A Nonlinear Model for Gene-Based Gene-Environment Interaction.
Sa, Jian; Liu, Xu; He, Tao; Liu, Guifen; Cui, Yuehua
2016-06-04
A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.
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A kernel regression approach to gene-gene interaction detection for case-control studies.
Larson, Nicholas B; Schaid, Daniel J
2013-11-01
Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.
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A single-molecule view of gene regulation in cancer
NASA Astrophysics Data System (ADS)
Larson, Daniel
2013-03-01
Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. Steroid receptors coordinate a diverse range of responses in higher eukaryotes and are involved in a wide range of human diseases, including cancer. Steroid receptor response elements are present throughout the human genome and modulate chromatin remodeling and transcription in both a local and long-range fashion. As such, steroid receptor-mediated transcription is a paradigm of genetic control in the metazoan nucleus. Moreover, the ligand-dependent nature of these transcription factors makes them appealing targets for therapeutic intervention, necessitating a quantitative understanding of how receptors control output from target genes. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single gene and follow dynamic synthesis of RNA from the activated locus. The response delay is a measure of time required for chromatin remodeling at a single gene.
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Evolution dynamics of a model for gene duplication under adaptive conflict
NASA Astrophysics Data System (ADS)
Ancliff, Mark; Park, Jeong-Man
2014-06-01
We present and solve the dynamics of a model for gene duplication showing escape from adaptive conflict. We use a Crow-Kimura quasispecies model of evolution where the fitness landscape is a function of Hamming distances from two reference sequences, which are assumed to optimize two different gene functions, to describe the dynamics of a mixed population of individuals with single and double copies of a pleiotropic gene. The evolution equations are solved through a spin coherent state path integral, and we find two phases: one is an escape from an adaptive conflict phase, where each copy of a duplicated gene evolves toward subfunctionalization, and the other is a duplication loss of function phase, where one copy maintains its pleiotropic form and the other copy undergoes neutral mutation. The phase is determined by a competition between the fitness benefits of subfunctionalization and the greater mutational load associated with maintaining two gene copies. In the escape phase, we find a dynamics of an initial population of single gene sequences only which escape adaptive conflict through gene duplication and find that there are two time regimes: until a time t* single gene sequences dominate, and after t* double gene sequences outgrow single gene sequences. The time t* is identified as the time necessary for subfunctionalization to evolve and spread throughout the double gene sequences, and we show that there is an optimum mutation rate which minimizes this time scale.
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Campbell, Kieran R; Yau, Christopher
2017-03-15
Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.
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TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.
Cordero, Pablo; Stuart, Joshua M
2017-01-01
The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolvingco-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway.
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Chen, Shuonan; Mar, Jessica C
2018-06-19
A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.
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Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai
2017-12-28
Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.
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Beta-Poisson model for single-cell RNA-seq data analyses.
Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Rantalainen, Mattias; Pawitan, Yudi
2016-07-15
Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Zuo, Erwei; Cai, Yi-Jun; Li, Kui; Wei, Yu; Wang, Bang-An; Sun, Yidi; Liu, Zhen; Liu, Jiwei; Hu, Xinde; Wei, Wei; Huo, Xiaona; Shi, Linyu; Tang, Cheng; Liang, Dan; Wang, Yan; Nie, Yan-Hong; Zhang, Chen-Chen; Yao, Xuan; Wang, Xing; Zhou, Changyang; Ying, Wenqin; Wang, Qifang; Chen, Ren-Chao; Shen, Qi; Xu, Guo-Liang; Li, Jinsong; Sun, Qiang; Xiong, Zhi-Qi; Yang, Hui
2017-07-01
The CRISPR/Cas9 system is an efficient gene-editing method, but the majority of gene-edited animals showed mosaicism, with editing occurring only in a portion of cells. Here we show that single gene or multiple genes can be completely knocked out in mouse and monkey embryos by zygotic injection of Cas9 mRNA and multiple adjacent single-guide RNAs (spaced 10-200 bp apart) that target only a single key exon of each gene. Phenotypic analysis of F0 mice following targeted deletion of eight genes on the Y chromosome individually demonstrated the robustness of this approach in generating knockout mice. Importantly, this approach delivers complete gene knockout at high efficiencies (100% on Arntl and 91% on Prrt2) in monkey embryos. Finally, we could generate a complete Prrt2 knockout monkey in a single step, demonstrating the usefulness of this approach in rapidly establishing gene-edited monkey models.
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Single-feature polymorphism discovery in the barley transcriptome
Rostoks, Nils; Borevitz, Justin O; Hedley, Peter E; Russell, Joanne; Mudie, Sharon; Morris, Jenny; Cardle, Linda; Marshall, David F; Waugh, Robbie
2005-01-01
A probe-level model for analysis of GeneChip gene-expression data is presented which identified more than 10,000 single-feature polymorphisms (SFP) between two barley genotypes. The method has good sensitivity, as 67% of known single-nucleotide polymorphisms (SNP) were called as SFPs. This method is applicable to all oligonucleotide microarray data, accounts for SNP effects in gene-expression data and represents an efficient and versatile approach for highly parallel marker identification in large genomes. PMID:15960806
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Flaws in the LNT single-hit model for cancer risk: An historical assessment.
Calabrese, Edward J
2017-10-01
The LNT single-hit model was derived from the Nobel Prize-winning research of Herman J. Muller who showed that x-rays could induce gene mutations in Drosophila and that the dose response for these so-called mutational events was linear. Lewis J. Stadler, another well-known and respected geneticist at the time, strongly disagreed with and challenged Muller's claims. Detailed evaluations by Stadler over a prolonged series of investigations revealed that Muller's experiments had induced gross heritable chromosomal damage instead of specific gene mutations as had been claimed by Muller at his Nobel Lecture. These X-ray-induced alterations became progressively more frequent and were of larger magnitude (more destructive) with increasing doses. Thus, Muller's claim of having induced discrete gene mutations represented a substantial speculative overreach and was, in fact, without proof. The post hoc arguments of Muller to support his gene mutation hypothesis were significantly challenged and weakened by a series of new findings in the areas of cytogenetics, reverse mutation, adaptive and repair processes, and modern molecular methods for estimating induced genetic damage. These findings represented critical and substantial limitations to Muller's hypothesis of X-ray-induced gene mutations. Furthermore, they challenged the scientific foundations used in support of the LNT single-hit model by severing the logical nexus between Muller's data on radiation-induced inheritable alterations and the LNT single-hit model. These findings exposed fundamental scientific flaws that undermined not only the seminal recommendation of the 1956 BEAR I Genetics Panel to adopt the LNT single-hit Model for risk assessment but also any rationale for its continued use in the present day. Copyright © 2017 Elsevier Inc. All rights reserved.
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Microarray-based cancer prediction using soft computing approach.
Wang, Xiaosheng; Gotoh, Osamu
2009-05-26
One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.
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Brookfield, John F. Y.; Johnson, Louise J.
2006-01-01
Some families of mammalian interspersed repetitive DNA, such as the Alu SINE sequence, appear to have evolved by the serial replacement of one active sequence with another, consistent with there being a single source of transposition: the “master gene.” Alternative models, in which multiple source sequences are simultaneously active, have been called “transposon models.” Transposon models differ in the proportion of elements that are active and in whether inactivation occurs at the moment of transposition or later. Here we examine the predictions of various types of transposon model regarding the patterns of sequence variation expected at an equilibrium between transposition, inactivation, and deletion. Under the master gene model, all bifurcations in the true tree of elements occur in a single lineage. We show that this property will also hold approximately for transposon models in which most elements are inactive and where at least some of the inactivation events occur after transposition. Such tree shapes are therefore not conclusive evidence for a single source of transposition. PMID:16790583
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Estimating intrinsic and extrinsic noise from single-cell gene expression measurements
Fu, Audrey Qiuyan; Pachter, Lior
2017-01-01
Gene expression is stochastic and displays variation (“noise”) both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization. PMID:27875323
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BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Vallejos, Catalina A.; Marioni, John C.; Richardson, Sylvia
2015-01-01
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach. PMID:26107944
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BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.
Vallejos, Catalina A; Marioni, John C; Richardson, Sylvia
2015-06-01
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell's lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.
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Ezawa, Kiyoshi; Innan, Hideki
2013-07-01
The population genetic behavior of mutations in sperm genes is theoretically investigated. We modeled the processes at two levels. One is the standard population genetic process, in which the population allele frequencies change generation by generation, depending on the difference in selective advantages. The other is the sperm competition during each genetic transmission from one generation to the next generation. For the sperm competition process, we formulate the situation where a huge number of sperm with alleles A and B, produced by a single heterozygous male, compete to fertilize a single egg. This "minimal model" demonstrates that a very slight difference in sperm performance amounts to quite a large difference between the alleles' winning probabilities. By incorporating this effect of paternity-sharing sperm competition into the standard population genetic process, we show that fierce sperm competition can enhance the fixation probability of a mutation with a very small phenotypic effect at the single-sperm level, suggesting a contribution of sperm competition to rapid amino acid substitutions in haploid-expressed sperm genes. Considering recent genome-wide demonstrations that a substantial fraction of the mammalian sperm genes are haploid expressed, our model could provide a potential explanation of rapid evolution of sperm genes with a wide variety of functions (as long as they are expressed in the haploid phase). Another advantage of our model is that it is applicable to a wide range of species, irrespective of whether the species is externally fertilizing, polygamous, or monogamous. The theoretical result was applied to mammalian data to estimate the selection intensity on nonsynonymous mutations in sperm genes.
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Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle.
Khatib, H; Huang, W; Wang, X; Tran, A H; Bindrim, A B; Schutzkus, V; Monson, R L; Yandell, B S
2009-05-01
Decrease in fertility and conception rates is a major cause of economic loss and cow culling in dairy herds. Conception rate is the product of fertilization rate and embryonic survival rate. Identification of genetic factors that cause the death of embryos is the first step in eliminating this problem from the population and thereby increasing reproductive efficiency. A candidate pathway approach was used to identify candidate genes affecting fertilization and embryo survival rates using an in vitro fertilization experimental system. A total of 7,413 in vitro fertilizations were performed using oocytes from 504 ovaries and semen samples from 10 different bulls. Fertilization rate was calculated as the number of cleaved embryos 48 h postfertilization out of the total number of oocytes exposed to sperm. Survival rate of embryos was calculated as the number of blastocysts on d 7 of development out of the number of total embryos cultured. All ovaries were genotyped for 8 genes in the POU1F1 signaling pathway. Single-gene analysis revealed significant associations of GHR, PRLR, STAT5A, and UTMP with survival rate and of POU1F1, GHR, STAT5A, and OPN with fertilization rate. To further characterize the contribution of the entire integrated POU1F1 pathway to fertilization and early embryonic survival, a model selection procedure was applied. Comparisons among the different models showed that interactions between adjacent genes in the pathway revealed a significant contribution to the variation in fertility traits compared with other models that analyzed only bull information or only genes without interactions. Moreover, some genes that were not significant in the single-gene analysis showed significant effects in the interaction analysis. Thus, we propose that single genes as well as an entire pathway can be used in selection programs to improve reproduction performance in dairy cattle.
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Woodhouse, Steven; Piterman, Nir; Wintersteiger, Christoph M; Göttgens, Berthold; Fisher, Jasmin
2018-05-25
Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.
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Miró-Bueno, Jesús M.; Rodríguez-Patón, Alfonso
2011-01-01
Negative and positive transcriptional feedback loops are present in natural and synthetic genetic oscillators. A single gene with negative transcriptional feedback needs a time delay and sufficiently strong nonlinearity in the transmission of the feedback signal in order to produce biochemical rhythms. A single gene with only positive transcriptional feedback does not produce oscillations. Here, we demonstrate that this single-gene network in conjunction with a simple negative interaction can also easily produce rhythms. We examine a model comprised of two well-differentiated parts. The first is a positive feedback created by a protein that binds to the promoter of its own gene and activates the transcription. The second is a negative interaction in which a repressor molecule prevents this protein from binding to its promoter. A stochastic study shows that the system is robust to noise. A deterministic study identifies that the dynamics of the oscillator are mainly driven by two types of biomolecules: the protein, and the complex formed by the repressor and this protein. The main conclusion of this paper is that a simple and usual negative interaction, such as degradation, sequestration or inhibition, acting on the positive transcriptional feedback of a single gene is a sufficient condition to produce reliable oscillations. One gene is enough and the positive transcriptional feedback signal does not need to activate a second repressor gene. This means that at the genetic level an explicit negative feedback loop is not necessary. The model needs neither cooperative binding reactions nor the formation of protein multimers. Therefore, our findings could help to clarify the design principles of cellular clocks and constitute a new efficient tool for engineering synthetic genetic oscillators. PMID:22205920
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Single-Copy Genes as Molecular Markers for Phylogenomic Studies in Seed Plants
De La Torre, Amanda R.; Sterck, Lieven; Cánovas, Francisco M.; Avila, Concepción; Merino, Irene; Cabezas, José Antonio; Cervera, María Teresa; Ingvarsson, Pär K.
2017-01-01
Phylogenetic relationships among seed plant taxa, especially within the gymnosperms, remain contested. In contrast to angiosperms, for which several genomic, transcriptomic and phylogenetic resources are available, there are few, if any, molecular markers that allow broad comparisons among gymnosperm species. With few gymnosperm genomes available, recently obtained transcriptomes in gymnosperms are a great addition to identifying single-copy gene families as molecular markers for phylogenomic analysis in seed plants. Taking advantage of an increasing number of available genomes and transcriptomes, we identified single-copy genes in a broad collection of seed plants and used these to infer phylogenetic relationships between major seed plant taxa. This study aims at extending the current phylogenetic toolkit for seed plants, assessing its ability for resolving seed plant phylogeny, and discussing potential factors affecting phylogenetic reconstruction. In total, we identified 3,072 single-copy genes in 31 gymnosperms and 2,156 single-copy genes in 34 angiosperms. All studied seed plants shared 1,469 single-copy genes, which are generally involved in functions like DNA metabolism, cell cycle, and photosynthesis. A selected set of 106 single-copy genes provided good resolution for the seed plant phylogeny except for gnetophytes. Although some of our analyses support a sister relationship between gnetophytes and other gymnosperms, phylogenetic trees from concatenated alignments without 3rd codon positions and amino acid alignments under the CAT + GTR model, support gnetophytes as a sister group to Pinaceae. Our phylogenomic analyses demonstrate that, in general, single-copy genes can uncover both recent and deep divergences of seed plant phylogeny. PMID:28460034
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General statistics of stochastic process of gene expression in eukaryotic cells.
Kuznetsov, V A; Knott, G D; Bonner, R F
2002-01-01
Thousands of genes are expressed at such very low levels (< or =1 copy per cell) that global gene expression analysis of rarer transcripts remains problematic. Ambiguity in identification of rarer transcripts creates considerable uncertainty in fundamental questions such as the total number of genes expressed in an organism and the biological significance of rarer transcripts. Knowing the distribution of the true number of genes expressed at each level and the corresponding gene expression level probability function (GELPF) could help resolve these uncertainties. We found that all observed large-scale gene expression data sets in yeast, mouse, and human cells follow a Pareto-like distribution model skewed by many low-abundance transcripts. A novel stochastic model of the gene expression process predicts the universality of the GELPF both across different cell types within a multicellular organism and across different organisms. This model allows us to predict the frequency distribution of all gene expression levels within a single cell and to estimate the number of expressed genes in a single cell and in a population of cells. A random "basal" transcription mechanism for protein-coding genes in all or almost all eukaryotic cell types is predicted. This fundamental mechanism might enhance the expression of rarely expressed genes and, thus, provide a basic level of phenotypic diversity, adaptability, and random monoallelic expression in cell populations. PMID:12136033
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Moore, Jason H; Gilbert, Joshua C; Tsai, Chia-Ti; Chiang, Fu-Tien; Holden, Todd; Barney, Nate; White, Bill C
2006-07-21
Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.
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ImpulseDE: detection of differentially expressed genes in time series data using impulse models.
Sander, Jil; Schultze, Joachim L; Yosef, Nir
2017-03-01
Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data. ImpulseDE is available on Bioconductor ( https://bioconductor.org/packages/ImpulseDE/ ). niryosef@berkeley.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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Woldesemayat, Adugna Abdi; Van Heusden, Peter; Ndimba, Bongani K; Christoffels, Alan
2017-12-22
Drought is the most disastrous abiotic stress that severely affects agricultural productivity worldwide. Understanding the biological basis of drought-regulated traits, requires identification and an in-depth characterization of genetic determinants using model organisms and high-throughput technologies. However, studies on drought tolerance have generally been limited to traditional candidate gene approach that targets only a single gene in a pathway that is related to a trait. In this study, we used sorghum, one of the model crops that is well adapted to arid regions, to mine genes and define determinants for drought tolerance using drought expression libraries and RNA-seq data. We provide an integrated and comparative in silico candidate gene identification, characterization and annotation approach, with an emphasis on genes playing a prominent role in conferring drought tolerance in sorghum. A total of 470 non-redundant functionally annotated drought responsive genes (DRGs) were identified using experimental data from drought responses by employing pairwise sequence similarity searches, pathway and interpro-domain analysis, expression profiling and orthology relation. Comparison of the genomic locations between these genes and sorghum quantitative trait loci (QTLs) showed that 40% of these genes were co-localized with QTLs known for drought tolerance. The genome reannotation conducted using the Program to Assemble Spliced Alignment (PASA), resulted in 9.6% of existing single gene models being updated. In addition, 210 putative novel genes were identified using AUGUSTUS and PASA based analysis on expression dataset. Among these, 50% were single exonic, 69.5% represented drought responsive and 5.7% were complete gene structure models. Analysis of biochemical metabolism revealed 14 metabolic pathways that are related to drought tolerance and also had a strong biological network, among categories of genes involved. Identification of these pathways, signifies the interplay of biochemical reactions that make up the metabolic network, constituting fundamental interface for sorghum defence mechanism against drought stress. This study suggests untapped natural variability in sorghum that could be used for developing drought tolerance. The data presented here, may be regarded as an initial reference point in functional and comparative genomics in the Gramineae family.
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Mutation and Chaos in Nonlinear Models of Heredity
Nawi, Ashraf Mohamed
2014-01-01
We shall explore a nonlinear discrete dynamical system that naturally occurs in population systems to describe a transmission of a trait from parents to their offspring. We consider a Mendelian inheritance for a single gene with three alleles and assume that to form a new generation, each gene has a possibility to mutate, that is, to change into a gene of the other kind. We investigate the derived models and observe chaotic behaviors of such models. PMID:25136693
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Shi, Xu; Gao, Weimin; Chao, Shih-hui
2013-01-01
Directly monitoring the stress response of microbes to their environments could be one way to inspect the health of microorganisms themselves, as well as the environments in which the microorganisms live. The ultimate resolution for such an endeavor could be down to a single-cell level. In this study, using the diatom Thalassiosira pseudonana as a model species, we aimed to measure gene expression responses of this organism to various stresses at a single-cell level. We developed a single-cell quantitative real-time reverse transcription-PCR (RT-qPCR) protocol and applied it to determine the expression levels of multiple selected genes under nitrogen, phosphate, and iron depletion stress conditions. The results, for the first time, provided a quantitative measurement of gene expression at single-cell levels in T. pseudonana and demonstrated that significant gene expression heterogeneity was present within the cell population. In addition, different expression patterns between single-cell- and bulk-cell-based analyses were also observed for all genes assayed in this study, suggesting that cell response heterogeneity needs to be taken into consideration in order to obtain accurate information that indicates the environmental stress condition. PMID:23315741
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Shi, Xu; Gao, Weimin; Chao, Shih-hui; Zhang, Weiwen; Meldrum, Deirdre R
2013-03-01
Directly monitoring the stress response of microbes to their environments could be one way to inspect the health of microorganisms themselves, as well as the environments in which the microorganisms live. The ultimate resolution for such an endeavor could be down to a single-cell level. In this study, using the diatom Thalassiosira pseudonana as a model species, we aimed to measure gene expression responses of this organism to various stresses at a single-cell level. We developed a single-cell quantitative real-time reverse transcription-PCR (RT-qPCR) protocol and applied it to determine the expression levels of multiple selected genes under nitrogen, phosphate, and iron depletion stress conditions. The results, for the first time, provided a quantitative measurement of gene expression at single-cell levels in T. pseudonana and demonstrated that significant gene expression heterogeneity was present within the cell population. In addition, different expression patterns between single-cell- and bulk-cell-based analyses were also observed for all genes assayed in this study, suggesting that cell response heterogeneity needs to be taken into consideration in order to obtain accurate information that indicates the environmental stress condition.
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Huang, D; Wu, W; Lu, L
2004-05-01
Amplification of resistance gene analogs (RGAs) is both a useful method for acquiring DNA markers closely linked to disease resistance (R) genes and a potential approach for the rapid cloning of R genes in plants. However, the screening of target sequences from among the numerous amplified RGAs can be very laborious. The amplification of RGAs from specific chromosomes could greatly reduce the number of RGAs to be screened and, consequently, speed up the identification of target RGAs. We have developed two methods for amplifying RGAs from single chromosomes. Method 1 uses products of Sau3A linker adaptor-mediated PCR (LAM-PCR) from a single chromosome as the templates for RGA amplification, while Method 2 directly uses a single chromosomal DNA molecule as the template. Using a pair of degenerate primers designed on the basis of the conserved nucleotide-binding-site motifs in many R genes, RGAs were successfully amplified from single chromosomes of pomelo using both these methods. Sequencing and cluster analysis of RGA clones obtained from single chromosomes revealed the number, type and organization of R-gene clusters on the chromosomes. We suggest that Method 1 is suitable for analyzing chromosomes that are unidentifiable under a microscope, while Method 2 is more appropriate when chromosomes can be clearly identified.
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Ezawa, Kiyoshi; Innan, Hideki
2013-01-01
The population genetic behavior of mutations in sperm genes is theoretically investigated. We modeled the processes at two levels. One is the standard population genetic process, in which the population allele frequencies change generation by generation, depending on the difference in selective advantages. The other is the sperm competition during each genetic transmission from one generation to the next generation. For the sperm competition process, we formulate the situation where a huge number of sperm with alleles A and B, produced by a single heterozygous male, compete to fertilize a single egg. This “minimal model” demonstrates that a very slight difference in sperm performance amounts to quite a large difference between the alleles’ winning probabilities. By incorporating this effect of paternity-sharing sperm competition into the standard population genetic process, we show that fierce sperm competition can enhance the fixation probability of a mutation with a very small phenotypic effect at the single-sperm level, suggesting a contribution of sperm competition to rapid amino acid substitutions in haploid-expressed sperm genes. Considering recent genome-wide demonstrations that a substantial fraction of the mammalian sperm genes are haploid expressed, our model could provide a potential explanation of rapid evolution of sperm genes with a wide variety of functions (as long as they are expressed in the haploid phase). Another advantage of our model is that it is applicable to a wide range of species, irrespective of whether the species is externally fertilizing, polygamous, or monogamous. The theoretical result was applied to mammalian data to estimate the selection intensity on nonsynonymous mutations in sperm genes. PMID:23666936
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Limited family structure and BRCA gene mutation status in single cases of breast cancer.
Weitzel, Jeffrey N; Lagos, Veronica I; Cullinane, Carey A; Gambol, Patricia J; Culver, Julie O; Blazer, Kathleen R; Palomares, Melanie R; Lowstuter, Katrina J; MacDonald, Deborah J
2007-06-20
An autosomal dominant pattern of hereditary breast cancer may be masked by small family size or transmission through males given sex-limited expression. To determine if BRCA gene mutations are more prevalent among single cases of early onset breast cancer in families with limited vs adequate family structure than would be predicted by currently available probability models. A total of 1543 women seen at US high-risk clinics for genetic cancer risk assessment and BRCA gene testing were enrolled in a prospective registry study between April 1997 and February 2007. Three hundred six of these women had breast cancer before age 50 years and no first- or second-degree relatives with breast or ovarian cancers. The main outcome measure was whether family structure, assessed from multigenerational pedigrees, predicts BRCA gene mutation status. Limited family structure was defined as fewer than 2 first- or second-degree female relatives surviving beyond age 45 years in either lineage. Family structure effect and mutation probability by the Couch, Myriad, and BRCAPRO models were assessed with stepwise multiple logistic regression. Model sensitivity and specificity were determined and receiver operating characteristic curves were generated. Family structure was limited in 153 cases (50%). BRCA gene mutations were detected in 13.7% of participants with limited vs 5.2% with adequate family structure. Family structure was a significant predictor of mutation status (odds ratio, 2.8; 95% confidence interval, 1.19-6.73; P = .02). Although none of the models performed well, receiver operating characteristic analysis indicated that modification of BRCAPRO output by a corrective probability index accounting for family structure was the most accurate BRCA gene mutation status predictor (area under the curve, 0.72; 95% confidence interval, 0.63-0.81; P<.001) for single cases of breast cancer. Family structure can affect the accuracy of mutation probability models. Genetic testing guidelines may need to be more inclusive for single cases of breast cancer when the family structure is limited and probability models need to be recreated using limited family history as an actual variable.
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Decoding the Regulatory Network for Blood Development from Single-Cell Gene Expression Measurements
Haghverdi, Laleh; Lilly, Andrew J.; Tanaka, Yosuke; Wilkinson, Adam C.; Buettner, Florian; Macaulay, Iain C.; Jawaid, Wajid; Diamanti, Evangelia; Nishikawa, Shin-Ichi; Piterman, Nir; Kouskoff, Valerie; Theis, Fabian J.; Fisher, Jasmin; Göttgens, Berthold
2015-01-01
Here we report the use of diffusion maps and network synthesis from state transition graphs to better understand developmental pathways from single cell gene expression profiling. We map the progression of mesoderm towards blood in the mouse by single-cell expression analysis of 3,934 cells, capturing cells with blood-forming potential at four sequential developmental stages. By adapting the diffusion plot methodology for dimensionality reduction to single-cell data, we reconstruct the developmental journey to blood at single-cell resolution. Using transitions between individual cellular states as input, we develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model that recapitulates blood development. Model predictions were validated by showing that Sox7 inhibits primitive erythropoiesis, and that Sox and Hox factors control early expression of Erg. We therefore demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that control organogenesis. PMID:25664528
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Kobayashi, Tetsuya
2016-01-01
Many plant-parasite interactions that include major plant resistance genes have subsequently been shown to exhibit features of gene-for-gene interactions between plant Resistance genes and parasite Avirulence genes. The brown planthopper (BPH) Nilaparvata lugens is an important pest of rice (Oryza sativa). Historically, major Resistance genes have played an important role in agriculture. As is common in gene-for-gene interactions, evolution of BPH virulence compromises the effectiveness of singly-deployed resistance genes. It is therefore surprising that laboratory studies of BPH have supported the conclusion that virulence is conferred by changes in many genes rather than a change in a single gene, as is proposed by the gene-for-gene model. Here we review the behaviour, physiology and genetics of the BPH in the context of host plant resistance. A problem for genetic understanding has been the use of various insect populations that differ in frequencies of virulent genotypes. We show that the previously proposed polygenic inheritance of BPH virulence can be explained by the heterogeneity of parental populations. Genetic mapping of Avirulence genes indicates that virulence is a monogenic trait. These evolving concepts, which have brought the gene-for-gene model back into the picture, are accelerating our understanding of rice-BPH interactions at the molecular level. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong
2013-01-01
For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809
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Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong
2013-01-01
For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.
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Gene expression distribution deconvolution in single-cell RNA sequencing.
Wang, Jingshu; Huang, Mo; Torre, Eduardo; Dueck, Hannah; Shaffer, Sydney; Murray, John; Raj, Arjun; Li, Mingyao; Zhang, Nancy R
2018-06-26
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND's noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Copyright © 2018 the Author(s). Published by PNAS.
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Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad
2017-01-01
The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635
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Shabanpoor, Fazel; McClorey, Graham; Saleh, Amer F.; Järver, Peter; Wood, Matthew J.A.; Gait, Michael J.
2015-01-01
The potential for therapeutic application of splice-switching oligonucleotides (SSOs) to modulate pre-mRNA splicing is increasingly evident in a number of diseases. However, the primary drawback of this approach is poor cell and in vivo oligonucleotide uptake efficacy. Biological activities can be significantly enhanced through the use of synthetically conjugated cationic cell penetrating peptides (CPPs). Studies to date have focused on the delivery of a single SSO conjugated to a CPP, but here we describe the conjugation of two phosphorodiamidate morpholino oligonucleotide (PMO) SSOs to a single CPP for simultaneous delivery and pre-mRNA targeting of two separate genes, exon 23 of the Dmd gene and exon 5 of the Acvr2b gene, in a mouse model of Duchenne muscular dystrophy. Conjugations of PMOs to a single CPP were carried out through an amide bond in one case and through a triazole linkage (‘click chemistry’) in the other. The most active bi-specific CPP–PMOs demonstrated comparable exon skipping levels for both pre-mRNA targets when compared to individual CPP–PMO conjugates both in cell culture and in vivo in the mdx mouse model. Thus, two SSOs with different target sequences conjugated to a single CPP are biologically effective and potentially suitable for future therapeutic exploitation. PMID:25468897
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Kozlov, Konstantin N.; Kulakovskiy, Ivan V.; Zubair, Asif; Marjoram, Paul; Lawrie, David S.; Nuzhdin, Sergey V.; Samsonova, Maria G.
2017-01-01
Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects. PMID:28898266
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Statistical method to compare massive parallel sequencing pipelines.
Elsensohn, M H; Leblay, N; Dimassi, S; Campan-Fournier, A; Labalme, A; Roucher-Boulez, F; Sanlaville, D; Lesca, G; Bardel, C; Roy, P
2017-03-01
Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect. Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines). The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.
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Successful transient expression of Cas9 and single guide RNA genes in Chlamydomonas reinhardtii.
Jiang, Wenzhi; Brueggeman, Andrew J; Horken, Kempton M; Plucinak, Thomas M; Weeks, Donald P
2014-11-01
The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system has become a powerful and precise tool for targeted gene modification (e.g., gene knockout and gene replacement) in numerous eukaryotic organisms. Initial attempts to apply this technology to a model, the single-cell alga, Chlamydomonas reinhardtii, failed to yield cells containing edited genes. To determine if the Cas9 and single guide RNA (sgRNA) genes were functional in C. reinhardtii, we tested the ability of a codon-optimized Cas9 gene along with one of four different sgRNAs to cause targeted gene disruption during a 24-h period immediately following transformation. All three exogenously supplied gene targets as well as the endogenous FKB12 (rapamycin sensitivity) gene of C. reinhardtii displayed distinct Cas9/sgRNA-mediated target site modifications as determined by DNA sequencing of cloned PCR amplicons of the target site region. Success in transient expression of Cas9 and sgRNA genes contrasted with the recovery of only a single rapamycin-resistant colony bearing an appropriately modified FKB12 target site in 16 independent transformation experiments involving >10(9) cells. Failure to recover transformants with intact or expressed Cas9 genes following transformation with the Cas9 gene alone (or even with a gene encoding a Cas9 lacking nuclease activity) provided strong suggestive evidence for Cas9 toxicity when Cas9 is produced constitutively in C. reinhardtii. The present results provide compelling evidence that Cas9 and sgRNA genes function properly in C. reinhardtii to cause targeted gene modifications and point to the need for a focus on development of methods to properly stem Cas9 production and/or activity following gene editing. Copyright © 2014, American Society for Microbiology. All Rights Reserved.
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Weidner, Christopher; Steinfath, Matthias; Wistorf, Elisa; Oelgeschläger, Michael; Schneider, Marlon R; Schönfelder, Gilbert
2017-08-16
Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.
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Green, Tina M; Jensen, Andreas K; Holst, René; Falgreen, Steffen; Bøgsted, Martin; de Stricker, Karin; Plesner, Torben; Mourits-Andersen, Torben; Frederiksen, Mikael; Johnsen, Hans E; Pedersen, Lars M; Møller, Michael B
2016-09-01
We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B-cell lymphoma (DLBCL). Real-time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de novo DLBCL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone). Including International Prognostic Index (IPI) as a variable in a penalized Cox regression, we investigated the association with disease progression for single genes or gene combinations in four models. The best model was validated in data from an online available R-CHOP treated cohort. With progression-free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAMxMIB1, GCSAMxCTGF and FOXP1xPDE4B. This model assigned 33% of patients (n = 60) to poor outcome with an estimated 3-year PFS of 40% vs. 87% for low risk (n = 61) and intermediate (n = 60) risk groups (P < 0·001). However, a simpler, IPI independent model incorporated LMO2 and BCL2 and assigned 33% of the patients with a 3-year PFS of 35% vs. 82% for low risk group (P < 0·001). We have documented the impact of a few single genes added to IPI for assignment in new drug trials. © 2016 John Wiley & Sons Ltd.
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Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P
2007-02-08
Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.
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Effect of misspecification of gene frequency on the two-point LOD score.
Pal, D K; Durner, M; Greenberg, D A
2001-11-01
In this study, we used computer simulation of simple and complex models to ask: (1) What is the penalty in evidence for linkage when the assumed gene frequency is far from the true gene frequency? (2) If the assumed model for gene frequency and inheritance are misspecified in the analysis, can this lead to a higher maximum LOD score than that obtained under the true parameters? Linkage data simulated under simple dominant, recessive, dominant and recessive with reduced penetrance, and additive models, were analysed assuming a single locus with both the correct and incorrect dominance model and assuming a range of different gene frequencies. We found that misspecifying the analysis gene frequency led to little penalty in maximum LOD score in all models examined, especially if the assumed gene frequency was lower than the generating one. Analysing linkage data assuming a gene frequency of the order of 0.01 for a dominant gene, and 0.1 for a recessive gene, appears to be a reasonable tactic in the majority of realistic situations because underestimating the gene frequency, even when the true gene frequency is high, leads to little penalty in the LOD score.
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Pham, Nikki T.; Wei, Tong; Schackwitz, Wendy S.; Lipzen, Anna M.; Duong, Phat Q.; Jones, Kyle C.; Ruan, Deling; Bauer, Diane; Peng, Yi; Schmutz, Jeremy
2017-01-01
The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake (Oryza sativa ssp japonica), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportion of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. This work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations. PMID:28576844
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A powerful score-based test statistic for detecting gene-gene co-association.
Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun
2016-01-29
The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.
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Knowledge-driven genomic interactions: an application in ovarian cancer.
Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D
2014-01-01
Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.
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A double-strand break can trigger immunoglobulin gene conversion
Bastianello, Giulia; Arakawa, Hiroshi
2017-01-01
All three B cell-specific activities of the immunoglobulin (Ig) gene re-modeling system—gene conversion, somatic hypermutation and class switch recombination—require activation-induced deaminase (AID). AID-induced DNA lesions must be further processed and dissected into different DNA recombination pathways. In order to characterize potential intermediates for Ig gene conversion, we inserted an I-SceI recognition site into the complementarity determining region 1 (CDR1) of the Ig light chain locus of the AID knockout DT40 cell line, and conditionally expressed I-SceI endonuclease. Here, we show that a double-strand break (DSB) in CDR1 is sufficient to trigger Ig gene conversion in the absence of AID. The pattern and pseudogene usage of DSB-induced gene conversion were comparable to those of AID-induced gene conversion; surprisingly, sometimes a single DSB induced multiple gene conversion events. These constitute direct evidence that a DSB in the V region can be an intermediate for gene conversion. The fate of the DNA lesion downstream of a DSB had more flexibility than that of AID, suggesting two alternative models: (i) DSBs during the physiological gene conversion are in the minority compared to single-strand breaks (SSBs), which are frequently generated following DNA deamination, or (ii) the physiological gene conversion is mediated by a tightly regulated DSB that is locally protected from non-homologous end joining (NHEJ) or other non-homologous DNA recombination machineries. PMID:27701075
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Imaging mRNA In Vivo, from Birth to Death.
Tutucci, Evelina; Livingston, Nathan M; Singer, Robert H; Wu, Bin
2018-05-20
RNA is the fundamental information transfer system in the cell. The ability to follow single messenger RNAs (mRNAs) from transcription to degradation with fluorescent probes gives quantitative information about how the information is transferred from DNA to proteins. This review focuses on the latest technological developments in the field of single-mRNA detection and their usage to study gene expression in both fixed and live cells. By describing the application of these imaging tools, we follow the journey of mRNA from transcription to decay in single cells, with single-molecule resolution. We review current theoretical models for describing transcription and translation that were generated by single-molecule and single-cell studies. These methods provide a basis to study how single-molecule interactions generate phenotypes, fundamentally changing our understating of gene expression regulation.
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ERIC Educational Resources Information Center
Restifo, Linda L.
2005-01-01
"Drosophila melanogaster" is emerging as a valuable genetic model system for the study of mental retardation (MR). MR genes are remarkably similar between humans and fruit flies. Cognitive behavioral assays can detect reductions in learning and memory in flies with mutations in MR genes. Neuroanatomical methods, including some at single-neuron…
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A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction
De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David
2016-01-01
Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847
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Kogelman, Lisette J A; Zhernakova, Daria V; Westra, Harm-Jan; Cirera, Susanna; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N
2015-10-20
Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.
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Genetic heterogeneity in autism: From single gene to a pathway perspective.
An, Joon Yong; Claudianos, Charles
2016-09-01
The extreme genetic heterogeneity of autism spectrum disorder (ASD) represents a major challenge. Recent advances in genetic screening and systems biology approaches have extended our knowledge of the genetic etiology of ASD. In this review, we discuss the paradigm shift from a single gene causation model to pathway perturbation model as a guide to better understand the pathophysiology of ASD. We discuss recent genetic findings obtained through next-generation sequencing (NGS) and examine various integrative analyses using systems biology and complex networks approaches that identify convergent patterns of genetic elements associated with ASD. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.
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Selective Gene Transfection of Individual Cells In Vitro with Plasmonic Nanobubbles
Lukianova-Hleb, Ekaterina; Samaniego, Adam P.; Wen, Jianguo; Metelitsa, Leonid; Chang, Chung-Che; Lapotko, Dmitri
2011-01-01
Gene delivery and transfection of eukaryotic cells is widely used for research and for developing gene cell therapy. However, the existing methods lack selectivity, efficacy and safety when heterogeneous cell systems must be treated. We report a new method that employs plasmonic nanobubbles (PNBs) for delivery and transfection. A PNB is a novel, tunable cellular agent with a dual mechanical and optical action due to the formation of the vapor nanobubble around a transiently heated gold nanoparticle upon its exposure to a laser pulse. PNBs enabled the mechanical injection of the extracellular cDNA plasmid into the cytoplasm of individual target living cells, cultured leukemia cells and human CD34+CD117+ stem cells and expression of a green fluorescent protein (GFP) in those cells. PNB generation and lifetime correlated with the expression of green fluorescent protein in PNB-treated cells. Optical scattering by PNBs additionally provided the detection of the target cells and the guidance of cDNA injection at single cell level. In both cell models PNBs demonstrated a gene transfection effect in a single pulse treatment with high selectivity, efficacy and safety. Thus, PNBs provided targeted gene delivery at the single cell level in a single pulse procedure that can be used for safe and effective gene therapy. PMID:21315120
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Selective gene transfection of individual cells in vitro with plasmonic nanobubbles.
Lukianova-Hleb, Ekaterina Y; Samaniego, Adam P; Wen, Jianguo; Metelitsa, Leonid S; Chang, Chung-Che; Lapotko, Dmitri O
2011-06-10
Gene delivery and transfection of eukaryotic cells are widely used for research and for developing gene cell therapy. However, the existing methods lack selectivity, efficacy and safety when heterogeneous cell systems must be treated. We report a new method that employs plasmonic nanobubbles (PNBs) for delivery and transfection. A PNB is a novel, tunable cellular agent with a dual mechanical and optical action due to the formation of the vapor nanobubble around a transiently heated gold nanoparticle upon its exposure to a laser pulse. PNBs enabled the mechanical injection of the extracellular cDNA plasmid into the cytoplasm of individual target living cells, cultured leukemia cells and human CD34+ CD117+ stem cells and expression of a green fluorescent protein (GFP) in those cells. PNB generation and lifetime correlated with the expression of green fluorescent protein in PNB-treated cells. Optical scattering by PNBs additionally provided the detection of the target cells and the guidance of cDNA injection at single cell level. In both cell models PNBs demonstrated a gene transfection effect in a single pulse treatment with high selectivity, efficacy and safety. Thus, PNBs provided targeted gene delivery at the single cell level in a single pulse procedure that can be used for safe and effective gene therapy. Copyright © 2011 Elsevier B.V. All rights reserved.
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Modeling stochastic noise in gene regulatory systems
Meister, Arwen; Du, Chao; Li, Ye Henry; Wong, Wing Hung
2014-01-01
The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems. PMID:25632368
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Morphological Identification and Single-Cell Genomics of Marine Diplonemids.
Gawryluk, Ryan M R; Del Campo, Javier; Okamoto, Noriko; Strassert, Jürgen F H; Lukeš, Julius; Richards, Thomas A; Worden, Alexandra Z; Santoro, Alyson E; Keeling, Patrick J
2016-11-21
Recent global surveys of marine biodiversity have revealed that a group of organisms known as "marine diplonemids" constitutes one of the most abundant and diverse planktonic lineages [1]. Though discovered over a decade ago [2, 3], their potential importance was unrecognized, and our knowledge remains restricted to a single gene amplified from environmental DNA, the 18S rRNA gene (small subunit [SSU]). Here, we use single-cell genomics (SCG) and microscopy to characterize ten marine diplonemids, isolated from a range of depths in the eastern North Pacific Ocean. Phylogenetic analysis confirms that the isolates reflect the entire range of marine diplonemid diversity, and comparisons to environmental SSU surveys show that sequences from the isolates range from rare to superabundant, including the single most common marine diplonemid known. SCG generated a total of ∼915 Mbp of assembled sequence across all ten cells and ∼4,000 protein-coding genes with homologs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology database, distributed across categories expected for heterotrophic protists. Models of highly conserved genes indicate a high density of non-canonical introns, lacking conventional GT-AG splice sites. Mapping metagenomic datasets [4] to SCG assemblies reveals virtually no overlap, suggesting that nuclear genomic diversity is too great for representative SCG data to provide meaningful phylogenetic context to metagenomic datasets. This work provides an entry point to the future identification, isolation, and cultivation of these elusive yet ecologically important cells. The high density of nonconventional introns, however, also portends difficulty in generating accurate gene models and highlights the need for the establishment of stable cultures and transcriptomic analyses. Copyright © 2016 Elsevier Ltd. All rights reserved.
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A Single Multiplex crRNA Array for FnCpf1-Mediated Human Genome Editing.
Sun, Huihui; Li, Fanfan; Liu, Jie; Yang, Fayu; Zeng, Zhenhai; Lv, Xiujuan; Tu, Mengjun; Liu, Yeqing; Ge, Xianglian; Liu, Changbao; Zhao, Junzhao; Zhang, Zongduan; Qu, Jia; Song, Zongming; Gu, Feng
2018-06-15
Cpf1 has been harnessed as a tool for genome manipulation in various species because of its simplicity and high efficiency. Our recent study demonstrated that FnCpf1 could be utilized for human genome editing with notable advantages for target sequence selection due to the flexibility of the protospacer adjacent motif (PAM) sequence. Multiplex genome editing provides a powerful tool for targeting members of multigene families, dissecting gene networks, modeling multigenic disorders in vivo, and applying gene therapy. However, there are no reports at present that show FnCpf1-mediated multiplex genome editing via a single customized CRISPR RNA (crRNA) array. In the present study, we utilize a single customized crRNA array to simultaneously target multiple genes in human cells. In addition, we also demonstrate that a single customized crRNA array to target multiple sites in one gene could be achieved. Collectively, FnCpf1, a powerful genome-editing tool for multiple genomic targets, can be harnessed for effective manipulation of the human genome. Copyright © 2018 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.
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Rivera-Torres, Natalia; Banas, Kelly; Bialk, Pawel; Bloh, Kevin M; Kmiec, Eric B
2017-01-01
CRISPR/Cas9 and single-stranded DNA oligonucleotides (ssODNs) have been used to direct the repair of a single base mutation in human genes. Here, we examine a method designed to increase the precision of RNA guided genome editing in human cells by utilizing a CRISPR/Cas9 ribonucleoprotein (RNP) complex to initiate DNA cleavage. The RNP is assembled in vitro and induces a double stranded break at a specific site surrounding the mutant base designated for correction by the ssODN. We use an integrated mutant eGFP gene, bearing a single base change rendering the expressed protein nonfunctional, as a single copy target in HCT 116 cells. We observe significant gene correction activity of the mutant base, promoted by the RNP and single-stranded DNA oligonucleotide with validation through genotypic and phenotypic readout. We demonstrate that all individual components must be present to obtain successful gene editing. Importantly, we examine the genotype of individually sorted corrected and uncorrected clonally expanded cell populations for the mutagenic footprint left by the action of these gene editing tools. While the DNA sequence of the corrected population is exact with no adjacent sequence modification, the uncorrected population exhibits heterogeneous mutagenicity with a wide variety of deletions and insertions surrounding the target site. We designate this type of DNA aberration as on-site mutagenicity. Analyses of two clonal populations bearing specific DNA insertions surrounding the target site, indicate that point mutation repair has occurred at the level of the gene. The phenotype, however, is not rescued because a section of the single-stranded oligonucleotide has been inserted altering the reading frame and generating truncated proteins. These data illustrate the importance of analysing mutagenicity in uncorrected cells. Our results also form the basis of a simple model for point mutation repair directed by a short single-stranded DNA oligonucleotides and CRISPR/Cas9 ribonucleoprotein complex.
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Rivera-Torres, Natalia; Bialk, Pawel; Bloh, Kevin M.; Kmiec, Eric B.
2017-01-01
CRISPR/Cas9 and single-stranded DNA oligonucleotides (ssODNs) have been used to direct the repair of a single base mutation in human genes. Here, we examine a method designed to increase the precision of RNA guided genome editing in human cells by utilizing a CRISPR/Cas9 ribonucleoprotein (RNP) complex to initiate DNA cleavage. The RNP is assembled in vitro and induces a double stranded break at a specific site surrounding the mutant base designated for correction by the ssODN. We use an integrated mutant eGFP gene, bearing a single base change rendering the expressed protein nonfunctional, as a single copy target in HCT 116 cells. We observe significant gene correction activity of the mutant base, promoted by the RNP and single-stranded DNA oligonucleotide with validation through genotypic and phenotypic readout. We demonstrate that all individual components must be present to obtain successful gene editing. Importantly, we examine the genotype of individually sorted corrected and uncorrected clonally expanded cell populations for the mutagenic footprint left by the action of these gene editing tools. While the DNA sequence of the corrected population is exact with no adjacent sequence modification, the uncorrected population exhibits heterogeneous mutagenicity with a wide variety of deletions and insertions surrounding the target site. We designate this type of DNA aberration as on-site mutagenicity. Analyses of two clonal populations bearing specific DNA insertions surrounding the target site, indicate that point mutation repair has occurred at the level of the gene. The phenotype, however, is not rescued because a section of the single-stranded oligonucleotide has been inserted altering the reading frame and generating truncated proteins. These data illustrate the importance of analysing mutagenicity in uncorrected cells. Our results also form the basis of a simple model for point mutation repair directed by a short single-stranded DNA oligonucleotides and CRISPR/Cas9 ribonucleoprotein complex. PMID:28052104
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Translational animal models of autism and neurodevelopmental disorders.
Crawley, Jacqueline N
2012-09-01
Autism is a neurodevelopmental disorder whose diagnosis is based on three behavioral criteria: unusual reciprocal social interactions, deficits in communication, and stereotyped repetitive behaviors with restricted interests. A large number of de novo single gene mutations and chromosomal deletions are associated with autism spectrum disorders. Based on the strong genetic evidence, mice with targeted mutations in homologous genes have been generated as translational research tools. Mouse models of autism have revealed behavioral and biological outcomes of mutations in risk genes. The field is now poised to employ the most robust phenotypes in the most replicable mouse models for preclinical screening of novel therapeutics.
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Problems in mechanistic theoretical models for cell transformation by ionizing radiation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chatterjee, A.; Holley, W.R.
1991-10-01
A mechanistic model based on yields of double strand breaks has been developed to determine the dose response curves for cell transformation frequencies. At its present stage the model is applicable to immortal cell lines and to various qualities (X-rays, Neon and Iron) of ionizing radiation. Presently, we have considered four types of processes which can lead to activation phenomena: (1) point mutation events on a regulatory segment of selected oncogenes, (2) inactivation of suppressor genes, through point mutation, (3) deletion of a suppressor gene by a single track, and (4) deletion of a suppressor gene by two tracks.
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Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation
NASA Technical Reports Server (NTRS)
Mjolsness, Eric
2000-01-01
This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.
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Quantitative gene expression analysis in Caenorhabditis elegans using single molecule RNA FISH.
Bolková, Jitka; Lanctôt, Christian
2016-04-01
Advances in fluorescent probe design and synthesis have allowed the uniform in situ labeling of individual RNA molecules. In a technique referred to as single molecule RNA FISH (smRNA FISH), the labeled RNA molecules can be imaged as diffraction-limited spots and counted using image analysis algorithms. Single RNA counting has provided valuable insights into the process of gene regulation. This microscopy-based method has often revealed a high cell-to-cell variability in expression levels, which has in turn led to a growing interest in investigating the biological significance of gene expression noise. Here we describe the application of the smRNA FISH technique to samples of Caenorhabditis elegans, a well-characterized model organism. Copyright © 2015 Elsevier Inc. All rights reserved.
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Natural killer cell receptor genes in the family Equidae: not only Ly49.
Futas, Jan; Horin, Petr
2013-01-01
Natural killer (NK) cells have important functions in immunity. NK recognition in mammals can be mediated through killer cell immunoglobulin-like receptors (KIR) and/or killer cell lectin-like Ly49 receptors. Genes encoding highly variable NK cell receptors (NKR) represent rapidly evolving genomic regions. No single conservative model of NKR genes was observed in mammals. Single-copy low polymorphic NKR genes present in one mammalian species may expand into highly polymorphic multigene families in other species. In contrast to other non-rodent mammals, multiple Ly49-like genes appear to exist in the horse, while no functional KIR genes were observed in this species. In this study, Ly49 and KIR were sought and their evolution was characterized in the entire family Equidae. Genomic sequences retrieved showed the presence of at least five highly conserved polymorphic Ly49 genes in horses, asses and zebras. These findings confirmed that the expansion of Ly49 occurred in the entire family. Several KIR-like sequences were also identified in the genome of Equids. Besides a previously identified non-functional KIR-Immunoglobulin-like transcript fusion gene (KIR-ILTA) and two putative pseudogenes, a KIR3DL-like sequence was analyzed. In contrast to previous observations made in the horse, the KIR3DL sequence, genomic organization and mRNA expression suggest that all Equids might produce a functional KIR receptor protein molecule with a single non-mutated immune tyrosine-based inhibition motif (ITIM) domain. No evidence for positive selection in the KIR3DL gene was found. Phylogenetic analysis including rhinoceros and tapir genomic DNA and deduced amino acid KIR-related sequences showed differences between families and even between species within the order Perissodactyla. The results suggest that the order Perissodactyla and its family Equidae with expanded Ly49 genes and with a potentially functional KIR gene may represent an interesting model for evolutionary biology of NKR genes.
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Natural Killer Cell Receptor Genes in the Family Equidae: Not only Ly49
Futas, Jan; Horin, Petr
2013-01-01
Natural killer (NK) cells have important functions in immunity. NK recognition in mammals can be mediated through killer cell immunoglobulin-like receptors (KIR) and/or killer cell lectin-like Ly49 receptors. Genes encoding highly variable NK cell receptors (NKR) represent rapidly evolving genomic regions. No single conservative model of NKR genes was observed in mammals. Single-copy low polymorphic NKR genes present in one mammalian species may expand into highly polymorphic multigene families in other species. In contrast to other non-rodent mammals, multiple Ly49-like genes appear to exist in the horse, while no functional KIR genes were observed in this species. In this study, Ly49 and KIR were sought and their evolution was characterized in the entire family Equidae. Genomic sequences retrieved showed the presence of at least five highly conserved polymorphic Ly49 genes in horses, asses and zebras. These findings confirmed that the expansion of Ly49 occurred in the entire family. Several KIR-like sequences were also identified in the genome of Equids. Besides a previously identified non-functional KIR-Immunoglobulin-like transcript fusion gene (KIR-ILTA) and two putative pseudogenes, a KIR3DL-like sequence was analyzed. In contrast to previous observations made in the horse, the KIR3DL sequence, genomic organization and mRNA expression suggest that all Equids might produce a functional KIR receptor protein molecule with a single non-mutated immune tyrosine-based inhibition motif (ITIM) domain. No evidence for positive selection in the KIR3DL gene was found. Phylogenetic analysis including rhinoceros and tapir genomic DNA and deduced amino acid KIR-related sequences showed differences between families and even between species within the order Perissodactyla. The results suggest that the order Perissodactyla and its family Equidae with expanded Ly49 genes and with a potentially functional KIR gene may represent an interesting model for evolutionary biology of NKR genes. PMID:23724088
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Armbruster, Chelsie E; Forsyth-DeOrnellas, Valerie; Johnson, Alexandra O; Smith, Sara N; Zhao, Lili; Wu, Weisheng; Mobley, Harry L T
2017-06-01
The Gram-negative bacterium Proteus mirabilis is a leading cause of catheter-associated urinary tract infections (CAUTIs), which are often polymicrobial. Numerous prior studies have uncovered virulence factors for P. mirabilis pathogenicity in a murine model of ascending UTI, but little is known concerning pathogenesis during CAUTI or polymicrobial infection. In this study, we utilized five pools of 10,000 transposon mutants each and transposon insertion-site sequencing (Tn-Seq) to identify the full arsenal of P. mirabilis HI4320 fitness factors for single-species versus polymicrobial CAUTI with Providencia stuartii BE2467. 436 genes in the input pools lacked transposon insertions and were therefore concluded to be essential for P. mirabilis growth in rich medium. 629 genes were identified as P. mirabilis fitness factors during single-species CAUTI. Tn-Seq from coinfection with P. stuartii revealed 217/629 (35%) of the same genes as identified by single-species Tn-Seq, and 1353 additional factors that specifically contribute to colonization during coinfection. Mutants were constructed in eight genes of interest to validate the initial screen: 7/8 (88%) mutants exhibited the expected phenotypes for single-species CAUTI, and 3/3 (100%) validated the expected phenotypes for polymicrobial CAUTI. This approach provided validation of numerous previously described P. mirabilis fitness determinants from an ascending model of UTI, the discovery of novel fitness determinants specifically for CAUTI, and a stringent assessment of how polymicrobial infection influences fitness requirements. For instance, we describe a requirement for branched-chain amino acid biosynthesis by P. mirabilis during coinfection due to high-affinity import of leucine by P. stuartii. Further investigation of genes and pathways that provide a competitive advantage during both single-species and polymicrobial CAUTI will likely provide robust targets for therapeutic intervention to reduce P. mirabilis CAUTI incidence and severity.
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Smith, Sara N.; Zhao, Lili; Wu, Weisheng
2017-01-01
The Gram-negative bacterium Proteus mirabilis is a leading cause of catheter-associated urinary tract infections (CAUTIs), which are often polymicrobial. Numerous prior studies have uncovered virulence factors for P. mirabilis pathogenicity in a murine model of ascending UTI, but little is known concerning pathogenesis during CAUTI or polymicrobial infection. In this study, we utilized five pools of 10,000 transposon mutants each and transposon insertion-site sequencing (Tn-Seq) to identify the full arsenal of P. mirabilis HI4320 fitness factors for single-species versus polymicrobial CAUTI with Providencia stuartii BE2467. 436 genes in the input pools lacked transposon insertions and were therefore concluded to be essential for P. mirabilis growth in rich medium. 629 genes were identified as P. mirabilis fitness factors during single-species CAUTI. Tn-Seq from coinfection with P. stuartii revealed 217/629 (35%) of the same genes as identified by single-species Tn-Seq, and 1353 additional factors that specifically contribute to colonization during coinfection. Mutants were constructed in eight genes of interest to validate the initial screen: 7/8 (88%) mutants exhibited the expected phenotypes for single-species CAUTI, and 3/3 (100%) validated the expected phenotypes for polymicrobial CAUTI. This approach provided validation of numerous previously described P. mirabilis fitness determinants from an ascending model of UTI, the discovery of novel fitness determinants specifically for CAUTI, and a stringent assessment of how polymicrobial infection influences fitness requirements. For instance, we describe a requirement for branched-chain amino acid biosynthesis by P. mirabilis during coinfection due to high-affinity import of leucine by P. stuartii. Further investigation of genes and pathways that provide a competitive advantage during both single-species and polymicrobial CAUTI will likely provide robust targets for therapeutic intervention to reduce P. mirabilis CAUTI incidence and severity. PMID:28614382
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A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity.
Forno, Erick; Wang, Ting; Yan, Qi; Brehm, John; Acosta-Perez, Edna; Colon-Semidey, Angel; Alvarez, Maria; Boutaoui, Nadia; Cloutier, Michelle M; Alcorn, John F; Canino, Glorisa; Chen, Wei; Celedón, Juan C
2017-10-01
Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics "vertical" approach that integrates multiple analytical steps using linear and logistic regression models. In a case-control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression-protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2-3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4-8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate-severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.
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Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang
2011-01-01
Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases. PMID:21909426
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Wang, Shuo; Gao, Li-Zhi
2016-09-01
The complete chloroplast genome of green foxtail (Setaria viridis), a promising model system for C4 photosynthesis, is first reported in this study. The genome harbors a large single copy (LSC) region of 81 016 bp and a small single copy (SSC) region of 12 456 bp separated by a pair of inverted repeat (IRa and IRb) regions of 22 315 bp. GC content is 38.92%. The proportion of coding sequence is 57.97%, comprising of 111 (19 duplicated in IR regions) unique genes, 71 of which are protein-coding genes, four are rRNA genes, and 36 are tRNA genes. Phylogenetic analysis indicated that S. viridis was clustered with its cultivated species S. italica in the tribe Paniceae of the family Poaceae. This newly determined chloroplast genome will provide valuable genetic resources to assist future studies on C4 photosynthesis in grasses.
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ACTG: novel peptide mapping onto gene models.
Choi, Seunghyuk; Kim, Hyunwoo; Paek, Eunok
2017-04-15
In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. Available at http://prix.hanyang.ac.kr/ACTG/search.jsp . eunokpaek@hanyang.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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Li, Guotian; Jain, Rashmi; Chern, Mawsheng; Pham, Nikki T; Martin, Joel A; Wei, Tong; Schackwitz, Wendy S; Lipzen, Anna M; Duong, Phat Q; Jones, Kyle C; Jiang, Liangrong; Ruan, Deling; Bauer, Diane; Peng, Yi; Barry, Kerrie W; Schmutz, Jeremy; Ronald, Pamela C
2017-06-01
The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake ( Oryza sativa ssp japonica ), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportion of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. This work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations. © 2017 American Society of Plant Biologists. All rights reserved.
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Li, Guotian; Jain, Rashmi; Chern, Mawsheng; ...
2017-06-02
The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake (Oryza sativa ssp japonica), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportionmore » of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. In conclusion, this work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations.« less
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Validation of reference genes for quantitative gene expression analysis in experimental epilepsy.
Sadangi, Chinmaya; Rosenow, Felix; Norwood, Braxton A
2017-12-01
To grasp the molecular mechanisms and pathophysiology underlying epilepsy development (epileptogenesis) and epilepsy itself, it is important to understand the gene expression changes that occur during these phases. Quantitative real-time polymerase chain reaction (qPCR) is a technique that rapidly and accurately determines gene expression changes. It is crucial, however, that stable reference genes are selected for each experimental condition to ensure that accurate values are obtained for genes of interest. If reference genes are unstably expressed, this can lead to inaccurate data and erroneous conclusions. To date, epilepsy studies have used mostly single, nonvalidated reference genes. This is the first study to systematically evaluate reference genes in male Sprague-Dawley rat models of epilepsy. We assessed 15 potential reference genes in hippocampal tissue obtained from 2 different models during epileptogenesis, 1 model during chronic epilepsy, and a model of noninjurious seizures. Reference gene ranking varied between models and also differed between epileptogenesis and chronic epilepsy time points. There was also some variance between the four mathematical models used to rank reference genes. Notably, we found novel reference genes to be more stably expressed than those most often used in experimental epilepsy studies. The consequence of these findings is that reference genes suitable for one epilepsy model may not be appropriate for others and that reference genes can change over time. It is, therefore, critically important to validate potential reference genes before using them as normalizing factors in expression analysis in order to ensure accurate, valid results. © 2017 Wiley Periodicals, Inc.
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Degl'Innocenti, Andrea; Parrilla, Marta; Harr, Bettina; Teschke, Meike
2016-01-01
In vertebrates, several anatomical regions located within the nasal cavity mediate olfaction. Among these, the main olfactory epithelium detects most conventional odorants. Olfactory sensory neurons, provided with cilia exposed to the air, detect volatile chemicals via an extremely large family of seven-transmembrane chemoreceptors named odorant receptors. Their genes are expressed in a monogenic and monoallelic fashion: a single allele of a single odorant receptor gene is transcribed in a given mature neuron, through a still uncharacterized molecular mechanism known as odorant receptor gene choice. Odorant receptor genes are typically arranged in genomic clusters, but a few are isolated (we call them solitary) from the others within a region broader than 1 Mb upstream and downstream with respect to their transcript's coordinates. The study of clustered genes is problematic, because of redundancy and ambiguities in their regulatory elements: we propose to use the solitary genes as simplified models to understand odorant receptor gene choice. Here we define number and identity of the solitary genes in the mouse genome (C57BL/6J), and assess the conservation of the solitary status in some mammalian orthologs. Furthermore, we locate their putative promoters, predict their homeodomain binding sites (commonly present in the promoters of odorant receptor genes) and compare candidate promoter sequences with those of wild-caught mice. We also provide expression data from histological sections. In the mouse genome there are eight intact solitary genes: Olfr19 (M12), Olfr49, Olfr266, Olfr267, Olfr370, Olfr371, Olfr466, Olfr1402; five are conserved as solitary in rat. These genes are all expressed in the main olfactory epithelium of three-day-old mice. The C57BL/6J candidate promoter of Olfr370 has considerably varied compared to its wild-type counterpart. Within the putative promoter for Olfr266 a homeodomain binding site is predicted. As a whole, our findings favor Olfr266 as a model gene to investigate odorant receptor gene choice.
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Cooperative Adaptive Responses in Gene Regulatory Networks with Many Degrees of Freedom
Inoue, Masayo; Kaneko, Kunihiko
2013-01-01
Cells generally adapt to environmental changes by first exhibiting an immediate response and then gradually returning to their original state to achieve homeostasis. Although simple network motifs consisting of a few genes have been shown to exhibit such adaptive dynamics, they do not reflect the complexity of real cells, where the expression of a large number of genes activates or represses other genes, permitting adaptive behaviors. Here, we investigated the responses of gene regulatory networks containing many genes that have undergone numerical evolution to achieve high fitness due to the adaptive response of only a single target gene; this single target gene responds to changes in external inputs and later returns to basal levels. Despite setting a single target, most genes showed adaptive responses after evolution. Such adaptive dynamics were not due to common motifs within a few genes; even without such motifs, almost all genes showed adaptation, albeit sometimes partial adaptation, in the sense that expression levels did not always return to original levels. The genes split into two groups: genes in the first group exhibited an initial increase in expression and then returned to basal levels, while genes in the second group exhibited the opposite changes in expression. From this model, genes in the first group received positive input from other genes within the first group, but negative input from genes in the second group, and vice versa. Thus, the adaptation dynamics of genes from both groups were consolidated. This cooperative adaptive behavior was commonly observed if the number of genes involved was larger than the order of ten. These results have implications in the collective responses of gene expression networks in microarray measurements of yeast Saccharomyces cerevisiae and the significance to the biological homeostasis of systems with many components. PMID:23592959
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Maye, Peter; Stover, Mary Louise; Liu, Yaling; Rowe, David W; Gong, Shiaochin; Lichtler, Alexander C
2009-03-13
Reporter gene mice are valuable animal models for biological research providing a gene expression readout that can contribute to cellular characterization within the context of a developmental process. With the advancement of bacterial recombination techniques to engineer reporter gene constructs from BAC genomic clones and the generation of optically distinguishable fluorescent protein reporter genes, there is an unprecedented capability to engineer more informative transgenic reporter mouse models relative to what has been traditionally available. We demonstrate here our first effort on the development of a three stage bacterial recombination strategy to physically link multiple genes together with their respective fluorescent protein (FP) reporters in one DNA fragment. This strategy uses bacterial recombination techniques to: (1) subclone genes of interest into BAC linking vectors, (2) insert desired reporter genes into respective genes and (3) link different gene-reporters together. As proof of concept, we have generated a single DNA fragment containing the genes Trap, Dmp1, and Ibsp driving the expression of ECFP, mCherry, and Topaz FP reporter genes, respectively. Using this DNA construct, we have successfully generated transgenic reporter mice that retain two to three gene readouts. The three stage methodology to link multiple genes with their respective fluorescent protein reporter works with reasonable efficiency. Moreover, gene linkage allows for their common chromosomal integration into a single locus. However, the testing of this multi-reporter DNA construct by transgenesis does suggest that the linkage of two different genes together, despite their large size, can still create a positional effect. We believe that gene choice, genomic DNA fragment size and the presence of endogenous insulator elements are critical variables.
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DEsingle for detecting three types of differential expression in single-cell RNA-seq data.
Miao, Zhun; Deng, Ke; Wang, Xiaowo; Zhang, Xuegong
2018-04-24
The excessive amount of zeros in single-cell RNA-seq data include "real" zeros due to the on-off nature of gene transcription in single cells and "dropout" zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy. The R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor's consideration now. zhangxg@tsinghua.edu.cn. Supplementary data are available at Bioinformatics online.
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Pendergrass, Sarah A; Verma, Shefali S; Holzinger, Emily R; Moore, Carrie B; Wallace, John; Dudek, Scott M; Huggins, Wayne; Kitchner, Terrie; Waudby, Carol; Berg, Richard; McCarty, Catherine A; Ritchie, Marylyn D
2013-01-01
Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, 2580 cataract cases and 1367 controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) Biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 529,431 single nucleotide polymorphisms (SNPs) with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using the Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use; these environmental factors have been previously associated with the formation of cataracts. We found a total of 288 models that exhibit an interaction term with a p-value ≤ 1×10(-4) associated with cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.
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CRISPR-mediated direct mutation of cancer genes in the mouse liver
Xue, Wen; Chen, Sidi; Yin, Hao; Tammela, Tuomas; Papagiannakopoulos, Thales; Joshi, Nikhil S.; Cai, Wenxin; Yang, Gillian; Bronson, Roderick; Crowley, Denise G.; Zhang, Feng; Anderson, Daniel G.; Sharp, Phillip A.; Jacks, Tyler
2014-01-01
The study of cancer genes in mouse models has traditionally relied on genetically-engineered strains made via transgenesis or gene targeting in embryonic stem (ES) cells1. Here we describe a new method of cancer model generation using the CRISPR/Cas system in vivo in wild-type mice. We have used hydrodynamic injection to deliver a CRISPR plasmid DNA expressing Cas9 and single guide RNAs (sgRNAs)2–4 to the liver and directly target the tumor suppressor genes Pten5 and p536, alone and in combination. CRISPR-mediated Pten mutation led to elevated Akt phosphorylation and lipid accumulation in hepatocytes, phenocopying the effects of deletion of the gene using Cre-LoxP technology7, 8. Simultaneous targeting of Pten and p53 induced liver tumors that mimicked those caused by Cre-loxP-mediated deletion of Pten and p53. DNA sequencing of liver and tumor tissue revealed insertion or deletion (indel) mutations of the tumor suppressor genes, including bi-allelic mutations of both Pten and p53 in tumors. Furthermore, co-injection of Cas9 plasmids harboring sgRNAs targeting the β-Catenin gene (Ctnnb1) and a single-stranded DNA (ssDNA) oligonucleotide donor carrying activating point mutations led to the generation of hepatocytes with nuclear localization of β-Catenin. This study demonstrates the feasibility of direct mutation of tumor suppressor genes and oncogenes in the liver using the CRISPR/Cas system, which presents a new avenue for rapid development of liver cancer models and functional genomics. PMID:25119044
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CRISPR-mediated direct mutation of cancer genes in the mouse liver.
Xue, Wen; Chen, Sidi; Yin, Hao; Tammela, Tuomas; Papagiannakopoulos, Thales; Joshi, Nikhil S; Cai, Wenxin; Yang, Gillian; Bronson, Roderick; Crowley, Denise G; Zhang, Feng; Anderson, Daniel G; Sharp, Phillip A; Jacks, Tyler
2014-10-16
The study of cancer genes in mouse models has traditionally relied on genetically-engineered strains made via transgenesis or gene targeting in embryonic stem cells. Here we describe a new method of cancer model generation using the CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins) system in vivo in wild-type mice. We used hydrodynamic injection to deliver a CRISPR plasmid DNA expressing Cas9 and single guide RNAs (sgRNAs) to the liver that directly target the tumour suppressor genes Pten (ref. 5) and p53 (also known as TP53 and Trp53) (ref. 6), alone and in combination. CRISPR-mediated Pten mutation led to elevated Akt phosphorylation and lipid accumulation in hepatocytes, phenocopying the effects of deletion of the gene using Cre-LoxP technology. Simultaneous targeting of Pten and p53 induced liver tumours that mimicked those caused by Cre-loxP-mediated deletion of Pten and p53. DNA sequencing of liver and tumour tissue revealed insertion or deletion mutations of the tumour suppressor genes, including bi-allelic mutations of both Pten and p53 in tumours. Furthermore, co-injection of Cas9 plasmids harbouring sgRNAs targeting the β-catenin gene and a single-stranded DNA oligonucleotide donor carrying activating point mutations led to the generation of hepatocytes with nuclear localization of β-catenin. This study demonstrates the feasibility of direct mutation of tumour suppressor genes and oncogenes in the liver using the CRISPR/Cas system, which presents a new avenue for rapid development of liver cancer models and functional genomics.
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Lin, Eugene; Pei, Dee; Huang, Yi-Jen; Hsieh, Chang-Hsun; Wu, Lawrence Shih-Hsin
2009-08-01
Recent studies indicate that obesity may play a key role in modulating genetic predispositions to type 2 diabetes (T2D). This study examines the main effects of both single-locus and multilocus interactions among genetic variants in Taiwanese obese and nonobese individuals to test the hypothesis that obesity-related genes may contribute to the etiology of T2D independently and/or through such complex interactions. We genotyped 11 single nucleotide polymorphisms for 10 obesity candidate genes including adrenergic beta-2-receptor surface, adrenergic beta-3-receptor surface, angiotensinogen, fat mass and obesity associated gene, guanine nucleotide binding protein beta polypeptide 3 (GNB3), interleukin 6 receptor, proprotein convertase subtilisin/kexin type 1 (PCSK1), uncoupling protein 1, uncoupling protein 2, and uncoupling protein 3. There were 389 patients diagnosed with T2D and 186 age- and sex-matched controls. Single-locus analyses showed significant main effects of the GNB3 and PCSK1 genes on the risk of T2D among the nonobese group (p = 0.002 and 0.047, respectively). Further, interactions involving GNB3 and PCSK1 were suggested among the nonobese population using the generalized multifactor dimensionality reduction method (p = 0.001). In addition, interactions among angiotensinogen, fat mass and obesity associated gene, GNB3, and uncoupling protein 3 genes were found in a significant four-locus generalized multifactor dimensionality reduction model among the obese population (p = 0.001). The results suggest that the single nucleotide polymorphisms from the obesity candidate genes may contribute to the risk of T2D independently and/or in an interactive manner according to the presence or absence of obesity.
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Translational animal models of autism and neurodevelopmental disorders
Crawley, Jacqueline N.
2012-01-01
Autism is a neurodevelopmental disorder whose diagnosis is based on three behavioral criteria: unusual reciprocal social interactions, deficits in communication, and stereotyped repetitive behaviors with restricted interests. A large number of de novo single gene mutations and chromosomal deletions are associated with autism spectrum disorders. Based on the strong genetic evidence, mice with targeted mutations in homologous genes have been generated as translational research tools. Mouse models of autism have revealed behavioral and biological outcomes of mutations in risk genes. The field is now poised to employ the most robust phenotypes in the most replicable mouse models for preclinical screening of novel therapeutics. PMID:23226954
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Howe, Douglas G.; Bradford, Yvonne M.; Eagle, Anne; Fashena, David; Frazer, Ken; Kalita, Patrick; Mani, Prita; Martin, Ryan; Moxon, Sierra Taylor; Paddock, Holly; Pich, Christian; Ramachandran, Sridhar; Ruzicka, Leyla; Schaper, Kevin; Shao, Xiang; Singer, Amy; Toro, Sabrina; Van Slyke, Ceri; Westerfield, Monte
2017-01-01
The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, ‘Fish’ records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search. PMID:27899582
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Origin and evolution of TNF and TNF receptor superfamilies
USDA-ARS?s Scientific Manuscript database
The tumor necrosis factor superfamily (TNFSF) and the TNF receptor superfamily (TNFRSF) have an ancient evolutionary origin that can be traced back to single copy genes within Arthropods. In humans, 18 TNFSF and 29 TNFRSF genes have been identified. Evolutionary models account for the increase in g...
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The role of ghrelin and ghrelin-receptor gene variants and promoter activity in type 2 diabetes.
Garcia, Edwin A; King, Peter; Sidhu, Kally; Ohgusu, Hideko; Walley, Andrew; Lecoeur, Cecile; Gueorguiev, Maria; Khalaf, Sahira; Davies, Derek; Grossman, Ashley B; Kojima, Masayasu; Petersenn, Stephan; Froguel, Phillipe; Korbonits, Márta
2009-08-01
Ghrelin and its receptor play an important role in glucose metabolism and energy homeostasis, and therefore they are functional candidates for genes carrying susceptibility alleles for type 2 diabetes. We assessed common genetic variation of the ghrelin (GHRL; five single nucleotide polymorphisms (SNP)) and the ghrelin-receptor (GHSR) genes (four SNPs) in 610 Caucasian patients with type 2 diabetes and 820 controls. In addition, promoter reporter assays were conducted to model the regulatory regions of both genes. Neither GHRL nor GHSR gene SNPs were associated with type 2 diabetes. One of the ghrelin haplotypes showed a marginal protective role in type 2 diabetes. We observed profound differences in the regulation of the GHRL gene according to promoter sequence variants. There are three different GHRL promoter haplotypes represented in the studied cohort causing up to 45% difference in the level of gene expression, while the promoter region of GHSR gene is primarily represented by a single haplotype. The GHRL and GHSR gene variants are not associated with type 2 diabetes, although GHRL promoter variants have significantly different activities.
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An Empirically Calibrated Model of Cell Fate Decision Following Viral Infection
NASA Astrophysics Data System (ADS)
Coleman, Seth; Igoshin, Oleg; Golding, Ido
The life cycle of the virus (phage) lambda is an established paradigm for the way genetic networks drive cell fate decisions. But despite decades of interrogation, we are still unable to theoretically predict whether the infection of a given cell will result in cell death or viral dormancy. The poor predictive power of current models reflects the absence of quantitative experimental data describing the regulatory interactions between different lambda genes. To address this gap, we are constructing a theoretical model that captures the known interactions in the lambda network. Model assumptions and parameters are calibrated using new single-cell data from our lab, describing the activity of lambda genes at single-molecule resolution. We began with a mean-field model, aimed at exploring the population averaged gene-expression trajectories under different initial conditions. Next, we will develop a stochastic formulation, to capture the differences between individual cells within the population. The eventual goal is to identify how the post-infection decision is driven by the interplay between network topology, initial conditions, and stochastic effects. The insights gained here will inform our understanding of cell fate choices in more complex cellular systems.
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Identification of SNPs associated with variola virus virulence.
Hoen, Anne Gatewood; Gardner, Shea N; Moore, Jason H
2013-02-14
Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs. Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The top performing unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV. We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity.
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Identification of SNPs associated with variola virus virulence
2013-01-01
Background Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs. Findings Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The top performing unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV. Conclusions We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity. PMID:23410064
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A descriptive marker gene approach to single-cell pseudotime inference.
Campbell, Kieran R; Yau, Christopher
2018-06-23
Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasise an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour. Here we introduce an orthogonal Bayesian approach termed "Ouija" that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. We demonstrate that this small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify "metastable" states - discrete cell types along the continuous trajectories - that recapitulate known cell types. An open source implementation is available as an R package at http://www.github.com/kieranrcampbell/ouija and as a Python/TensorFlow package at http://www.github.com/kieranrcampbell/ouijaflow. Supplementary text, figures, and tables are available at Bioinformatics online.
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Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold
2015-03-01
A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Rao, V.S.; Auerbach, S.A.; Farrer, L.A.
1996-09-01
Apolipoprotein E (APOE) genotype is the single most important determinant to the common form of Alzheimer disease (AD) yet identified. Several studies show that family history of AD is not entirely accounted for by APOE genotype. Also, there is evidence for an interaction between APOE genotype and gender. We carried out a complex segregation analysis in 636 nuclear families of consecutively ascertained and rigorously diagnosed probands in the Multi-Institutional Research in Alzheimer Genetic Epidemiology study in order to derive models of disease transmission which account for the influences of APOE genotype of the proband and gender. In the total groupmore » of families, models postulating sporadic occurrence, no major gene effect, random environmental transmission, and Mendelian inheritance were rejected. Transmission of AD in families of probands with at least one {epsilon}4 allele best fit a dominant model. Moreover, single gene inheritance best explained clustering of the disorder in families of probands lacking E4, but a more complex genetic model or multiple genetic models may ultimately account for risk in this group of families. Our results also suggest that susceptibility to AD differs between men and women regardless of the proband`s APOE status. Assuming a dominant model, AD appears to be completely penetrant in women, whereas only 62%-65% of men with predisposing genotypes develop AD. However, parameter estimates from the arbitrary major gene model suggests that AD is expressed dominantly in women and additively in men. These observations, taken together with epidemiologic data, are consistent with the hypothesis of an interaction between genes and other biological factors affecting disease susceptibility. 76 refs., 4 tabs.« less
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Buettner, Florian; Natarajan, Kedar N; Casale, F Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J; Teichmann, Sarah A; Marioni, John C; Stegle, Oliver
2015-02-01
Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.
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Song, Jia; Zheng, Sisi; Nguyen, Nhung; Wang, Youjun; Zhou, Yubin; Lin, Kui
2017-10-03
Because phylogenetic inference is an important basis for answering many evolutionary problems, a large number of algorithms have been developed. Some of these algorithms have been improved by integrating gene evolution models with the expectation of accommodating the hierarchy of evolutionary processes. To the best of our knowledge, however, there still is no single unifying model or algorithm that can take all evolutionary processes into account through a stepwise or simultaneous method. On the basis of three existing phylogenetic inference algorithms, we built an integrated pipeline for inferring the evolutionary history of a given gene family; this pipeline can model gene sequence evolution, gene duplication-loss, gene transfer and multispecies coalescent processes. As a case study, we applied this pipeline to the STIMATE (TMEM110) gene family, which has recently been reported to play an important role in store-operated Ca 2+ entry (SOCE) mediated by ORAI and STIM proteins. We inferred their phylogenetic trees in 69 sequenced chordate genomes. By integrating three tree reconstruction algorithms with diverse evolutionary models, a pipeline for inferring the evolutionary history of a gene family was developed, and its application was demonstrated.
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Identification of innate lymphoid cells in single-cell RNA-Seq data.
Suffiotti, Madeleine; Carmona, Santiago J; Jandus, Camilla; Gfeller, David
2017-07-01
Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.
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Ellis, BL; Hirsch, ML; Porter, SN; Samulski, RJ; Porteus, MH
2016-01-01
An emerging strategy for the treatment of monogenic diseases uses genetic engineering to precisely correct the mutation(s) at the genome level. Recent advancements in this technology have demonstrated therapeutic levels of gene correction using a zinc-finger nuclease (ZFN)-induced DNA double-strand break in conjunction with an exogenous DNA donor substrate. This strategy requires efficient nucleic acid delivery and among viral vectors, recombinant adeno-associated virus (rAAV) has demonstrated clinical success without pathology. However, a major limitation of rAAV is the small DNA packaging capacity and to date, the use of rAAV for ZFN gene delivery has yet to be reported. Theoretically, an ideal situation is to deliver both ZFNs and the repair substrate in a single vector to avoid inefficient gene targeting and unwanted mutagenesis, both complications of a rAAV co-transduction strategy. Therefore, a rAAV format was generated in which a single polypeptide encodes the ZFN monomers connected by a ribosome skipping 2A peptide and furin cleavage sequence. On the basis of this arrangement, a DNA repair substrate of 750 nucleotides was also included in this vector. Efficient polypeptide processing to discrete ZFNs is demonstrated, as well as the ability of this single vector format to stimulate efficient gene targeting in a human cell line and mouse model derived fibroblasts. Additionally, we increased rAAV-mediated gene correction up to sixfold using a combination of Food and Drug Administration-approved drugs, which act at the level of AAV vector transduction. Collectively, these experiments demonstrate the ability to deliver ZFNs and a repair substrate by a single AAV vector and offer insights for the optimization of rAAV-mediated gene correction using drug therapy. PMID:22257934
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Honda, Arata; Hirose, Michiko; Sankai, Tadashi; Yasmin, Lubna; Yuzawa, Kazuaki; Honsho, Kimiko; Izu, Haruna; Iguchi, Atsushi; Ikawa, Masahito; Ogura, Atsuo
2015-01-01
Targeted genome editing of nonrodent mammalian species has provided the potential for highly accurate interventions into gene function in humans and the generation of useful animal models of human diseases. Here we show successful clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated (Cas)-mediated gene targeting via circular plasmid injection in rabbits. The rabbit tyrosinase gene (TYR) was effectively disrupted, and we confirmed germline transmission by pronuclear injection of a circular plasmid expressing humanized Cas9 (hCas9) and single-guide RNA. Direct injection into pronuclear stage zygotes was possible following an in vitro validation assay. Neither off-target mutagenesis nor hCas9 transgenesis was detected in any of the genetically targeted pups and embryos examined. Gene targeting with this rapid and simplified strategy will help accelerate the development of translational research using other nonrodent mammalian species.
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Honda, Arata; Hirose, Michiko; Sankai, Tadashi; Yasmin, Lubna; Yuzawa, Kazuaki; Honsho, Kimiko; Izu, Haruna; Iguchi, Atsushi; Ikawa, Masahito; Ogura, Atsuo
2014-01-01
Targeted genome editing of nonrodent mammalian species has provided the potential for highly accurate interventions into gene function in humans and the generation of useful animal models of human diseases. Here we show successful clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated (Cas)-mediated gene targeting via circular plasmid injection in rabbits. The rabbit tyrosinase gene (TYR) was effectively disrupted, and we confirmed germline transmission by pronuclear injection of a circular plasmid expressing humanized Cas9 (hCas9) and single-guide RNA. Direct injection into pronuclear stage zygotes was possible following an in vitro validation assay. Neither off-target mutagenesis nor hCas9 transgenesis was detected in any of the genetically targeted pups and embryos examined. Gene targeting with this rapid and simplified strategy will help accelerate the development of translational research using other nonrodent mammalian species. PMID:25195632
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Sorting by Cuts, Joins, and Whole Chromosome Duplications.
Zeira, Ron; Shamir, Ron
2017-02-01
Genome rearrangement problems have been extensively studied due to their importance in biology. Most studied models assumed a single copy per gene. However, in reality, duplicated genes are common, most notably in cancer. In this study, we make a step toward handling duplicated genes by considering a model that allows the atomic operations of cut, join, and whole chromosome duplication. Given two linear genomes, [Formula: see text] with one copy per gene and [Formula: see text] with two copies per gene, we give a linear time algorithm for computing a shortest sequence of operations transforming [Formula: see text] into [Formula: see text] such that all intermediate genomes are linear. We also show that computing an optimal sequence with fewest duplications is NP-hard.
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MU OPIOID RECEPTORS IN PAIN MANAGEMENT
Pasternak, Gavril; Pan, Ying-Xian
2014-01-01
Most of the potent analgesics currently in use act through the mu opioid receptor. Although they are classified as mu opioids, clinical experience suggests differences among them. The relative potencies of the agents can vary from patient to patient, as well as the side-effect profiles. These observations, coupled with pharmacological approaches in preclinical models, led to the suggestion of multiple subtypes of mu receptors. The explosion in molecular biology has led to the identification of a single gene encoding mu opioid receptors. It now appears that this gene undergoes extensive splicing, in which a single gene can generate multiple proteins. Evidence now suggests that these splice variants may help explain the clinical variability in responses among patients. PMID:21453899
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Duan, Dongmei; Yang, Xiuyan; Ya, Tu; Chen, Liping
2014-01-01
Preliminary basic research and clinical findings have demonstrated that electroacupuncture therapy exhibits positive effects in ameliorating depression. However, most studies of the underlying mechanism are at the single gene level; there are few reports regarding the mechanism at the whole-genome level. Using a rat genomic gene-chip, we profiled hippocampal gene expression changes in rats after electroacupuncture therapy. Electroacupuncture therapy alleviated depression-related manifestations in the model rats. Using gene-chip analysis, we demonstrated that electroacupuncture at Baihui (DU20) and Yintang (EX-HN3) regulates the expression of 21 genes. Real-time PCR showed that the genes Vgf, Igf2, Tmp32, Loc500373, Hif1a, Folr1, Nmb, and Rtn were upregulated or downregulated in depression and that their expression tended to normalize after electroacupuncture therapy. These results indicate that electroacupuncture at Baihui and Yintang modulates depression by regulating the expression of particular genes. PMID:25206746
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Single-cell transcriptomics for microbial eukaryotes.
Kolisko, Martin; Boscaro, Vittorio; Burki, Fabien; Lynn, Denis H; Keeling, Patrick J
2014-11-17
One of the greatest hindrances to a comprehensive understanding of microbial genomics, cell biology, ecology, and evolution is that most microbial life is not in culture. Solutions to this problem have mainly focused on whole-community surveys like metagenomics, but these analyses inevitably loose information and present particular challenges for eukaryotes, which are relatively rare and possess large, gene-sparse genomes. Single-cell analyses present an alternative solution that allows for specific species to be targeted, while retaining information on cellular identity, morphology, and partitioning of activities within microbial communities. Single-cell transcriptomics, pioneered in medical research, offers particular potential advantages for uncultivated eukaryotes, but the efficiency and biases have not been tested. Here we describe a simple and reproducible method for single-cell transcriptomics using manually isolated cells from five model ciliate species; we examine impacts of amplification bias and contamination, and compare the efficacy of gene discovery to traditional culture-based transcriptomics. Gene discovery using single-cell transcriptomes was found to be comparable to mass-culture methods, suggesting single-cell transcriptomics is an efficient entry point into genomic data from the vast majority of eukaryotic biodiversity. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Rivera-Torres, Natalia; Strouse, Bryan; Bialk, Pawel; Niamat, Rohina A; Kmiec, Eric B
2014-01-01
With recent technological advances that enable DNA cleavage at specific sites in the human genome, it may now be possible to reverse inborn errors, thereby correcting a mutation, at levels that could have an impact in a clinical setting. We have been developing gene editing, using single-stranded DNA oligonucleotides (ssODNs), as a tool to direct site specific single base changes. Successful application of this technique has been demonstrated in many systems ranging from bacteria to human (ES and somatic) cells. While the frequency of gene editing can vary widely, it is often at a level that does not enable clinical application. As such, a number of stimulatory factors such as double-stranded breaks are known to elevate the frequency significantly. The majority of these results have been discovered using a validated HCT116 mammalian cell model system where credible genetic and biochemical readouts are available. Here, we couple TAL-Effector Nucleases (TALENs) that execute specific ds DNA breaks with ssODNs, designed specifically to repair a missense mutation, in an integrated single copy eGFP gene. We find that proximal cleavage, relative to the mutant base, is key for enabling high frequencies of editing. A directionality of correction is also observed with TALEN activity upstream from the target base being more effective in promoting gene editing than activity downstream. We also find that cells progressing through S phase are more amenable to combinatorial gene editing activity. Thus, we identify novel aspects of gene editing that will help in the design of more effective protocols for genome modification and gene therapy in natural genes.
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Routine Discovery of Complex Genetic Models using Genetic Algorithms
Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.
2010-01-01
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983
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Ishikawa, Akira
2017-11-27
Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.
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Identification of gene regulation models from single-cell data
NASA Astrophysics Data System (ADS)
Weber, Lisa; Raymond, William; Munsky, Brian
2018-09-01
In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MATLAB and PYTHON software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584–7) and human cells (Senecal et al 2014 Cell Rep. 8 75–83)5.
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Producing a functional eukaryotic messenger RNA (mRNA) requires the coordinated activity of several large protein complexes to initiate transcription, elongate nascent transcripts, splice together exons, and cleave and polyadenylate the 3’ end. Kinetic competition between these various processes has been proposed to regulate mRNA maturation, but this model could lead to multiple, randomly determined, or stochastic, pathways or outcomes. Regulatory checkpoints have been suggested as a means of ensuring quality control. However, current methods have been unable to tease apart the contributions of these processes at a single gene or on a time scale that could provide mechanistic insight. To begin to investigate the kinetic relationship between transcription and splicing, Daniel Larson, Ph.D., of CCR’s Laboratory of Receptor Biology and Gene Expression, and his colleagues employed a single-molecule RNA imaging approach to monitor production and processing of a human β-globin reporter gene in living cells.
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NASA Astrophysics Data System (ADS)
Brazelton, W. J.; Mehta, M. P.; Baross, J. A.
2010-04-01
DNA sequencing and metabolic activity measurements show that lateral gene transfer promotes phenotypic diversity in single-species archaeal biofilms attached to hydrothermal chimneys. This system may be a useful model for early cellular evolution.
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The function of dog models in developing gene therapy strategies for human health.
Nowend, Keri L; Starr-Moss, Alison N; Murphy, Keith E
2011-08-01
The domestic dog is of great benefit to humankind, not only through companionship and working activities cultivated through domestication and selective breeding, but also as a model for biomedical research. Many single-gene traits have been well-characterized at the genomic level, and recent advances in whole-genome association studies will allow for better understanding of complex, multigenic hereditary diseases. Additionally, the dog serves as an invaluable large animal model for assessment of novel therapeutic agents. Thus, the dog has filled a crucial step in the translation of basic research to new treatment regimens for various human diseases. Four well-characterized diseases in canine models are discussed as they relate to other animal model availability, novel therapeutic approach, and extrapolation to human gene therapy trials.
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Spatially coordinated dynamic gene transcription in living pituitary tissue
Featherstone, Karen; Hey, Kirsty; Momiji, Hiroshi; McNamara, Anne V; Patist, Amanda L; Woodburn, Joanna; Spiller, David G; Christian, Helen C; McNeilly, Alan S; Mullins, John J; Finkenstädt, Bärbel F; Rand, David A; White, Michael RH; Davis, Julian RE
2016-01-01
Transcription at individual genes in single cells is often pulsatile and stochastic. A key question emerges regarding how this behaviour contributes to tissue phenotype, but it has been a challenge to quantitatively analyse this in living cells over time, as opposed to studying snap-shots of gene expression state. We have used imaging of reporter gene expression to track transcription in living pituitary tissue. We integrated live-cell imaging data with statistical modelling for quantitative real-time estimation of the timing of switching between transcriptional states across a whole tissue. Multiple levels of transcription rate were identified, indicating that gene expression is not a simple binary ‘on-off’ process. Immature tissue displayed shorter durations of high-expressing states than the adult. In adult pituitary tissue, direct cell contacts involving gap junctions allowed local spatial coordination of prolactin gene expression. Our findings identify how heterogeneous transcriptional dynamics of single cells may contribute to overall tissue behaviour. DOI: http://dx.doi.org/10.7554/eLife.08494.001 PMID:26828110
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Predicting effects of structural stress in a genome-reduced model bacterial metabolism
NASA Astrophysics Data System (ADS)
Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles
2012-08-01
Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.
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Dynamics of Bacterial Gene Regulatory Networks.
Shis, David L; Bennett, Matthew R; Igoshin, Oleg A
2018-05-20
The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.
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Aguiar, Bruno; Vieira, Jorge; Cunha, Ana E; Fonseca, Nuno A; Reboiro-Jato, David; Reboiro-Jato, Miguel; Fdez-Riverola, Florentino; Raspé, Olivier; Vieira, Cristina P
2013-05-01
S-RNase-based gametophytic self-incompatibility evolved once before the split of the Asteridae and Rosidae. In Prunus (tribe Amygdaloideae of Rosaceae), the self-incompatibility S-pollen is a single F-box gene that presents the expected evolutionary signatures. In Malus and Pyrus (subtribe Pyrinae of Rosaceae), however, clusters of F-box genes (called SFBBs) have been described that are expressed in pollen only and are linked to the S-RNase gene. Although polymorphic, SFBB genes present levels of diversity lower than those of the S-RNase gene. They have been suggested as putative S-pollen genes, in a system of non-self recognition by multiple factors. Subsets of allelic products of the different SFBB genes interact with non-self S-RNases, marking them for degradation, and allowing compatible pollinations. This study performed a detailed characterization of SFBB genes in Sorbus aucuparia (Pyrinae) to address three predictions of the non-self recognition by multiple factors model. As predicted, the number of SFBB genes was large to account for the many S-RNase specificities. Secondly, like the S-RNase gene, the SFBB genes were old. Thirdly, amino acids under positive selection-those that could be involved in specificity determination-were identified when intra-haplotype SFBB genes were analysed using codon models. Overall, the findings reported here support the non-self recognition by multiple factors model.
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Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
Manning, Cerys; Rattray, Magnus
2017-01-01
Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package. PMID:28493880
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Salvianti, Francesca; Rotunno, Giada; Galardi, Francesca; De Luca, Francesca; Pestrin, Marta; Vannucchi, Alessandro Maria; Di Leo, Angelo; Pazzagli, Mario; Pinzani, Pamela
2015-09-01
The purpose of the study was to explore the feasibility of a protocol for the isolation and molecular characterization of single circulating tumor cells (CTCs) from cancer patients using a single-cell next generation sequencing (NGS) approach. To reach this goal we used as a model an artificial sample obtained by spiking a breast cancer cell line (MDA-MB-231) into the blood of a healthy donor. Tumor cells were enriched and enumerated by CellSearch(®) and subsequently isolated by DEPArray™ to obtain single or pooled pure samples to be submitted to the analysis of the mutational status of multiple genes involved in cancer. Upon whole genome amplification, samples were analysed by NGS on the Ion Torrent PGM™ system (Life Technologies) using the Ion AmpliSeq™ Cancer Hotspot Panel v2 (Life Technologies), designed to investigate genomic "hot spot" regions of 50 oncogenes and tumor suppressor genes. We successfully sequenced five single cells, a pool of 5 cells and DNA from a cellular pellet of the same cell line with a mean depth of the sequencing reaction ranging from 1581 to 3479 reads. We found 27 sequence variants in 18 genes, 15 of which already reported in the COSMIC or dbSNP databases. We confirmed the presence of two somatic mutations, in the BRAF and TP53 gene, which had been already reported for this cells line, but also found new mutations and single nucleotide polymorphisms. Three variants were common to all the analysed samples, while 18 were present only in a single cell suggesting a high heterogeneity within the same cell line. This paper presents an optimized workflow for the molecular characterization of multiple genes in single cells by NGS. The described pipeline can be easily transferred to the study of single CTCs from oncologic patients.
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Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.
Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi
2016-01-01
Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.
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NASA Astrophysics Data System (ADS)
Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.
2018-03-01
The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.
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High-coverage methylation data of a gene model before and after DNA damage and homologous repair.
Pezone, Antonio; Russo, Giusi; Tramontano, Alfonso; Florio, Ermanno; Scala, Giovanni; Landi, Rosaria; Zuchegna, Candida; Romano, Antonella; Chiariotti, Lorenzo; Muller, Mark T; Gottesman, Max E; Porcellini, Antonio; Avvedimento, Enrico V
2017-04-11
Genome-wide methylation analysis is limited by its low coverage and the inability to detect single variants below 10%. Quantitative analysis provides accurate information on the extent of methylation of single CpG dinucleotide, but it does not measure the actual polymorphism of the methylation profiles of single molecules. To understand the polymorphism of DNA methylation and to decode the methylation signatures before and after DNA damage and repair, we have deep sequenced in bisulfite-treated DNA a reporter gene undergoing site-specific DNA damage and homologous repair. In this paper, we provide information on the data generation, the rationale for the experiments and the type of assays used, such as cytofluorimetry and immunoblot data derived during a previous work published in Scientific Reports, describing the methylation and expression changes of a model gene (GFP) before and after formation of a double-strand break and repair by homologous-recombination or non-homologous-end-joining. These data provide: 1) a reference for the analysis of methylation polymorphism at selected loci in complex cell populations; 2) a platform and the tools to compare transcription and methylation profiles.
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High-coverage methylation data of a gene model before and after DNA damage and homologous repair
Pezone, Antonio; Russo, Giusi; Tramontano, Alfonso; Florio, Ermanno; Scala, Giovanni; Landi, Rosaria; Zuchegna, Candida; Romano, Antonella; Chiariotti, Lorenzo; Muller, Mark T.; Gottesman, Max E.; Porcellini, Antonio; Avvedimento, Enrico V.
2017-01-01
Genome-wide methylation analysis is limited by its low coverage and the inability to detect single variants below 10%. Quantitative analysis provides accurate information on the extent of methylation of single CpG dinucleotide, but it does not measure the actual polymorphism of the methylation profiles of single molecules. To understand the polymorphism of DNA methylation and to decode the methylation signatures before and after DNA damage and repair, we have deep sequenced in bisulfite-treated DNA a reporter gene undergoing site-specific DNA damage and homologous repair. In this paper, we provide information on the data generation, the rationale for the experiments and the type of assays used, such as cytofluorimetry and immunoblot data derived during a previous work published in Scientific Reports, describing the methylation and expression changes of a model gene (GFP) before and after formation of a double-strand break and repair by homologous-recombination or non-homologous-end-joining. These data provide: 1) a reference for the analysis of methylation polymorphism at selected loci in complex cell populations; 2) a platform and the tools to compare transcription and methylation profiles. PMID:28398335
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Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C
2009-01-01
Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.
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Genetic risk profiling and gene signature modeling to predict risk of complications after IPAA.
Sehgal, Rishabh; Berg, Arthur; Polinski, Joseph I; Hegarty, John P; Lin, Zhenwu; McKenna, Kevin J; Stewart, David B; Poritz, Lisa S; Koltun, Walter A
2012-03-01
Severe pouchitis and Crohn's disease-like complications are 2 adverse postoperative complications that confound the success of the IPAA in patients with ulcerative colitis. To date, approximately 83 single nucleotide polymorphisms within 55 genes have been associated with IBD. The aim of this study was to identify single-nucleotide polymorphisms that correlate with complications after IPAA that could be utilized in a gene signature fashion to predict postoperative complications and aid in preoperative surgical decision making. One hundred forty-two IPAA patients were retrospectively classified as "asymptomatic" (n = 104, defined as no Crohn's disease-like complications or severe pouchitis for at least 2 years after IPAA) and compared with a "severe pouchitis" group (n = 12, ≥ 4 episodes pouchitis per year for 2 years including the need for long-term therapy to maintain remission) and a "Crohn's disease-like" group (n = 26, presence of fistulae, pouch inlet stricture, proximal small-bowel disease, or pouch granulomata, occurring at least 6 months after surgery). Genotyping for 83 single-nucleotide polymorphisms previously associated with Crohn's disease and/or ulcerative colitis was performed on a customized Illumina genotyping platform. The top 2 single-nucleotide polymorphisms statistically identified as being independently associated with each of Crohn's disease-like and severe pouchitis were used in a multivariate logistic regression model. These single-nucleotide polymorphisms were then used to create probability equations to predict overall chance of a positive or negative outcome for that complication. The top 2 single-nucleotide polymorphisms for Crohn's disease-like complications were in the 10q21 locus and the gene for PTGER4 (p = 0.006 and 0.007), whereas for severe pouchitis it was NOD2 and TNFSF15 (p = 0.003 and 0.011). Probability equations suggested that the risk of these 2 complications greatly increased with increasing number of risk alleles, going as high as 92% for severe pouchitis and 65% for Crohn's disease-like complications. In this IPAA patient cohort, mutations in the 10q21 locus and the PTGER4 gene were associated with Crohn's disease-like complications, whereas mutations in NOD2 and TNFSF15 correlated with severe pouchitis. Preoperative genetic analysis and use of such gene signatures hold promise for improved preoperative surgical patient selection to minimize these IPAA complications.
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[Construction of EZH2 Knockout Animal Model by CRISPR/Cas9 Technology].
Meng, Fanrong; Zhao, Dan; Zhou, Qinghua; Liu, Zhe
2018-05-20
It has been proven that CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated 9) system was the modern gene-editing technology through the constitutive expression of nucleases Cas9 in the mammalian, which binds to the specific site in the genome mediated by single-guide RNA (sgRNA) at desired genomic loci. The aim of this study is that the animal model of EZH2 gene knockout was constructed using CRISPR/Cas9 technology. In this study, we designed two single-guide RNAs targeting the Exon3 and Exon4 of EZH2 gene. Then, their gene-targeting efficiency were detected by SURVEYOR assay. The lentivirus was perfused into the lungs of mice by using a bronchial tube and detected by immunohistochemistry and qRT-PCR. The experimental results of NIH-3T3 cells verify that the designed sgEZH2 can efficiently effect the cleavage of target DNA by Cas9 in vitro. The immunohistochemistry and qRT-PCR results showed that the EZH2 expression in experimental group was significantly decreased in the mouse lung tissue. The study successfully designed two sgRNA which can play a knock-out EZH2 function. An EZH2 knockout animal model was successfully constructed by CRISPR/Cas9 system, and it will be an effective animal model for studying the functions and mechanisms of EZH2.
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GeneNetFinder2: Improved Inference of Dynamic Gene Regulatory Relations with Multiple Regulators.
Han, Kyungsook; Lee, Jeonghoon
2016-01-01
A gene involved in complex regulatory interactions may have multiple regulators since gene expression in such interactions is often controlled by more than one gene. Another thing that makes gene regulatory interactions complicated is that regulatory interactions are not static, but change over time during the cell cycle. Most research so far has focused on identifying gene regulatory relations between individual genes in a particular stage of the cell cycle. In this study we developed a method for identifying dynamic gene regulations of several types from the time-series gene expression data. The method can find gene regulations with multiple regulators that work in combination or individually as well as those with single regulators. The method has been implemented as the second version of GeneNetFinder (hereafter called GeneNetFinder2) and tested on several gene expression datasets. Experimental results with gene expression data revealed the existence of genes that are not regulated by individual genes but rather by a combination of several genes. Such gene regulatory relations cannot be found by conventional methods. Our method finds such regulatory relations as well as those with multiple, independent regulators or single regulators, and represents gene regulatory relations as a dynamic network in which different gene regulatory relations are shown in different stages of the cell cycle. GeneNetFinder2 is available at http://bclab.inha.ac.kr/GeneNetFinder and will be useful for modeling dynamic gene regulations with multiple regulators.
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GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.
Schulz, Tizian; Stoye, Jens; Doerr, Daniel
2018-05-08
Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.
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Wang, Tianyu; Nabavi, Sheida
2018-04-24
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity. Copyright © 2018 Elsevier Inc. All rights reserved.
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Prediction and Testing of Biological Networks Underlying Intestinal Cancer
Mariadason, John M.; Wang, Donghai; Augenlicht, Leonard H.; Chance, Mark R.
2010-01-01
Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data. PMID:20824133
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Degl'Innocenti, Andrea
2016-01-01
Background In vertebrates, several anatomical regions located within the nasal cavity mediate olfaction. Among these, the main olfactory epithelium detects most conventional odorants. Olfactory sensory neurons, provided with cilia exposed to the air, detect volatile chemicals via an extremely large family of seven-transmembrane chemoreceptors named odorant receptors. Their genes are expressed in a monogenic and monoallelic fashion: a single allele of a single odorant receptor gene is transcribed in a given mature neuron, through a still uncharacterized molecular mechanism known as odorant receptor gene choice. Aim Odorant receptor genes are typically arranged in genomic clusters, but a few are isolated (we call them solitary) from the others within a region broader than 1 Mb upstream and downstream with respect to their transcript's coordinates. The study of clustered genes is problematic, because of redundancy and ambiguities in their regulatory elements: we propose to use the solitary genes as simplified models to understand odorant receptor gene choice. Procedures Here we define number and identity of the solitary genes in the mouse genome (C57BL/6J), and assess the conservation of the solitary status in some mammalian orthologs. Furthermore, we locate their putative promoters, predict their homeodomain binding sites (commonly present in the promoters of odorant receptor genes) and compare candidate promoter sequences with those of wild-caught mice. We also provide expression data from histological sections. Results In the mouse genome there are eight intact solitary genes: Olfr19 (M12), Olfr49, Olfr266, Olfr267, Olfr370, Olfr371, Olfr466, Olfr1402; five are conserved as solitary in rat. These genes are all expressed in the main olfactory epithelium of three-day-old mice. The C57BL/6J candidate promoter of Olfr370 has considerably varied compared to its wild-type counterpart. Within the putative promoter for Olfr266 a homeodomain binding site is predicted. As a whole, our findings favor Olfr266 as a model gene to investigate odorant receptor gene choice. PMID:26794459
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting
2013-01-01
Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less
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Model-based design of RNA hybridization networks implemented in living cells
Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter
2017-01-01
Abstract Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. PMID:28934501
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NF-κB dynamics show digital activation and analog information processing in cells
NASA Astrophysics Data System (ADS)
Tay, Savas; Hughey, Jake; Lee, Timothy; Lipniacki, Tomasz; Covert, Markus; Quake, Stephen
2010-03-01
Cells operate in ever changing environments using extraordinary communication capabilities. Cell-to-cell communication is mediated by signaling molecules that form spatiotemporal concentration gradients, which requires cells to respond to a wide range of signal intensities. We used high-throughput microfluidic cell culture, quantitative gene expression analysis and mathematical modeling to investigate how single mammalian cells respond to different concentrations of the signaling molecule TNF-α via the transcription factor NF-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. In contrast to population studies, the activation is a stochastic, switch-like process at the single cell level with fewer cells responding at lower doses. The activated cells respond fully and express early genes independent of the TNF-α concentration, while only high dose stimulation results in the expression of late genes. Cells also encode a set of analog parameters such as the NF-κB peak intensity, response time and number of oscillations to modulate the outcome. We developed a stochastic model that reproduces both the digital and analog dynamics as well as the gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α induced NF-κB signaling in various types of cells.
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Miller, A
1977-01-01
The data available from other laboratories as well as our own on the frequency of cells recognizing major histocompatibility antigens or conventional protein and hapten antigens is critically evaluated. The frequency of specific binding for a large number of antigens is sufficiently high to support the idea that at least part of the antigen-binding cell population must have multiple specificities. Our results suggest that these multiple specific cells result from single cells synthesizing and displaying as many as 50-100 species of receptor, each at a frequency of 10(4) per cell. A model involving gene expansion of constant-region genes is suggested and some auxilliary evidence consistent with such C-gene expansion is presented.
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Imaging Transcriptional Regulation of Eukaryotic mRNA Genes: Advances and Outlook.
Yao, Jie
2017-01-06
Regulation of eukaryotic transcription in vivo occurs at distinct stages. Previous research has identified many active or repressive transcription factors (TFs) and core transcription components and studied their functions in vitro and in vivo. Nonetheless, how individual TFs act in concert to regulate mRNA gene expression in a single cell remains poorly understood. Direct observation of TF assembly and disassembly and various biochemical reactions during transcription of a single-copy gene in vivo is the ideal approach to study this problem. Research in this area requires developing novel techniques for single-cell transcription imaging and integrating imaging studies into understanding the molecular biology of transcription. In the past decade, advanced cell imaging has enabled unprecedented capabilities to visualize individual TF molecules, to track single transcription sites, and to detect individual mRNA in fixed and living cells. These studies have raised several novel insights on transcriptional regulation such as the "hit-and-run" model and transcription bursting that could not be obtained by in vitro biochemistry analysis. At this point, the key question is how to achieve deeper understandings or discover novel mechanisms of eukaryotic transcriptional regulation by imaging transcription in single cells. Meanwhile, further technical advancements are likely required for visualizing distinct kinetic steps of transcription on a single-copy gene in vivo. This review article summarizes recent progress in the field and describes the challenges and opportunities ahead. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Chemical Memory Reactions Induced Bursting Dynamics in Gene Expression
Tian, Tianhai
2013-01-01
Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems. PMID:23349679
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Chemical memory reactions induced bursting dynamics in gene expression.
Tian, Tianhai
2013-01-01
Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems.
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An adaptive radiation model for the origin of new genefunctions
DOE Office of Scientific and Technical Information (OSTI.GOV)
Francino, M. Pilar
2004-10-18
The evolution of new gene functions is one of the keys to evolutionary innovation. Most novel functions result from gene duplication followed by divergence. However, the models hitherto proposed to account for this process are not fully satisfactory. The classic model of neofunctionalization holds that the two paralogous gene copies resulting from a duplication are functionally redundant, such that one of them can evolve under no functional constraints and occasionally acquire a new function. This model lacks a convincing mechanism for the new gene copies to increase in frequency in the population and survive the mutational load expected to accumulatemore » under neutrality, before the acquisition of the rare beneficial mutations that would confer new functionality. The subfunctionalization model has been proposed as an alternative way to generate genes with altered functions. This model also assumes that new paralogous gene copies are functionally redundant and therefore neutral, but it predicts that relaxed selection will affect both gene copies such that some of the capabilities of the parent gene will disappear in one of the copies and be retained in the other. Thus, the functions originally present in a single gene will be partitioned between the two descendant copies. However, although this model can explain increases in gene number, it does not really address the main evolutionary question, which is the development of new biochemical capabilities. Recently, a new concept has been introduced into the gene evolution literature which is most likely to help solve this dilemma. The key point is to allow for a period of natural selection for the duplication per se, before new function evolves, rather than considering gene duplication to be neutral as in the previous models. Here, I suggest a new model that draws on the advantage of postulating selection for gene duplication, and proposes that bursts of adaptive gene amplification in response to specific selection pressures provide the raw material for the evolution of new function.« less
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Isoform-level gene expression patterns in single-cell RNA-sequencing data.
Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Pawitan, Yudi; Rantalainen, Mattias
2018-02-27
RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16,562 isoform-pairs from 4,929 genes. Among those, 26% of the discovered patterns were significant (p<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. The effect of drop-out events, mean expression level, and properties of the expression distribution on the performances of ISOP were also investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoformlevel preference, commitment and heterogeneity in single-cell RNA-sequencing data. The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. mattias.rantalainen@ki.se. Supplementary data are available at Bioinformatics online.
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Kujoth, Gregory C.; Sullivan, Thomas D.; Merkhofer, Richard; Lee, Taek-Jin; Wang, Huafeng; Brandhorst, Tristan; Wüthrich, Marcel
2018-01-01
ABSTRACT Blastomyces dermatitidis is a human fungal pathogen of the lung that can lead to disseminated disease in healthy and immunocompromised individuals. Genetic analysis of this fungus is hampered by the relative inefficiency of traditional recombination-based gene-targeting approaches. Here, we demonstrate the feasibility of applying CRISPR/Cas9-mediated gene editing to Blastomyces, including to simultaneously target multiple genes. We created targeting plasmid vectors expressing Cas9 and either one or two single guide RNAs and introduced these plasmids into Blastomyces via Agrobacterium gene transfer. We succeeded in disrupting several fungal genes, including PRA1 and ZRT1, which are involved in scavenging and uptake of zinc from the extracellular environment. Single-gene-targeting efficiencies varied by locus (median, 60% across four loci) but were approximately 100-fold greater than traditional methods of Blastomyces gene disruption. Simultaneous dual-gene targeting proceeded with efficiencies similar to those of single-gene-targeting frequencies for the respective targets. CRISPR/Cas9 disruption of PRA1 or ZRT1 had a variable impact on growth under zinc-limiting conditions, showing reduced growth at early time points in low-passage-number cultures and growth similar to wild-type levels by later passage. Individual impairment of PRA1 or ZRT1 resulted in a reduction of the fungal burden in a mouse model of Blastomyces infection by a factor of ~1 log (range, up to 3 logs), and combined disruption of both genes had no additional impact on the fungal burden. These results underscore the utility of CRISPR/Cas9 for efficient gene disruption in dimorphic fungi and reveal a role for zinc metabolism in Blastomyces fitness in vivo. PMID:29615501
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2014-01-01
Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved. PMID:24444313
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Does Marriage Moderate Genetic Effects on Delinquency and Violence?
Li, Yi; Liu, Hexuan; Guo, Guang
2015-01-01
Using data from the National Longitudinal Study of Adolescent to Adult Health (N = 1,254), the authors investigated whether marriage can foster desistance from delinquency and violence by moderating genetic effects. In contrast to existing gene–environment research that typically focuses on one or a few genetic polymorphisms, they extended a recently developed mixed linear model to consider the collective influence of 580 single nucleotide polymorphisms in 64 genes related to aggression and risky behavior. The mixed linear model estimates the proportion of variance in the phenotype that is explained by the single nucleotide polymorphisms. The authors found that the proportion of variance in delinquency/violence explained was smaller among married individuals than unmarried individuals. Because selection, confounding, and heterogeneity may bias the estimate of the Gene × Marriage interaction, they conducted a series of analyses to address these issues. The findings suggest that the Gene × Marriage interaction results were not seriously affected by these issues. PMID:26549892
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Conserved Genes Act as Modifiers of Invertebrate SMN Loss of Function Defects
Chang, Howard C.; Sen, Anindya; Kalloo, Geetika; Harris, Jevede; Barsby, Tom; Walsh, Melissa B.; Satterlee, John S.; Li, Chris; Van Vactor, David; Artavanis-Tsakonas, Spyros; Hart, Anne C.
2010-01-01
Spinal Muscular Atrophy (SMA) is caused by diminished function of the Survival of Motor Neuron (SMN) protein, but the molecular pathways critical for SMA pathology remain elusive. We have used genetic approaches in invertebrate models to identify conserved SMN loss of function modifier genes. Drosophila melanogaster and Caenorhabditis elegans each have a single gene encoding a protein orthologous to human SMN; diminished function of these invertebrate genes causes lethality and neuromuscular defects. To find genes that modulate SMN function defects across species, two approaches were used. First, a genome-wide RNAi screen for C. elegans SMN modifier genes was undertaken, yielding four genes. Second, we tested the conservation of modifier gene function across species; genes identified in one invertebrate model were tested for function in the other invertebrate model. Drosophila orthologs of two genes, which were identified originally in C. elegans, modified Drosophila SMN loss of function defects. C. elegans orthologs of twelve genes, which were originally identified in a previous Drosophila screen, modified C. elegans SMN loss of function defects. Bioinformatic analysis of the conserved, cross-species, modifier genes suggests that conserved cellular pathways, specifically endocytosis and mRNA regulation, act as critical genetic modifiers of SMN loss of function defects across species. PMID:21124729
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Identification of essential genes and synthetic lethal gene combinations in Escherichia coli K-12.
Mori, Hirotada; Baba, Tomoya; Yokoyama, Katsushi; Takeuchi, Rikiya; Nomura, Wataru; Makishi, Kazuichi; Otsuka, Yuta; Dose, Hitomi; Wanner, Barry L
2015-01-01
Here we describe the systematic identification of single genes and gene pairs, whose knockout causes lethality in Escherichia coli K-12. During construction of precise single-gene knockout library of E. coli K-12, we identified 328 essential gene candidates for growth in complex (LB) medium. Upon establishment of the Keio single-gene deletion library, we undertook the development of the ASKA single-gene deletion library carrying a different antibiotic resistance. In addition, we developed tools for identification of synthetic lethal gene combinations by systematic construction of double-gene knockout mutants. We introduce these methods herein.
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Lyng, Heidi; Lando, Malin; Brøvig, Runar S; Svendsrud, Debbie H; Johansen, Morten; Galteland, Eivind; Brustugun, Odd T; Meza-Zepeda, Leonardo A; Myklebost, Ola; Kristensen, Gunnar B; Hovig, Eivind; Stokke, Trond
2008-01-01
Absolute tumor DNA copy numbers can currently be achieved only on a single gene basis by using fluorescence in situ hybridization (FISH). We present GeneCount, a method for genome-wide calculation of absolute copy numbers from clinical array comparative genomic hybridization data. The tumor cell fraction is reliably estimated in the model. Data consistent with FISH results are achieved. We demonstrate significant improvements over existing methods for exploring gene dosages and intratumor copy number heterogeneity in cancers. PMID:18500990
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Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops
Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi
2016-01-01
Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement. PMID:27115872
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Lamontagne, Jason; Mell, Joshua C; Bouchard, Michael J
2016-02-01
Globally, a chronic hepatitis B virus (HBV) infection remains the leading cause of primary liver cancer. The mechanisms leading to the development of HBV-associated liver cancer remain incompletely understood. In part, this is because studies have been limited by the lack of effective model systems that are both readily available and mimic the cellular environment of a normal hepatocyte. Additionally, many studies have focused on single, specific factors or pathways that may be affected by HBV, without addressing cell physiology as a whole. Here, we apply RNA-seq technology to investigate transcriptome-wide, HBV-mediated changes in gene expression to identify single factors and pathways as well as networks of genes and pathways that are affected in the context of HBV replication. Importantly, these studies were conducted in an ex vivo model of cultured primary hepatocytes, allowing for the transcriptomic characterization of this model system and an investigation of early HBV-mediated effects in a biologically relevant context. We analyzed differential gene expression within the context of time-mediated gene-expression changes and show that in the context of HBV replication a number of genes and cellular pathways are altered, including those associated with metabolism, cell cycle regulation, and lipid biosynthesis. Multiple analysis pipelines, as well as qRT-PCR and an independent, replicate RNA-seq analysis, were used to identify and confirm differentially expressed genes. HBV-mediated alterations to the transcriptome that we identified likely represent early changes to hepatocytes following an HBV infection, suggesting potential targets for early therapeutic intervention. Overall, these studies have produced a valuable resource that can be used to expand our understanding of the complex network of host-virus interactions and the impact of HBV-mediated changes to normal hepatocyte physiology on viral replication.
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Yi, Zhenzhen; Strüder-Kypke, Michaela; Hu, Xiaozhong; Lin, Xiaofeng; Song, Weibo
2014-02-01
In order to assess how dataset-selection for multi-gene analyses affects the accuracy of inferred phylogenetic trees in ciliates, we chose five genes and the genus Paramecium, one of the most widely used model protist genera, and compared tree topologies of the single- and multi-gene analyses. Our empirical study shows that: (1) Using multiple genes improves phylogenetic accuracy, even when their one-gene topologies are in conflict with each other. (2) The impact of missing data on phylogenetic accuracy is ambiguous: resolution power and topological similarity, but not number of represented taxa, are the most important criteria of a dataset for inclusion in concatenated analyses. (3) As an example, we tested the three classification models of the genus Paramecium with a multi-gene based approach, and only the monophyly of the subgenus Paramecium is supported. Copyright © 2013 Elsevier Inc. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Anderson, W.F.; Martinell, J.; Whitney, J.B. III
The group of diseases called the thalassemias is the largest single-gene health problem in the world according the World Health Organization. The thalassemias are lethal hereditary anemias in which the infants cannot make their own blood. Three mouse mutants are shown to be models of the human disease ..cap alpha..-thalassemia. However, since an additional gene is affected, these mutants represent a particularly severe condition in which death occurs in the homozygous embryo even before globin genes are activated. Phenotypic and genotypic characteristics are described. (ACR)
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Tremblay, Johanne; Wang, Yujia; Raelson, John; Marois-Blanchet, Francois-Christophe; Wu, Zenghui; Luo, Hongyu; Bradley, Edward; Chalmers, John; Woodward, Mark; Harrap, Stephen; Hamet, Pavel; Wu, Jiangping
2017-03-08
EPH kinases and their ligands, ephrins (EFNs), have vital and diverse biological functions. We recently reported that Efnb3 gene deletion results in hypertension in female but not male mice. These data suggest that EFNB3 regulates blood pressure in a sex- and sex hormone-dependent way. In the present study, we conducted a human genetic study to assess the association of EFNB3 single nucleotide polymorphisms with human hypertension risks, using 3,448 patients with type 2 diabetes from the ADVANCE study (Action in Diabetes and Vascular Disease: Peterax and Diamicron MR Controlled Evaluation). We have observed significant association between 2 SNPs in the 3' untranslated region or within the adjacent region just 3' of the EFNB3 gene with hypertension, corroborating our findings from the mouse model. Thus, our investigation has shown that EFNB3 is a hypertension risk gene in certain individuals.
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Wang, Shuo; Gao, Li-Zhi
2016-11-01
The complete chloroplast genome sequence of foxtail millet (Setaria italica), an important food and fodder crop in the family Poaceae, is first reported in this study. The genome consists of 1 35 516 bp containing a pair of inverted repeats (IRs) of 21 804 bp separated by a large single-copy (LSC) region and a small single-copy (SSC) region of 79 896 bp and 12 012 bp, respectively. Coding sequences constitute 58.8% of the genome harboring 111 unique genes, 71 of which are protein-coding genes, 4 are rRNA genes, and 36 are tRNA genes. Phylogenetic analysis indicated foxtail millet clustered with Panicum virgatum and Echinochloa crus-galli belonging to the tribe Paniceae of the subfamily Panicoideae. This newly determined chloroplast genome will provide valuable information for the future breeding programs of valuable cereal crops in the family Poaceae.
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Aguiar, Bruno; Vieira, Jorge; Cunha, Ana E.; Fonseca, Nuno A.; Reboiro-Jato, David; Reboiro-Jato, Miguel; Fdez-Riverola, Florentino; Raspé, Olivier; Vieira, Cristina P.
2013-01-01
S-RNase-based gametophytic self-incompatibility evolved once before the split of the Asteridae and Rosidae. In Prunus (tribe Amygdaloideae of Rosaceae), the self-incompatibility S-pollen is a single F-box gene that presents the expected evolutionary signatures. In Malus and Pyrus (subtribe Pyrinae of Rosaceae), however, clusters of F-box genes (called SFBBs) have been described that are expressed in pollen only and are linked to the S-RNase gene. Although polymorphic, SFBB genes present levels of diversity lower than those of the S-RNase gene. They have been suggested as putative S-pollen genes, in a system of non-self recognition by multiple factors. Subsets of allelic products of the different SFBB genes interact with non-self S-RNases, marking them for degradation, and allowing compatible pollinations. This study performed a detailed characterization of SFBB genes in Sorbus aucuparia (Pyrinae) to address three predictions of the non-self recognition by multiple factors model. As predicted, the number of SFBB genes was large to account for the many S-RNase specificities. Secondly, like the S-RNase gene, the SFBB genes were old. Thirdly, amino acids under positive selection—those that could be involved in specificity determination—were identified when intra-haplotype SFBB genes were analysed using codon models. Overall, the findings reported here support the non-self recognition by multiple factors model. PMID:23606363
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NASA Astrophysics Data System (ADS)
Werthmann, Britta; Marwan, Wolfgang
2017-11-01
The developmental switch to sporulation in Physarum polycephalum is a phytochrome-mediated far-red light-induced cell fate decision that synchronously encompasses the entire multinucleate plasmodial cell and is associated with extensive reprogramming of the transcriptome. By repeatedly taking samples of single cells after delivery of a light stimulus pulse, we analysed differential gene expression in two mutant strains and in a heterokaryon of the two strains all of which display a different propensity for making the cell fate decision. Multidimensional scaling of the gene expression data revealed individually different single cell trajectories eventually leading to sporulation. Characterization of the trajectories as walks through states of gene expression discretized by hierarchical clustering allowed the reconstruction of Petri nets that model and predict the observed behavior. Structural analyses of the Petri nets indicated stimulus- and genotype-dependence of both, single cell trajectories and of the quasipotential landscape through which these trajectories are taken. The Petri net-based approach to the analysis and decomposition of complex cellular responses and of complex mutant phenotypes may provide a scaffold for the data-driven reconstruction of causal molecular mechanisms that shape the topology of the quasipotential landscape.
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Bengtsson, Niclas E.; Hall, John K.; Odom, Guy L.; Phelps, Michael P.; Andrus, Colin R.; Hawkins, R. David; Hauschka, Stephen D.; Chamberlain, Joel R.; Chamberlain, Jeffrey S.
2017-01-01
Gene replacement therapies utilizing adeno-associated viral (AAV) vectors hold great promise for treating Duchenne muscular dystrophy (DMD). A related approach uses AAV vectors to edit specific regions of the DMD gene using CRISPR/Cas9. Here we develop multiple approaches for editing the mutation in dystrophic mdx4cv mice using single and dual AAV vector delivery of a muscle-specific Cas9 cassette together with single-guide RNA cassettes and, in one approach, a dystrophin homology region to fully correct the mutation. Muscle-restricted Cas9 expression enables direct editing of the mutation, multi-exon deletion or complete gene correction via homologous recombination in myogenic cells. Treated muscles express dystrophin in up to 70% of the myogenic area and increased force generation following intramuscular delivery. Furthermore, systemic administration of the vectors results in widespread expression of dystrophin in both skeletal and cardiac muscles. Our results demonstrate that AAV-mediated muscle-specific gene editing has significant potential for therapy of neuromuscular disorders. PMID:28195574
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Storm Water Management Model Reference Manual Volume I, Hydrology
SWMM is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and gene...
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Storm Water Management Model Reference Manual Volume II – Hydraulics
SWMM is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and gene...
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Kim, Dongkyu; Ku, Sook Hee; Kim, Hyosuk; Jeong, Ji Hoon; Lee, Minhyung; Kwon, Ick Chan; Choi, Donghoon; Kim, Sun Hwa
2016-12-10
Gene therapy is aimed at selectively knocking up or knocking down the target genes involved in the development of diseases. In many human diseases, dysregulation of disease-associated genes is occurred concurrently: some genes are abnormally turned up and some are turned down. In the field of non-viral gene therapy, plasmid DNA (pDNA) and small interfering RNA (siRNA) are suggested as representative regulation tools for activating and silencing the expression of genes of interest, representatively. Herein, we simultaneously loaded both siRNA (Src homology region 2 domain-containing tyrosine phosphatase-1 siRNA, siSHP-1) for anti-apoptosis and pDNA (hypoxia-inducible vascular endothelial growth factor expression vector, pHI-VEGF) for angiogenesis in a single polymeric nanocarrier and used to synergistically attenuate ischemia-reperfusion (IR)-induced myocardial infarction, which is mainly caused by dysregulating of cardiac apoptosis and angiogenesis. For dual-modality cardiac gene delivery, siSHP-1 and pHI-VEGF were sequentially incorporated into a stable nanocomplex by using deoxycholic acid-modified polyethylenimine (DA-PEI). The resulting DA-PEI/siSHP-1/pHI-VEGF complexes exhibited the high structural stability against polyanion competition and the improved resistance to digestion by nucleases. The cardiac administration of DA-PEI/siSHP-1/pHI-VEGF reduced cardiomyocyte apoptosis and enhanced cardiac microvessel formation, thereby reducing infarct size in rat ischemia-reperfusion model. The simultaneous anti-apoptotic and angiogenic gene therapies synergized the cardioprotective effects of each strategy; thus our dual-modal single-carrier gene delivery system can be considered as a promising candidate for treating ischemic heart diseases. Copyright © 2016 Elsevier B.V. All rights reserved.
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Ovando-Roche, Patrick; Georgiadis, Anastasios; Smith, Alexander J; Pearson, Rachael A; Ali, Robin R
2017-01-01
A major cause of visual disorders is dysfunction and/or loss of the light-sensitive cells of the retina, the photoreceptors. To develop better treatments for patients, we need to understand how inherited retinal disease mutations result in the dysfunction of photoreceptors. New advances in the field of stem cell and gene editing research offer novel ways to model retinal dystrophies in vitro and present opportunities to translate basic biological insights into therapies. This brief review will discuss some of the issues that should be taken into account when carrying out disease modelling and gene editing of retinal cells. We will discuss (i) the use of human induced pluripotent stem cells (iPSCs) for disease modelling and cell therapy; (ii) the importance of using isogenic iPSC lines as controls; (iii) CRISPR/Cas9 gene editing of iPSCs; and (iv) in vivo gene editing using AAV vectors. Ground-breaking advances in differentiation of iPSCs into retinal organoids and methods to derive mature light sensitive photoreceptors from iPSCs. Furthermore, single AAV systems for in vivo gene editing have been developed which makes retinal in vivo gene editing therapy a real prospect. Genome editing is becoming a valuable tool for disease modelling and in vivo gene editing in the retina.
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Bao, Shaopan; Lu, Qicong; Dai, Heping; Zhang, Chao
2015-01-01
To develop applicable and susceptible models to evaluate the toxicity of nanoparticles, the antimicrobial effects of CuO nanoparticles (CuO-NPs) on various Saccharomyces cerevisiae (S. cerevisiae) strains (wild type, single-gene-deleted mutants, and multiple-gene-deleted mutants) were determined and compared. Further experiments were also conducted to analyze the mechanisms associated with toxicity using copper salt, bulk CuO (bCuO), carbon-shelled copper nanoparticles (C/Cu-NPs), and carbon nanoparticles (C-NPs) for comparisons. The results indicated that the growth inhibition rates of CuO-NPs for the wild-type and the single-gene-deleted strains were comparable, while for the multiple-gene deletion mutant, significantly higher toxicity was observed (P < 0.05). When the toxicity of the CuO-NPs to yeast cells was compared with the toxicities of copper salt and bCuO, we concluded that the toxicity of CuO-NPs should be attributed to soluble copper rather than to the nanoparticles. The striking difference in adverse effects of C-NPs and C/Cu-NPs with equivalent surface areas also proved this. A toxicity assay revealed that the multiple-gene-deleted mutant was significantly more sensitive to CuO-NPs than the wild type. Specifically, compared with the wild-type strain, copper was readily taken up by mutant strains when cell permeability genes were knocked out, and the mutants with deletions of genes regulated under oxidative stress (OS) were likely producing more reactive oxygen species (ROS). Hence, as mechanism-based gene inactivation could increase the susceptibility of yeast, the multiple-gene-deleted mutants should be improved model organisms to investigate the toxicity of nanoparticles. PMID:26386067
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Genome-wide association analysis of seedling root development in maize (Zea mays L.).
Pace, Jordon; Gardner, Candice; Romay, Cinta; Ganapathysubramanian, Baskar; Lübberstedt, Thomas
2015-02-05
Plants rely on the root system for anchorage to the ground and the acquisition and absorption of nutrients critical to sustaining productivity. A genome wide association analysis enables one to analyze allelic diversity of complex traits and identify superior alleles. 384 inbred lines from the Ames panel were genotyped with 681,257 single nucleotide polymorphism markers using Genotyping-by-Sequencing technology and 22 seedling root architecture traits were phenotyped. Utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3×10(-7)). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions of previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots. This study identifies putatively associated SNP markers associated with root traits at the seedling stage. Some SNPs were located within or near (<1 kb) gene models. These gene models identify possible candidate genes involved in root development at the seedling stage. These and respective linked or functional markers could be targets for breeders for marker assisted selection of seedling root traits.
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Model-based gene set analysis for Bioconductor.
Bauer, Sebastian; Robinson, Peter N; Gagneur, Julien
2011-07-01
Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.
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Bai, S; Hua, L; Wang, X; Liu, Q; Bao, Y
2018-05-11
Asthma is a complex and heterogeneous disease. We found that gene-gene interactions among IL13 rs20541, IL4 rs2243250, ADRB2 rs1042713, and FCER1B rs569108 in asthmatic children of Chinese Han nationality. This four-locus set constituted an optimal statistical interaction model. Objective: This study examined associations of the four-gene model consisting of IL13, IL4, FCER1B, and ADRB2 with the Asthma Predictive Index (API) and atopy in Chinese Han children. Four single-nucleotide polymorphisms (SNPs) in the four genes were genotyped in 385 preschool children with wheezing symptoms using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Student's t test and x2 tests were used for this analysis. : Significant correlations were found between the four-locus gene model and the stringent and loose API (both P<0.0001). Additionally, a high-risk asthma genotype was a risk factor for the positive API (stringent API: OR= 4.08, loose API: OR=2.36). We also found a statistically significant association of the four-locus gene model with atopy (P<0.01, OR= 2.09). Our results indicated that the four-locus gene model consisting of L13 rs20541, IL4 rs2243250, ADRB2 rs1042713 and FCER1B rs569108 was associated with the API and atopy. These findings provide an evidence of the gene model for determining a high risk of developing asthma and atopy in Chinese Han children.
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A stochastic model for optimizing composite predictors based on gene expression profiles.
Ramanathan, Murali
2003-07-01
This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics. The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a. The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method. The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.
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CD Nelson; TL Kubisiak; HV Amerson
2010-01-01
Fusiform rust disease remains the most destructive disease in pine plantations in the southern United States. Our ongoing research is designed to identify, map, and clone the interacting genes of the host and pathogen. Several resistance (R) genes have been identified and genetically mapped using informative pine families and single-spore isolate inoculations. In...
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Peng, Ruoqi; Sridhar, Sriram; Tyagi, Gaurav; Phillips, Jonathan E; Garrido, Rosario; Harris, Paul; Burns, Lisa; Renteria, Lorena; Woods, John; Chen, Leena; Allard, John; Ravindran, Palanikumar; Bitter, Hans; Liang, Zhenmin; Hogaboam, Cory M; Kitson, Chris; Budd, David C; Fine, Jay S; Bauer, Carla M T; Stevenson, Christopher S
2013-01-01
The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF), has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease.
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Pant, Kiran; Anderson, Brian; Perdana, Hendrik; Malinowski, Matthew A.; Win, Aye T.; Williams, Mark C.
2018-01-01
The model single-stranded DNA binding protein of bacteriophage T4, gene 32 protein (gp32) has well-established roles in DNA replication, recombination, and repair. gp32 is a single-chain polypeptide consisting of three domains. Based on thermodynamics and kinetics measurements, we have proposed that gp32 can undergo a conformational change where the acidic C-terminal domain binds internally to or near the single-stranded (ss) DNA binding surface in the core (central) domain, blocking ssDNA interaction. To test this model, we have employed a variety of experimental approaches and gp32 variants to characterize this conformational change. Utilizing stopped-flow methods, the association kinetics of wild type and truncated forms of gp32 with ssDNA were measured. When the C-domain is present, the log-log plot of k vs. [NaCl] shows a positive slope, whereas when it is absent (*I protein), there is little rate change with salt concentration, as expected for this model.A gp32 variant lacking residues 292–296 within the C-domain, ΔPR201, displays kinetic properties intermediate between gp32 and *I. The single molecule force-induced DNA helix-destabilizing activitiesas well as the single- and double-stranded DNA affinities of ΔPR201 and gp32 truncated at residue 295 also fall between full-length protein and *I. Finally, chemical cross-linking of recombinant C-domain and gp32 lacking both N- and C-terminal domains is inhibited by increasing concentrations of a short single-stranded oligonucleotide, and the salt dependence of cross-linking mirrors that expected for the model. Taken together, these results provide the first evidence in support of this model that have been obtained through structural probes. PMID:29634784
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Colot, V.; Rossignol, J. L.
1995-01-01
The ascomycete Ascobolus immersus has been extensively used as a model system for the genetic study of meiotic recombination. More recently, an epigenetic process, known as methylation induced premeiotically (MIP), that acts on duplicated sequences has been discovered in A. immersus and has raised a new interest in this fungus. To try and extend these studies, we have now cloned the A. immersus spore color gene b2, a well characterized recombination hot-spot. Isolation of the whole gene was verified by physical mapping of four large b2 alterations, followed by transformation and mutant rescue of a null b2 allele. Transformation was also used to duplicate b2 and subject it to MIP. As a result, we were able for the first time to observe gene silencing as early as just after meiosis and in single cells. Furthermore, we have found evidence for a modulating effect of MIP on b2 expression, depending on the region of the gene that is duplicated and hence subjected to MIP. PMID:8601475
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Gerchen, Jörn F.; Reichert, Samuel J.; Röhr, Johannes T.; Dieterich, Christoph; Kloas, Werner
2016-01-01
Large genome size, including immense repetitive and non-coding fractions, still present challenges for capacity, bioinformatics and thus affordability of whole genome sequencing in most amphibians. Here, we test the performance of a single transcriptome to understand whether it can provide a cost-efficient resource for species with large unknown genomes. Using RNA from six different tissues from a single Palearctic green toad (Bufo viridis) specimen and Hiseq2000, we obtained 22,5 Mio reads and publish >100,000 unigene sequences. To evaluate efficacy and quality, we first use this data to identify green toad specific candidate genes, known from other vertebrates for their role in sex determination and differentiation. Of a list of 37 genes, the transcriptome yielded 32 (87%), many of which providing the first such data for this non-model anuran species. However, for many of these genes, only fragments could be retrieved. In order to allow also applications to population genetics, we further used the transcriptome for the targeted development of 21 non-anonymous microsatellites and tested them in genetic families and backcrosses. Eleven markers were specifically developed to be located on the B. viridis sex chromosomes; for eight markers we can indeed demonstrate sex-specific transmission in genetic families. Depending on phylogenetic distance, several markers, which are sex-linked in green toads, show high cross-amplification success across the anuran phylogeny, involving nine systematic anuran families. Our data support the view that single transcriptome sequencing (based on multiple tissues) provides a reliable genomic resource and cost-efficient method for non-model amphibian species with large genome size and, despite limitations, should be considered as long as genome sequencing remains unaffordable for most species. PMID:27232626
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Lloyd Evans, Dyfed; Joshi, Shailesh Vinay
2017-07-01
In a genome context, sugarcane is a classic orphan crop, in that no genome and only very few genes have been assembled. We have devised a novel exome assembly methodology that has allowed us to assemble and characterize 49 genes that serve as herbicide targets, safener interacting proteins, and members of herbicide detoxification pathways within the sugarcane genome. We have structurally modelled the products of each of these genes, as well as determining allelic, genomic, and RNA-Seq based polymorphisms for each gene. This study provides the largest collection of sugarcane structures modelled to date. We demonstrate that sugarcane genes are highly polymorphic, revealing that each genotype is evolving both uniquely and independently. In addition, we present an exome assembly system for orphan crops that can be executed on commodity infrastructure, making exome assembly practical for any group. In terms of knowledge about herbicide modes of action and detoxification, we have advanced sugarcane from a crop where no information about any herbicide-associated gene was available to the situation where sugarcane is now a species with the single largest collection of known and annotated herbicide-associated genes.
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Cross-organism learning method to discover new gene functionalities.
Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro
2016-04-01
Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones, without influence of the evolutionary distance between the considered organisms. The generated ranked lists of reliably predicted annotations, which describe novel gene functionalities and have an associated likelihood value, are very valuable both to complement available annotations, for better coverage in biomedical knowledge discovery analyses, and to quicken the annotation curation process, by focusing it on the prioritized novel annotations predicted. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Kelly, Tanika N; Raj, Dominic; Rahman, Mahboob; Kretzler, Matthias; Kallem, Radhakrishna R; Ricardo, Ana C; Rosas, Sylvia E; Tao, Kaixiang; Xie, Dawei; Hamm, Lotuce Lee; He, Jiang
2015-10-01
We conducted single-marker, gene- and pathway-based analyses to examine the association between renin-angiotensin-aldosterone system (RAAS) variants and chronic kidney disease (CKD) progression among Chronic Renal Insufficiency Cohort study participants. A total of 1523 white and 1490 black subjects were genotyped for 490 single nucleotide polymorphisms (SNPs) in 12 RAAS genes as part of the ITMAT-Broad-CARe array. CKD progression phenotypes included decline in estimated glomerular filtration rate (eGFR) over time and the occurrence of a renal disease event, defined as incident end-stage renal disease or halving of eGFR from baseline. Mixed-effects models were used to examine SNP associations with eGFR decline, while Cox proportional hazards models tested SNP associations with renal events. Gene- and pathway-based analyses were conducted using the truncated product method. All analyses were stratified by race, and a Bonferroni correction was applied to adjust for multiple testing. Among white and black participants, eGFR declined an average of 1.2 and 2.3 mL/min/1.73 m(2)/year, respectively, while renal events occurred in a respective 11.5 and 24.9% of participants. We identified strong gene- and pathway-based associations with CKD progression. The AGT and RENBP genes were consistently associated with risk of renal events in separate analyses of white and black participants (both P < 1.00 × 10(-6)). Driven by the significant gene-based findings, the entire RAAS pathway was also associated with renal events in both groups (both P < 1.00 × 10(-6)). No single-marker associations with CKD progression were observed. The current study provides strong evidence for a role of the RAAS in CKD progression. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
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Kelly, Tanika N.; Raj, Dominic; Rahman, Mahboob; Kretzler, Matthias; Kallem, Radhakrishna R.; Ricardo, Ana C.; Rosas, Sylvia E.; Tao, Kaixiang; Xie, Dawei; Hamm, Lotuce Lee; He, Jiang; Appel, J.; Feldman, Harold I.; Go, Alan S.; Kusek, John W.; Lash, James P.; Ojo, Akinlolu; Townsend, Raymond R.
2015-01-01
Background We conducted single-marker, gene- and pathway-based analyses to examine the association between renin–angiotensin–aldosterone system (RAAS) variants and chronic kidney disease (CKD) progression among Chronic Renal Insufficiency Cohort study participants. Methods A total of 1523 white and 1490 black subjects were genotyped for 490 single nucleotide polymorphisms (SNPs) in 12 RAAS genes as part of the ITMAT-Broad-CARe array. CKD progression phenotypes included decline in estimated glomerular filtration rate (eGFR) over time and the occurrence of a renal disease event, defined as incident end-stage renal disease or halving of eGFR from baseline. Mixed-effects models were used to examine SNP associations with eGFR decline, while Cox proportional hazards models tested SNP associations with renal events. Gene- and pathway-based analyses were conducted using the truncated product method. All analyses were stratified by race, and a Bonferroni correction was applied to adjust for multiple testing. Results Among white and black participants, eGFR declined an average of 1.2 and 2.3 mL/min/1.73 m2/year, respectively, while renal events occurred in a respective 11.5 and 24.9% of participants. We identified strong gene- and pathway-based associations with CKD progression. The AGT and RENBP genes were consistently associated with risk of renal events in separate analyses of white and black participants (both P < 1.00 × 10−6). Driven by the significant gene-based findings, the entire RAAS pathway was also associated with renal events in both groups (both P < 1.00 × 10−6). No single-marker associations with CKD progression were observed. Conclusions The current study provides strong evidence for a role of the RAAS in CKD progression. PMID:25906781
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Macronuclear Genome Sequence of the Ciliate Tetrahymena thermophila, a Model Eukaryote
Eisen, Jonathan A; Coyne, Robert S; Wu, Martin; Wu, Dongying; Thiagarajan, Mathangi; Wortman, Jennifer R; Badger, Jonathan H; Ren, Qinghu; Amedeo, Paolo; Jones, Kristie M; Tallon, Luke J; Delcher, Arthur L; Salzberg, Steven L; Silva, Joana C; Haas, Brian J; Majoros, William H; Farzad, Maryam; Carlton, Jane M; Smith, Roger K; Garg, Jyoti; Pearlman, Ronald E; Karrer, Kathleen M; Sun, Lei; Manning, Gerard; Elde, Nels C; Turkewitz, Aaron P; Asai, David J; Wilkes, David E; Wang, Yufeng; Cai, Hong; Collins, Kathleen; Stewart, B. Andrew; Lee, Suzanne R; Wilamowska, Katarzyna; Weinberg, Zasha; Ruzzo, Walter L; Wloga, Dorota; Gaertig, Jacek; Frankel, Joseph; Tsao, Che-Chia; Gorovsky, Martin A; Keeling, Patrick J; Waller, Ross F; Patron, Nicola J; Cherry, J. Michael; Stover, Nicholas A; Krieger, Cynthia J; del Toro, Christina; Ryder, Hilary F; Williamson, Sondra C; Barbeau, Rebecca A; Hamilton, Eileen P; Orias, Eduardo
2006-01-01
The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC) has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC), which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases), using diverse resources (e.g., proteases and transporters), and generating structural complexity (e.g., kinesins and dyneins). In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates), no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from other model organisms makes T. thermophila an ideal model for functional genomic studies to address biological, biomedical, and biotechnological questions of fundamental importance. PMID:16933976
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Bayesian segregation analysis of production traits in two strains of laying chickens.
Szydłowski, M; Szwaczkowski, T
2001-02-01
A bayesian marker-free segregation analysis was applied to search for evidence of segregating genes affecting production traits in two strains of laying hens under long-term selection. The study used data from 6 generations of Leghorn (H77) and New Hampshire (N88) breeding nuclei. Estimation of marginal posterior means of variance components and parameters of a single autosomal locus was performed by use of the Gibbs sampler. The results showed evidence for a mixed major gene: -polygenic inheritance of BW and age at sexual maturity (ASM) in both strains. Single genes affecting BW and ASM explained one-third of the genetic variance. For ASM large overdominance effect at single locus was estimated. Initial egg production (IEP) and average egg weight (EW) showed a polygenic model of inheritance. The polygenic heritability estimates for BW, ASM, IEP, and EW were 0.32, 0.25, 0.23, and 0.08 in Strain H77 and 0.25, 0.24, 0.11, and 0.38 in Strain N88, respectively.
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Storm Water Management Model Reference Manual Volume III – Water Quality
SWMM is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and gene...
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Improved triacylglycerol production in Acinetobacter baylyi ADP1 by metabolic engineering.
Santala, Suvi; Efimova, Elena; Kivinen, Virpi; Larjo, Antti; Aho, Tommi; Karp, Matti; Santala, Ville
2011-05-18
Triacylglycerols are used in various purposes including food applications, cosmetics, oleochemicals and biofuels. Currently the main sources for triacylglycerol are vegetable oils, and microbial triacylglycerol has been suggested as an alternative for these. Due to the low production rates and yields of microbial processes, the role of metabolic engineering has become more significant. As a robust model organism for genetic and metabolic studies, and for the natural capability to produce triacylglycerol, Acinetobacter baylyi ADP1 serves as an excellent organism for modelling the effects of metabolic engineering for energy molecule biosynthesis. Beneficial gene deletions regarding triacylglycerol production were screened by computational means exploiting the metabolic model of ADP1. Four deletions, acr1, poxB, dgkA, and a triacylglycerol lipase were chosen to be studied experimentally both separately and concurrently by constructing a knock-out strain (MT) with three of the deletions. Improvements in triacylglycerol production were observed: the strain MT produced 5.6 fold more triacylglycerol (mg/g cell dry weight) compared to the wild type strain, and the proportion of triacylglycerol in total lipids was increased by 8-fold. In silico predictions of beneficial gene deletions were verified experimentally. The chosen single and multiple gene deletions affected beneficially the natural triacylglycerol metabolism of A. baylyi ADP1. This study demonstrates the importance of single gene deletions in triacylglycerol metabolism, and proposes Acinetobacter sp. ADP1 as a model system for bioenergetic studies regarding metabolic engineering.
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Single cell Hi-C reveals cell-to-cell variability in chromosome structure
Schoenfelder, Stefan; Yaffe, Eitan; Dean, Wendy; Laue, Ernest D.; Tanay, Amos; Fraser, Peter
2013-01-01
Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single cell Hi-C, combined with genome-wide statistical analysis and structural modeling of single copy X chromosomes, to show that individual chromosomes maintain domain organisation at the megabase scale, but show variable cell-to-cell chromosome territory structures at larger scales. Despite this structural stochasticity, localisation of active gene domains to boundaries of territories is a hallmark of chromosomal conformation. Single cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organisation underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns. PMID:24067610
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Multiple etiologies for Alzheimer disease are revealed by segregation analysis
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rao, V.S.; Connor-Lacke, L.; Cupplies, L.A.
1994-11-01
We have evaluated several transmission models for Alzheimer disease (AD), using the logistic regressive approach in 401 nuclear families of consecutively ascertained and rigorously diagnosed probands. Models postulating no major gene effect, random environmental transmission, recessive inheritance, and sporadic occurrence were rejected under varied assumptions regarding the associations among sex, age, and major gene susceptibility. Transmission of the disorder was not fully explained by a single Mendelian model for all families. Stratification of families as early- and late-onset by using the median of family mean onset ages showed that, regardless of the model studied, two groups of families fit bettermore » than a single group. AD in early-onset families is transmitted as an autosomal dominant trait with full penetrance in both sexes and has a gene frequency of 1.5%. Dominant inheritance also gave the best fit of the data in late-onset families, but this hypothesis was rejected, suggesting the presence of heterogeneity within this subset. Our study also revealed that genetically nonsusceptible males and females develop AD, indicating the presence of phenocopies within early-onset and late-onset groups. Moreover, our results suggest that the higher risk to females is not solely due to their increased longevity. 50 refs., 5 tabs.« less
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Stacking transgenes in forest trees.
Halpin, Claire; Boerjan, Wout
2003-08-01
Huge potential exists for improving plant raw materials and foodstuffs via metabolic engineering. To date, progress has mostly been limited to modulating the expression of single genes of well-studied pathways, such as the lignin biosynthetic pathway, in model species. However, a recent report illustrates a new level of sophistication in metabolic engineering by overexpressing one lignin enzyme while simultaneously suppressing the expression of another lignin gene in a tree, aspen. This novel approach to multi-gene manipulation has succeeded in concurrently improving several wood-quality traits.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Li, Guotian; Jain, Rashmi; Chern, Mawsheng
The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake (Oryza sativa ssp japonica), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportionmore » of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. In conclusion, this work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations.« less
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Naushad, Sohail; Barkema, Herman W.; Luby, Christopher; Condas, Larissa A. Z.; Nobrega, Diego B.; Carson, Domonique A.; De Buck, Jeroen
2016-01-01
Non-aureus staphylococci (NAS), a heterogeneous group of a large number of species and subspecies, are the most frequently isolated pathogens from intramammary infections in dairy cattle. Phylogenetic relationships among bovine NAS species are controversial and have mostly been determined based on single-gene trees. Herein, we analyzed phylogeny of bovine NAS species using whole-genome sequencing (WGS) of 441 distinct isolates. In addition, evolutionary relationships among bovine NAS were estimated from multilocus data of 16S rRNA, hsp60, rpoB, sodA, and tuf genes and sequences from these and numerous other single genes/proteins. All phylogenies were created with FastTree, Maximum-Likelihood, Maximum-Parsimony, and Neighbor-Joining methods. Regardless of methodology, WGS-trees clearly separated bovine NAS species into five monophyletic coherent clades. Furthermore, there were consistent interspecies relationships within clades in all WGS phylogenetic reconstructions. Except for the Maximum-Parsimony tree, multilocus data analysis similarly produced five clades. There were large variations in determining clades and interspecies relationships in single gene/protein trees, under different methods of tree constructions, highlighting limitations of using single genes for determining bovine NAS phylogeny. However, based on WGS data, we established a robust phylogeny of bovine NAS species, unaffected by method or model of evolutionary reconstructions. Therefore, it is now possible to determine associations between phylogeny and many biological traits, such as virulence, antimicrobial resistance, environmental niche, geographical distribution, and host specificity. PMID:28066335
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Yadav, Mukesh K.; Chae, Sung-Won; Go, Yoon Young; Im, Gi Jung; Song, Jae-Jun
2017-01-01
Staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA) are known to cause biofilm-related infections. MRSA and PA have been frequently isolated from chronically infected wounds, cystic fibrosis, chronic suppurative otitis media (CSOM), and from indwelling medical devices, and these bacteria co-exist; however, their interaction with each-other or with the host is not well known. In this study, we investigated MRSA and PA multi-species biofilm communities in vitro and their interaction with the host during in vivo colonization using an OM rat-model. In-vitro biofilm formation and in-vivo colonization were studied using CV-microtiter plate assay and OM rat-model respectively. The biofilms were viewed under scanning electron microscope and bacteria were enumerated using cfu counts. The differential gene expressions of rat mucosa colonized with single or multi-species of MRSA or PA were studied using RNA-sequencing of total transcriptome. In multi-species in-vitro biofilms PA partially inhibited SA growth. However, no significant inhibition of MRSA was detected during in-vivo colonization of multi-species in rat bullae. A total of 1,797 genes were significantly (p < 0.05) differentially expressed in MRSA or PA or MRSA + PA colonized rat middle ear mucosa with respect to the control. The poly-microbial colonization of MRSA and PA induced the differential expression of a significant number of genes that are involved in immune response, inflammation, signaling, development, and defense; these were not expressed with single species colonization by either MRSA or PA. Genes involved in defense, immune response, inflammatory response, and developmental process were exclusively up-regulated, and genes that are involved in nervous system signaling, development and transmission, regulation of cell growth and development, anatomical and system development, and cell differentiation were down-regulated after multi-species inoculation. These results indicate that poly-microbial colonization induces a host response that is different from that induced by single species infection. PMID:28459043
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Dynamic Network-Based Epistasis Analysis: Boolean Examples
Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.
2011-01-01
In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and single-path assumption, but also by demonstrating the importance of considering temporal dynamics, and specifically introducing the usefulness of Boolean network models and also reviewing some key properties of network approaches. PMID:22645556
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Handel, Adam E.; Chintawar, Satyan; Lalic, Tatjana; Whiteley, Emma; Vowles, Jane; Giustacchini, Alice; Argoud, Karene; Sopp, Paul; Nakanishi, Mahito; Bowden, Rory; Cowley, Sally; Newey, Sarah; Akerman, Colin; Ponting, Chris P.; Cader, M. Zameel
2016-01-01
Induced pluripotent stem cell (iPSC)-derived cortical neurons potentially present a powerful new model to understand corticogenesis and neurological disease. Previous work has established that differentiation protocols can produce cortical neurons, but little has been done to characterize these at cellular resolution. In particular, it is unclear to what extent in vitro two-dimensional, relatively disordered culture conditions recapitulate the development of in vivo cortical layer identity. Single-cell multiplex reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was used to interrogate the expression of genes previously implicated in cortical layer or phenotypic identity in individual cells. Totally, 93.6% of single cells derived from iPSCs expressed genes indicative of neuronal identity. High proportions of single neurons derived from iPSCs expressed glutamatergic receptors and synaptic genes. And, 68.4% of iPSC-derived neurons expressing at least one layer marker could be assigned to a laminar identity using canonical cortical layer marker genes. We compared single-cell RNA-seq of our iPSC-derived neurons to available single-cell RNA-seq data from human fetal and adult brain and found that iPSC-derived cortical neurons closely resembled primary fetal brain cells. Unexpectedly, a subpopulation of iPSC-derived neurons co-expressed canonical fetal deep and upper cortical layer markers. However, this appeared to be concordant with data from primary cells. Our results therefore provide reassurance that iPSC-derived cortical neurons are highly similar to primary cortical neurons at the level of single cells but suggest that current layer markers, although effective, may not be able to disambiguate cortical layer identity in all cells. PMID:26740550
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An efficient platform for genetic selection and screening of gene switches in Escherichia coli
Muranaka, Norihito; Sharma, Vandana; Nomura, Yoko; Yokobayashi, Yohei
2009-01-01
Engineered gene switches and circuits that can sense various biochemical and physical signals, perform computation, and produce predictable outputs are expected to greatly advance our ability to program complex cellular behaviors. However, rational design of gene switches and circuits that function in living cells is challenging due to the complex intracellular milieu. Consequently, most successful designs of gene switches and circuits have relied, to some extent, on high-throughput screening and/or selection from combinatorial libraries of gene switch and circuit variants. In this study, we describe a generic and efficient platform for selection and screening of gene switches and circuits in Escherichia coli from large libraries. The single-gene dual selection marker tetA was translationally fused to green fluorescent protein (gfpuv) via a flexible peptide linker and used as a dual selection and screening marker for laboratory evolution of gene switches. Single-cycle (sequential positive and negative selections) enrichment efficiencies of >7000 were observed in mock selections of model libraries containing functional riboswitches in liquid culture. The technique was applied to optimize various parameters affecting the selection outcome, and to isolate novel thiamine pyrophosphate riboswitches from a complex library. Artificial riboswitches with excellent characteristics were isolated that exhibit up to 58-fold activation as measured by fluorescent reporter gene assay. PMID:19190095
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Almeida, Ana M. R.; Brown, Andrew; Specht, Chelsea D.
2013-01-01
Flowers of the order Zingiberales demonstrate a remarkable trend of reduction in the number of fertile stamens; from five or six fertile, filamentous stamens bearing two thecae each in Musaceae and Strelitziaceae to just a single petaloid stamen bearing a single theca in Cannaceae and Marantaceae. As one progresses from ancestral to derived floral forms, 5–6 fertile stamens are replaced by 4–5 petaloid staminodes. In Cannaceae and Costaceae, all members of the androecial whorls exhibit petaloidy, including the fertile stamen. In Costaceae, a single fertile stamen develops two thecae embedded on a broad petaloid appendage, while in Cannaceae the single fertile stamen is further reduced to a single theca with a prominent, expanded petaloid appendage. Whether petaloidy of the fertile stamen is a synapomorphy of the entire ginger clade (including Cannaceae, Costaceae, Zingiberaceae and Marantaceae), or the result of independent convergent evolution in Cannaceae, Costaceae, and some Zingiberaceae, is unclear. We combine a developmental series of the formation of the petaloid fertile stamen in Canna indica with data on the expression of B- and C-class floral organ identity genes to elucidate the organogenetic identity of the petaloid stamen and staminodes. Our data indicate that the single fertile theca in C. indica and its petaloid appendage are derived from one-half of the primordium of a single stamen, with no contribution from the remaining part of the stamen (i.e. the second theca primordium) which aborts early in development. The petaloid appendage expands later, and develops from the position of the filament/connective of the developing theca. Floral identity gene expression shows that petal identity genes (i.e. B-class genes) are expressed in all floral organs studied while C-class gene AG-1 is expressed in an increasing gradient from sepals to gynoecium, and AG-2 is expressed in all floral organs except the petals. The canonical model for molecular specification of floral organ identity is not sufficient to explain petaloidy in the androecial whorl in Canna sp. Further studies understanding the regulation of gene networks are required. PMID:23539493
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Almeida, Ana M R; Brown, Andrew; Specht, Chelsea D
2013-01-01
Flowers of the order Zingiberales demonstrate a remarkable trend of reduction in the number of fertile stamens; from five or six fertile, filamentous stamens bearing two thecae each in Musaceae and Strelitziaceae to just a single petaloid stamen bearing a single theca in Cannaceae and Marantaceae. As one progresses from ancestral to derived floral forms, 5-6 fertile stamens are replaced by 4-5 petaloid staminodes. In Cannaceae and Costaceae, all members of the androecial whorls exhibit petaloidy, including the fertile stamen. In Costaceae, a single fertile stamen develops two thecae embedded on a broad petaloid appendage, while in Cannaceae the single fertile stamen is further reduced to a single theca with a prominent, expanded petaloid appendage. Whether petaloidy of the fertile stamen is a synapomorphy of the entire ginger clade (including Cannaceae, Costaceae, Zingiberaceae and Marantaceae), or the result of independent convergent evolution in Cannaceae, Costaceae, and some Zingiberaceae, is unclear. We combine a developmental series of the formation of the petaloid fertile stamen in Canna indica with data on the expression of B- and C-class floral organ identity genes to elucidate the organogenetic identity of the petaloid stamen and staminodes. Our data indicate that the single fertile theca in C. indica and its petaloid appendage are derived from one-half of the primordium of a single stamen, with no contribution from the remaining part of the stamen (i.e. the second theca primordium) which aborts early in development. The petaloid appendage expands later, and develops from the position of the filament/connective of the developing theca. Floral identity gene expression shows that petal identity genes (i.e. B-class genes) are expressed in all floral organs studied while C-class gene AG-1 is expressed in an increasing gradient from sepals to gynoecium, and AG-2 is expressed in all floral organs except the petals. The canonical model for molecular specification of floral organ identity is not sufficient to explain petaloidy in the androecial whorl in Canna sp. Further studies understanding the regulation of gene networks are required.
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Model-based design of RNA hybridization networks implemented in living cells.
Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter; Daròs, José-Antonio; Jaramillo, Alfonso
2017-09-19
Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Ellegood, J; Anagnostou, E; Babineau, B A; Crawley, J N; Lin, L; Genestine, M; DiCicco-Bloom, E; Lai, J K Y; Foster, J A; Peñagarikano, O; Geschwind, D H; Pacey, L K; Hampson, D R; Laliberté, C L; Mills, A A; Tam, E; Osborne, L R; Kouser, M; Espinosa-Becerra, F; Xuan, Z; Powell, C M; Raznahan, A; Robins, D M; Nakai, N; Nakatani, J; Takumi, T; van Eede, M C; Kerr, T M; Muller, C; Blakely, R D; Veenstra-VanderWeele, J; Henkelman, R M; Lerch, J P
2015-02-01
Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1-2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.
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Whalen, M C; Innes, R W; Bent, A F; Staskawicz, B J
1991-01-01
To develop a model system for molecular genetic analysis of plant-pathogen interactions, we studied the interaction between Arabidopsis thaliana and the bacterial pathogen Pseudomonas syringae pv tomato (Pst). Pst strains were found to be virulent or avirulent on specific Arabidopsis ecotypes, and single ecotypes were resistant to some Pst strains and susceptible to others. In many plant-pathogen interactions, disease resistance is controlled by the simultaneous presence of single plant resistance genes and single pathogen avirulence genes. Therefore, we tested whether avirulence genes in Pst controlled induction of resistance in Arabidopsis. Cosmids that determine avirulence were isolated from Pst genomic libraries, and the Pst avirulence locus avrRpt2 was defined. This allowed us to construct pathogens that differed only by the presence or absence of a single putative avirulence gene. We found that Arabidopsis ecotype Col-0 was susceptible to Pst strain DC3000 but resistant to the same strain carrying avrRpt2, suggesting that a single locus in Col-0 determines resistance. As a first step toward genetically mapping the postulated resistance locus, an ecotype susceptible to infection by DC3000 carrying avrRpt2 was identified. The avrRpt2 locus from Pst was also moved into virulent strains of the soybean pathogen P. syringae pv glycinea to test whether this locus could determine avirulence on soybean. The resulting strains induced a resistant response in a cultivar-specific manner, suggesting that similar resistance mechanisms may function in Arabidopsis and soybean.
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NASA Astrophysics Data System (ADS)
Huang, Guan-Rong; Saakian, David B.; Hu, Chin-Kun
2018-01-01
Studying gene regulation networks in a single cell is an important, interesting, and hot research topic of molecular biology. Such process can be described by chemical master equations (CMEs). We propose a Hamilton-Jacobi equation method with finite-size corrections to solve such CMEs accurately at the intermediate region of switching, where switching rate is comparable to fast protein production rate. We applied this approach to a model of self-regulating proteins [H. Ge et al., Phys. Rev. Lett. 114, 078101 (2015), 10.1103/PhysRevLett.114.078101] and found that as a parameter related to inducer concentration increases the probability of protein production changes from unimodal to bimodal, then to unimodal, consistent with phenotype switching observed in a single cell.
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duVerle, David A; Yotsukura, Sohiya; Nomura, Seitaro; Aburatani, Hiroyuki; Tsuda, Koji
2016-09-13
Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .
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Pitchiaya, Sethuramasundaram; Krishnan, Vishalakshi; Custer, Thomas C.; Walter, Nils G.
2013-01-01
Non-coding RNAs (ncRNAs) recently were discovered to outnumber their protein-coding counterparts, yet their diverse functions are still poorly understood. Here we report on a method for the intracellular Single-molecule High Resolution Localization and Counting (iSHiRLoC) of microRNAs (miRNAs), a conserved, ubiquitous class of regulatory ncRNAs that controls the expression of over 60% of all mammalian protein coding genes post-transcriptionally, by a mechanism shrouded by seemingly contradictory observations. We present protocols to execute single particle tracking (SPT) and single-molecule counting of functional microinjected, fluorophore-labeled miRNAs and thereby extract diffusion coefficients and molecular stoichiometries of micro-ribonucleoprotein (miRNP) complexes from living and fixed cells, respectively. This probing of miRNAs at the single molecule level sheds new light on the intracellular assembly/disassembly of miRNPs, thus beginning to unravel the dynamic nature of this important gene regulatory pathway and facilitating the development of a parsimonious model for their obscured mechanism of action. PMID:23820309
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Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia
2012-01-01
Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. PMID:23071521
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Annotating novel genes by integrating synthetic lethals and genomic information
Schöner, Daniel; Kalisch, Markus; Leisner, Christian; Meier, Lukas; Sohrmann, Marc; Faty, Mahamadou; Barral, Yves; Peter, Matthias; Gruissem, Wilhelm; Bühlmann, Peter
2008-01-01
Background Large scale screening for synthetic lethality serves as a common tool in yeast genetics to systematically search for genes that play a role in specific biological processes. Often the amounts of data resulting from a single large scale screen far exceed the capacities of experimental characterization of every identified target. Thus, there is need for computational tools that select promising candidate genes in order to reduce the number of follow-up experiments to a manageable size. Results We analyze synthetic lethality data for arp1 and jnm1, two spindle migration genes, in order to identify novel members in this process. To this end, we use an unsupervised statistical method that integrates additional information from biological data sources, such as gene expression, phenotypic profiling, RNA degradation and sequence similarity. Different from existing methods that require large amounts of synthetic lethal data, our method merely relies on synthetic lethality information from two single screens. Using a Multivariate Gaussian Mixture Model, we determine the best subset of features that assign the target genes to two groups. The approach identifies a small group of genes as candidates involved in spindle migration. Experimental testing confirms the majority of our candidates and we present she1 (YBL031W) as a novel gene involved in spindle migration. We applied the statistical methodology also to TOR2 signaling as another example. Conclusion We demonstrate the general use of Multivariate Gaussian Mixture Modeling for selecting candidate genes for experimental characterization from synthetic lethality data sets. For the given example, integration of different data sources contributes to the identification of genetic interaction partners of arp1 and jnm1 that play a role in the same biological process. PMID:18194531
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Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao
2016-04-01
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.
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Potentials of single-cell biology in identification and validation of disease biomarkers.
Niu, Furong; Wang, Diane C; Lu, Jiapei; Wu, Wei; Wang, Xiangdong
2016-09-01
Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.
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Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae
Rex, Rene; Bill, Nelli; Schmidt-Hohagen, Kerstin; Schomburg, Dietmar
2013-01-01
The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae. PMID:24098096
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Single-cut genome editing restores dystrophin expression in a new mouse model of muscular dystrophy
Amoasii, Leonela; Long, Chengzu; Li, Hui; Mireault, Alex A.; Shelton, John M.; Sanchez-Ortiz, Efrain; McAnally, John R.; Bhattacharyya, Samadrita; Schmidt, Florian; Grimm, Dirk; Hauschka, Stephen D.; Bassel-Duby, Rhonda; Olson, Eric N.
2017-01-01
Duchenne muscular dystrophy (DMD) is a severe, progressive muscle disease caused by mutations in the dystrophin gene. The majority of DMD mutations are deletions that prematurely terminate the dystrophin protein. Deletions of exon 50 of the dystrophin gene are among the most common single exon deletions causing DMD. Such mutations can be corrected by skipping exon 51, thereby restoring the dystrophin reading frame. Using clustered regularly interspaced short palindromic repeats/CRISPR-associated 9 (CRISPR/Cas9), we generated a DMD mouse model by deleting exon 50. These ΔEx50 mice displayed severe muscle dysfunction, which was corrected by systemic delivery of adeno-associated virus encoding CRISPR/Cas9 genome editing components. We optimized the method for dystrophin reading frame correction using a single guide RNA that created reframing mutations and allowed skipping of exon 51. In conjunction with muscle-specific expression of Cas9, this approach restored up to 90% of dystrophin protein expression throughout skeletal muscles and the heart of ΔEx50 mice. This method of permanently bypassing DMD mutations using a single cut in genomic DNA represents a step toward clinical correction of DMD mutations and potentially those of other neuromuscular disorders. PMID:29187645
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Cohen, M M
1989-12-01
The role of chance using a stochastic single gene model has been shown to generate a continuous liability curve resembling that obtained from a multifactorial threshold model. Segregation of some malformations may be explained by a single defective gene that predisposes to, but does not necessarily result in, the malformation. Low penetrance and remarkably variable expressivity that characterize a number of presumed autosomal dominant malformation syndromes are possibly reflections of specific stochastic influences that are intrinsic to the embryonic process itself. Gene analysis is discussed and illustrated. Using polymorphic DNA probes to study cleft palate and ankyloglossia in males and ankyloglossia only in females in a large Icelandic family, the responsible gene was found to be located on the long arm of the X chromosome in the Xq21.1 region. In addition to gene analysis, some of the implications of transgenic analysis using mice are discussed. Among disorders of collagen metabolism, both the osteogenesis imperfectas and the Ehlers-Danlos syndromes are shown to represent genetically heterogeneous groups of connective tissue disorders. The days of thinking about osteogenesis imperfecta as one disorder and the Ehlers-Danlos syndrome as another are a thing of the past; persistence of such thinking is erroneous and misleading. Of the many disorders affecting bone mineral, the complexities of hypophosphatasia and pseudohypoparathyroidism are singled out for discussion. For lysosomal storage disorders, an overview of the mucopolysaccharidoses is provided. Finally, the recently delineated peroxisomal disorders--hyperpipecolic acidemia, rhizomelic chondrodysplasia, neonatal adrenoleukodystrophy, Zellweger syndrome, and infantile Refsum disease--are known to share a distinctive biochemical phenotype, although fibroblast complementation analysis suggests that some of these disorders are etiologically distinct.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Rinchik, E.M.; Carpenter, D.A.; Handel, M.A.
1995-07-03
Variability and complexity of phenotypes observed in microdeletion syndromes can be due to deletion of a single gene whose product participates in several aspects of development or can be due to the deletion of a number of tightly linked genes, each adding its own effect to the syndrome. The p{sup 6H} deletion in mouse chromosome 7 presents a good model with which to address this question of multigene vs. single-gene pleiotropy. Mice homozygous for the p{sup 6H} deletion are diluted in pigmentation, are smaller than their littermates, and manifest a nervous jerky-gait phenotype. Male homozygotes are sterile and exhibit profoundmore » abnormalities in spermiogenesis. By using N-ethyl-N-nitrosourea (EtNU) mutagenesis and a breeding protocol designed to recover recessive mutations expressed hemizygously opposite a large p-locus deletion, we have generated three noncomplementing mutations that map to the p{sup 6H} deletion. Each of these EtNU-induced mutations has adverse effects on the size, nervous behavior, and progression of spermiogenesis that characterize p{sup 6H} deletion homozygotes. Because etNU is thought to induce primarily intragenic (point) mutations in mouse stem-cell spermatogonia, we propose that the trio of phenotypes (runtiness, nervous jerky gait, and male sterility) expressed in p{sup 6H} deletion homozygotes is the result of deletion of a single highly pleiotropic gene. We also predict that a homologous single locus, quite possibly tightly linked and distal to the D15S12 (P) locus in human chromosome 15q11-q13, may be associated with similar developmental abnormalities in humans. 29 refs., 3 figs., 1 tab.« less
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A single-gene explanation for the probability of having idiopathic talipes equinovarus
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rebbeck, T.R.; Buetow, K.H.; Dietz, F.R.
1993-11-01
It has been hypothesized that the pathogenesis of idiopathic talipes equinovarus (ITEV, or clubfoot) is explained by genetic regulation of development and growth. The objective of the present study was to determine whether a single Mendelian gene explains the probability of having ITEV in a sample of 143 Caucasian pedigrees from Iowa. These pedigrees were ascertained through probands with ITEV. Complex segregation analyses were undertaken using a regressive logistic model. The results of these analyses strongly rejected the hypotheses that the probability of having ITEV in these pedigrees was explained by a non-Mendelian pattern of transmission with residual sibling correlation,more » a nontransmitted (environmental) factor with residual sibling correlation, or residual sibling correlation alone. These results were consistent with the hypothesis that the probability of having ITEV was explained by the Mendelian segregation of a single gene with two alleles plus the effects of some unmeasured factor(s) shared among siblings. The segregation of alleles at this single Mendelian gene indicated that the disease allele A was incompletely dominant to the nondisease allele B. The disease allele A, associated with ITEV affection, was estimated to occur in the population of inference with a frequency of .007. After adjusting for sex-specific population incidences of ITEV, the conditional probability (penetrance) of ITEV affection given the AA, AB, and BB genotypes was computed to be 1.0, 0.039, and .0006, respectively. Individual pedigrees in this sample that most strongly supported the single Mendelian gene hypothesis were identified. These pedigrees are candidates for genetic linkage analyses or DNA association studies. 35 refs., 2 figs., 7 tabs.« less
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Investor Outlook: Solving Gene Therapy Pricing…with a Cures Voucher?
Schimmer, Joshua; Breazzano, Steven
2016-12-01
Gene therapy reimbursement continues to be an intense topic of discussion in the field given the unique and durable benefits from a single administration and generally small patient populations against a reimbursement framework that is not optimized for such "cures" or long-lived benefits. As more gene therapy programs enter the market and late-stage development, it is increasingly important for the field to define a reimbursement model that works for all stakeholders in order to encourage the next wave of innovation. To add to the discussion around new payment models and potential solutions, we propose a flexible voucher system that takes advantage of existing infrastructure, precedent, and regulatory frameworks.
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Gene Model Annotations for Drosophila melanogaster: The Rule-Benders
Crosby, Madeline A.; Gramates, L. Sian; dos Santos, Gilberto; Matthews, Beverley B.; St. Pierre, Susan E.; Zhou, Pinglei; Schroeder, Andrew J.; Falls, Kathleen; Emmert, David B.; Russo, Susan M.; Gelbart, William M.
2015-01-01
In the context of the FlyBase annotated gene models in Drosophila melanogaster, we describe the many exceptional cases we have curated from the literature or identified in the course of FlyBase analysis. These range from atypical but common examples such as dicistronic and polycistronic transcripts, noncanonical splices, trans-spliced transcripts, noncanonical translation starts, and stop-codon readthroughs, to single exceptional cases such as ribosomal frameshifting and HAC1-type intron processing. In FlyBase, exceptional genes and transcripts are flagged with Sequence Ontology terms and/or standardized comments. Because some of the rule-benders create problems for handlers of high-throughput data, we discuss plans for flagging these cases in bulk data downloads. PMID:26109356
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Fast and robust group-wise eQTL mapping using sparse graphical models.
Cheng, Wei; Shi, Yu; Zhang, Xiang; Wang, Wei
2015-01-16
Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways. We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods. Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes.
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Aboklaish, Ali F.; Dordet-Frisoni, Emilie; Citti, Christine; Toleman, Mark A; Glass, John I.; Spiller, O. Brad
2015-01-01
While transposon mutagenesis has been successfully used for Mycoplasma spp. to disrupt and determine non-essential genes, previous attempts with Ureaplasma spp. have been unsuccessful. Using a polyethylene glycol-transformation enhancing protocol, we were able to transform three separate serovars of Ureaplasma parvum with a Tn4001-based mini-transposon plasmid containing a gentamicin resistance selection marker. Despite the large degree of homology between Ureaplasma parvum and Ureaplasma urealyticum, all attempts to transform the latter in parallel failed, with the exception of a single clinical U. urealyticum isolate. PCR probing and sequencing were used to confirm transposon insertion into the bacterial genome and identify disrupted genes. Transformation of prototype serovar 3 consistently resulted in transfer only of sequence between the mini-transposon inverted repeats, but some strains showed additional sequence transfer. Transposon insertion occurred randomly in the genome resulting in unique disruption of genes UU047, UU390, UU440, UU450, UU520, UU526, UU582 for single clones from a panel of screened clones. An intergenic insertion between genes UU187 and UU188 was also characterised. Two phenotypic alterations were observed in the mutated strains: Disruption of a DEAD-box RNA helicase (UU582) altered growth kinetics, while the U. urealyticum strain lost resistance to serum attack coincident with disruption of gene UUR10_137 and loss of expression of a 41 kDa protein. Transposon mutagenesis was used successfully to insert single copies of a mini-transposon into the genome and disrupt genes leading to phenotypic changes in Ureaplasma parvum strains. This method can now be used to deliver exogenous genes for expression and determine essential genes for Ureaplasma parvum replication in culture and experimental models. PMID:25444567
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Aboklaish, Ali F; Dordet-Frisoni, Emilie; Citti, Christine; Toleman, Mark A; Glass, John I; Spiller, O Brad
2014-11-01
While transposon mutagenesis has been successfully used for Mycoplasma spp. to disrupt and determine non-essential genes, previous attempts with Ureaplasma spp. have been unsuccessful. Using a polyethylene glycol-transformation enhancing protocol, we were able to transform three separate serovars of Ureaplasma parvum with a Tn4001-based mini-transposon plasmid containing a gentamicin resistance selection marker. Despite the large degree of homology between Ureaplasma parvum and Ureaplasma urealyticum, all attempts to transform the latter in parallel failed, with the exception of a single clinical U. urealyticum isolate. PCR probing and sequencing were used to confirm transposon insertion into the bacterial genome and identify disrupted genes. Transformation of prototype serovar 3 consistently resulted in transfer only of sequence between the mini-transposon inverted repeats, but some strains showed additional sequence transfer. Transposon insertion occurred randomly in the genome resulting in unique disruption of genes UU047, UU390, UU440, UU450, UU520, UU526, UU582 for single clones from a panel of screened clones. An intergenic insertion between genes UU187 and UU188 was also characterised. Two phenotypic alterations were observed in the mutated strains: Disruption of a DEAD-box RNA helicase (UU582) altered growth kinetics, while the U. urealyticum strain lost resistance to serum attack coincident with disruption of gene UUR10_137 and loss of expression of a 41 kDa protein. Transposon mutagenesis was used successfully to insert single copies of a mini-transposon into the genome and disrupt genes leading to phenotypic changes in Ureaplasma parvum strains. This method can now be used to deliver exogenous genes for expression and determine essential genes for Ureaplasma parvum replication in culture and experimental models. Copyright © 2014 Elsevier GmbH. All rights reserved.
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Miklós, István
2009-01-01
Homologous genes originate from a common ancestor through vertical inheritance, duplication, or horizontal gene transfer. Entire homolog families spawned by a single ancestral gene can be identified across multiple genomes based on protein sequence similarity. The sequences, however, do not always reveal conclusively the history of large families. To study the evolution of complete gene repertoires, we propose here a mathematical framework that does not rely on resolved gene family histories. We show that so-called phylogenetic profiles, formed by family sizes across multiple genomes, are sufficient to infer principal evolutionary trends. The main novelty in our approach is an efficient algorithm to compute the likelihood of a phylogenetic profile in a model of birth-and-death processes acting on a phylogeny. We examine known gene families in 28 archaeal genomes using a probabilistic model that involves lineage- and family-specific components of gene acquisition, duplication, and loss. The model enables us to consider all possible histories when inferring statistics about archaeal evolution. According to our reconstruction, most lineages are characterized by a net loss of gene families. Major increases in gene repertoire have occurred only a few times. Our reconstruction underlines the importance of persistent streamlining processes in shaping genome composition in Archaea. It also suggests that early archaeal genomes were as complex as typical modern ones, and even show signs, in the case of the methanogenic ancestor, of an extremely large gene repertoire. PMID:19570746
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de Vega-Bartol, José J; Santos, Raquen Raissa; Simões, Marta; Miguel, Célia M
2013-05-01
Suitable internal control genes to normalize qPCR data from different stages of embryo development and germination were identified in two representative conifer species. Clonal propagation by somatic embryogenesis has a great application potentiality in conifers. Quantitative PCR (qPCR) is widely used for gene expression analysis during somatic embryogenesis and embryo germination. No single reference gene is universal, so a systematic characterization of endogenous genes for concrete conditions is fundamental for accuracy. We identified suitable internal control genes to normalize qPCR data obtained at different steps of somatic embryogenesis (embryonal mass proliferation, embryo maturation and germination) in two representative conifer species, Pinus pinaster and Picea abies. Candidate genes included endogenous genes commonly used in conifers, genes previously tested in model plants, and genes with a lower variation of the expression along embryo development according to genome-wide transcript profiling studies. Three different algorithms were used to evaluate expression stability. The geometric average of the expression values of elongation factor-1α, α-tubulin and histone 3 in P. pinaster, and elongation factor-1α, α-tubulin, adenosine kinase and CAC in P. abies were adequate for expression studies throughout somatic embryogenesis. However, improved accuracy was achieved when using other gene combinations in experiments with samples at a single developmental stage. The importance of studies selecting reference genes to use in different tissues or developmental stages within one or close species, and the instability of commonly used reference genes, is highlighted.
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Bish, Lawrence T.; Sleeper, Meg M.; Brainard, Benjamin; Cole, Stephen; Russell, Nicholas; Withnall, Elanor; Arndt, Jason; Reynolds, Caryn; Davison, Ellen; Sanmiguel, Julio; Wu, Di; Gao, Guangping; Wilson, James M.; Sweeney, H. Lee
2011-01-01
Achieving efficient cardiac gene transfer in a large animal model has proven to be technically challenging. Prior strategies have employed cardio-pulmonary bypass or dual catheterization with the aid of vasodilators to deliver vectors, such as adenovirus, adeno-associated virus or plasmid DNA. While single stranded adeno-associated virus vectors have shown the greatest promise, they suffer from delayed expression, which might be circumvented by using self-complementary vectors. We sought to optimize cardiac gene transfer using a percutaneous transendocardial injection catheter to deliver adeno-associated virus vectors to the canine myocardium. Four vectors were evaluated—single stranded adeno-associated virus 9, self-complementary adeno-associated virus 9, self-complementary adeno-associated virus 8, self-complementary adeno-associated virus 6—so that comparison could be made between single stranded and self complementary vectors as well as among serotypes 9, 8, and 6. We demonstrate that self-complementary adeno-associated virus is superior to single stranded adeno-associated virus and that adeno-associated virus 6 is superior to other serotypes evaluated. Biodistribution studies revealed that vector genome copies were 15 to 4000 times more abundant in the heart than in any other organ for self-complementary adeno-associated virus 6. Percutaneous transendocardial injection of self-complementary adeno-associated virus 6 is a safe, effective method for achieving efficient cardiac gene transfer. PMID:18813281
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Modeling DNA methylation by analyzing the individual configurations of single molecules
Affinito, Ornella; Scala, Giovanni; Palumbo, Domenico; Florio, Ermanno; Monticelli, Antonella; Miele, Gennaro; Avvedimento, Vittorio Enrico; Usiello, Alessandro; Chiariotti, Lorenzo; Cocozza, Sergio
2016-01-01
ABSTRACT DNA methylation is often analyzed by reporting the average methylation degree of each cytosine. In this study, we used a single molecule methylation analysis in order to look at the methylation conformation of individual molecules. Using D-aspartate oxidase as a model gene, we performed an in-depth methylation analysis through the developmental stages of 3 different mouse tissues (brain, lung, and gut), where this gene undergoes opposite methylation destiny. This approach allowed us to track both methylation and demethylation processes at high resolution. The complexity of these dynamics was markedly simplified by introducing the concept of methylation classes (MCs), defined as the number of methylated cytosines per molecule, irrespective of their position. The MC concept smooths the stochasticity of the system, allowing a more deterministic description. In this framework, we also propose a mathematical model based on the Markov chain. This model aims to identify the transition probability of a molecule from one MC to another during methylation and demethylation processes. The results of our model suggest that: 1) both processes are ruled by a dominant class of phenomena, namely, the gain or loss of one methyl group at a time; and 2) the probability of a single CpG site becoming methylated or demethylated depends on the methylation status of the whole molecule at that time. PMID:27748645
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Ahmed, Seemin Seher; Li, Huapeng; Cao, Chunyan; Sikoglu, Elif M; Denninger, Andrew R; Su, Qin; Eaton, Samuel; Liso Navarro, Ana A; Xie, Jun; Szucs, Sylvia; Zhang, Hongwei; Moore, Constance; Kirschner, Daniel A; Seyfried, Thomas N; Flotte, Terence R; Matalon, Reuben; Gao, Guangping
2013-01-01
Canavan's disease (CD) is a fatal pediatric leukodystrophy caused by mutations in aspartoacylase (AspA) gene. Currently, there is no effective treatment for CD; however, gene therapy is an attractive approach to ameliorate the disease. Here, we studied progressive neuropathology and gene therapy in short-lived (≤1 month) AspA−/− mice, a bona-fide animal model for the severest form of CD. Single intravenous (IV) injections of several primate-derived recombinant adeno-associated viruses (rAAVs) as late as postnatal day 20 (P20) completely rescued their early lethality and alleviated the major disease symptoms, extending survival in P0-injected rAAV9 and rAAVrh8 groups to as long as 2 years thus far. We successfully used microRNA (miRNA)-mediated post-transcriptional detargeting for the first time to restrict therapeutic rAAV expression in the central nervous system (CNS) and minimize potentially deleterious effects of transgene overexpression in peripheral tissues. rAAV treatment globally improved CNS myelination, although some abnormalities persisted in the content and distribution of myelin-specific and -enriched lipids. We demonstrate that systemically delivered and CNS-restricted rAAVs can serve as efficacious and sustained gene therapeutics in a model of a severe neurodegenerative disorder even when administered as late as P20. PMID:23817205
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Ibrahim, Nurul ‘Izzah; Mohamed, Norazlina; Soelaiman, Ima Nirwana; Shuid, Ahmad Nazrun
2015-01-01
Osteoporotic drugs are used to prevent fragility fractures, but their role in fracture healing still remains unknown. Thus, alternative agents with suitable mode of delivery are needed to promote fracture healing. This study was performed to investigate the effects of direct deliveries of lovastatin and tocotrienol to fracture sites on ossification-related gene expression in fracture healing in a postmenopausal osteoporosis model. Forty-eight Sprague Dawley female rats were divided into six groups. Group I comprised the sham-operated rats, while Groups II–VI were ovariectomized rats. After 8 weeks, the right tibiae of all rats were fractured and stabilized. Group I and Group II were given two single injections of lovastatin and tocotrienol carriers. Group III was given an estrogen preparation at 64.5 µg/kg daily via oral gavages. Group IV was injected with lovastatin particles (750 µg/kg), while Group V was injected with tocotrienol particles (60 mg/kg). Group VI received two single injections of 750 µg/kg lovastatin particles and 60 mg/kg tocotrienol particles. After 4 weeks, the gene expressions were measured. Group VI showed significantly higher gene expressions of osteocalcin, BMP-2, VEGF-α, and RUNX-2 compared to Group II. In conclusion, combined treatment of lovastatin and tocotrienol upregulated the expression of genes related to fracture healing. PMID:26501302
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Ibrahim, Nurul 'Izzah; Mohamed, Norazlina; Soelaiman, Ima Nirwana; Shuid, Ahmad Nazrun
2015-10-16
Osteoporotic drugs are used to prevent fragility fractures, but their role in fracture healing still remains unknown. Thus, alternative agents with suitable mode of delivery are needed to promote fracture healing. This study was performed to investigate the effects of direct deliveries of lovastatin and tocotrienol to fracture sites on ossification-related gene expression in fracture healing in a postmenopausal osteoporosis model. Forty-eight Sprague Dawley female rats were divided into six groups. Group I comprised the sham-operated rats, while Groups II-VI were ovariectomized rats. After 8 weeks, the right tibiae of all rats were fractured and stabilized. Group I and Group II were given two single injections of lovastatin and tocotrienol carriers. Group III was given an estrogen preparation at 64.5 µg/kg daily via oral gavages. Group IV was injected with lovastatin particles (750 µg/kg), while Group V was injected with tocotrienol particles (60 mg/kg). Group VI received two single injections of 750 µg/kg lovastatin particles and 60 mg/kg tocotrienol particles. After 4 weeks, the gene expressions were measured. Group VI showed significantly higher gene expressions of osteocalcin, BMP-2, VEGF-α, and RUNX-2 compared to Group II. In conclusion, combined treatment of lovastatin and tocotrienol upregulated the expression of genes related to fracture healing.
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Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb
2016-01-01
Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.
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Risk Classification with an Adaptive Naive Bayes Kernel Machine Model.
Minnier, Jessica; Yuan, Ming; Liu, Jun S; Cai, Tianxi
2015-04-22
Genetic studies of complex traits have uncovered only a small number of risk markers explaining a small fraction of heritability and adding little improvement to disease risk prediction. Standard single marker methods may lack power in selecting informative markers or estimating effects. Most existing methods also typically do not account for non-linearity. Identifying markers with weak signals and estimating their joint effects among many non-informative markers remains challenging. One potential approach is to group markers based on biological knowledge such as gene structure. If markers in a group tend to have similar effects, proper usage of the group structure could improve power and efficiency in estimation. We propose a two-stage method relating markers to disease risk by taking advantage of known gene-set structures. Imposing a naive bayes kernel machine (KM) model, we estimate gene-set specific risk models that relate each gene-set to the outcome in stage I. The KM framework efficiently models potentially non-linear effects of predictors without requiring explicit specification of functional forms. In stage II, we aggregate information across gene-sets via a regularization procedure. Estimation and computational efficiency is further improved with kernel principle component analysis. Asymptotic results for model estimation and gene set selection are derived and numerical studies suggest that the proposed procedure could outperform existing procedures for constructing genetic risk models.
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Petunia, Your Next Supermodel?
Vandenbussche, Michiel; Chambrier, Pierre; Rodrigues Bento, Suzanne; Morel, Patrice
2016-01-01
Plant biology in general, and plant evo–devo in particular would strongly benefit from a broader range of available model systems. In recent years, technological advances have facilitated the analysis and comparison of individual gene functions in multiple species, representing now a fairly wide taxonomic range of the plant kingdom. Because genes are embedded in gene networks, studying evolution of gene function ultimately should be put in the context of studying the evolution of entire gene networks, since changes in the function of a single gene will normally go together with further changes in its network environment. For this reason, plant comparative biology/evo–devo will require the availability of a defined set of ‘super’ models occupying key taxonomic positions, in which performing gene functional analysis and testing genetic interactions ideally is as straightforward as, e.g., in Arabidopsis. Here we review why petunia has the potential to become one of these future supermodels, as a representative of the Asterid clade. We will first detail its intrinsic qualities as a model system. Next, we highlight how the revolution in sequencing technologies will now finally allows exploitation of the petunia system to its full potential, despite that petunia has already a long history as a model in plant molecular biology and genetics. We conclude with a series of arguments in favor of a more diversified multi-model approach in plant biology, and we point out where the petunia model system may further play a role, based on its biological features and molecular toolkit. PMID:26870078
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Cochain, Clément; Vafadarnejad, Ehsan; Arampatzi, Panagiota; Jaroslav, Pelisek; Winkels, Holger; Ley, Klaus; Wolf, Dennis; Saliba, Antoine-Emmanuel; Zernecke, Alma
2018-03-15
Rationale: It is assumed that atherosclerotic arteries contain several macrophage subsets endowed with specific functions. The precise identity of these subsets is poorly characterized as they ha ve been defined by the expression of a restricted number of markers. Objective: We have applied single-cell RNA-seq as an unbiased profiling strategy to interrogate and classify aortic macrophage heterogeneity at the single-cell level in atherosclerosis. Methods and Results: We performed single-cell RNA sequencing of total aortic CD45 + cells extracted from the non-diseased (chow fed) and atherosclerotic (11 weeks of high fat diet) aorta of Ldlr -/- mice. Unsupervised clustering singled out 13 distinct aortic cell clusters. Among the myeloid cell populations, Resident-like macrophages with a gene expression profile similar to aortic resident macrophages were found in healthy and diseased aortae, whereas monocytes, monocyte-derived dendritic cells (MoDC), and two populations of macrophages were almost exclusively detectable in atherosclerotic aortae, comprising Inflammatory macrophages showing enrichment in I l1b , and previously undescribed TREM2 hi macrophages. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these three macrophage subsets and MoDC, and uncovered putative functions of each cell type. Notably, TREM2 hi macrophages appeared to be endowed with specialized functions in lipid metabolism and catabolism, and presented a gene expression signature reminiscent of osteoclasts, suggesting a role in lesion calcification. TREM2 expression was moreover detected in human lesional macrophages. Importantly, these macrophage populations were present also in advanced atherosclerosis and in Apoe -/- aortae, indicating relevance of our findings in different stages of atherosclerosis and mouse models. Conclusions: These data unprecedentedly uncovered the transcriptional landscape and phenotypic heterogeneity of aortic macrophages and MoDCs in atherosclerotic and identified previously unrecognized macrophage populations and their gene expression signature, suggesting specialized functions. Our findings will open up novel opportunities to explore distinct myeloid cell populations and their functions in atherosclerosis.
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Comparison of the theoretical and real-world evolutionary potential of a genetic circuit
NASA Astrophysics Data System (ADS)
Razo-Mejia, M.; Boedicker, J. Q.; Jones, D.; DeLuna, A.; Kinney, J. B.; Phillips, R.
2014-04-01
With the development of next-generation sequencing technologies, many large scale experimental efforts aim to map genotypic variability among individuals. This natural variability in populations fuels many fundamental biological processes, ranging from evolutionary adaptation and speciation to the spread of genetic diseases and drug resistance. An interesting and important component of this variability is present within the regulatory regions of genes. As these regions evolve, accumulated mutations lead to modulation of gene expression, which may have consequences for the phenotype. A simple model system where the link between genetic variability, gene regulation and function can be studied in detail is missing. In this article we develop a model to explore how the sequence of the wild-type lac promoter dictates the fold-change in gene expression. The model combines single-base pair resolution maps of transcription factor and RNA polymerase binding energies with a comprehensive thermodynamic model of gene regulation. The model was validated by predicting and then measuring the variability of lac operon regulation in a collection of natural isolates. We then implement the model to analyze the sensitivity of the promoter sequence to the regulatory output, and predict the potential for regulation to evolve due to point mutations in the promoter region.
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Regan, Kelly; Wang, Kanix; Doughty, Emily; Li, Haiquan; Li, Jianrong; Lee, Younghee; Kann, Maricel G
2012-01-01
Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics. PMID:22319180
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The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice1[OPEN
Lan, Liu; Wang, Hongze; Xu, Yuancheng; Yang, Xiaohong; Li, Wenqiang; Tong, Hao; Xiao, Yingjie; Pan, Qingchun; Qiao, Feng; Raihan, Mohammad Sharif; Liu, Haijun; Yang, Ning; Wang, Xiaqing; Deng, Min; Jin, Minliang; Zhao, Lijun; Luo, Xin; Zhan, Wei; Liu, Nannan; Wang, Hong; Chen, Gengshen
2017-01-01
Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis. PMID:28811335
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The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice.
Liu, Jie; Huang, Juan; Guo, Huan; Lan, Liu; Wang, Hongze; Xu, Yuancheng; Yang, Xiaohong; Li, Wenqiang; Tong, Hao; Xiao, Yingjie; Pan, Qingchun; Qiao, Feng; Raihan, Mohammad Sharif; Liu, Haijun; Zhang, Xuehai; Yang, Ning; Wang, Xiaqing; Deng, Min; Jin, Minliang; Zhao, Lijun; Luo, Xin; Zhou, Yang; Li, Xiang; Zhan, Wei; Liu, Nannan; Wang, Hong; Chen, Gengshen; Li, Qing; Yan, Jianbing
2017-10-01
Maize ( Zea mays ) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice ( Oryza sativa ) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1 , a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis ( Arabidopsis thaliana ) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1 ). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis. © 2017 American Society of Plant Biologists. All Rights Reserved.
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Talkowski, Michael E.; Rosenfeld, Jill A.; Blumenthal, Ian; Pillalamarri, Vamsee; Chiang, Colby; Heilbut, Adrian; Ernst, Carl; Hanscom, Carrie; Rossin, Elizabeth; Lindgren, Amelia; Pereira, Shahrin; Ruderfer, Douglas; Kirby, Andrew; Ripke, Stephan; Harris, David; Lee, Ji-Hyun; Ha, Kyungsoo; Kim, Hyung-Goo; Solomon, Benjamin D.; Gropman, Andrea L.; Lucente, Diane; Sims, Katherine; Ohsumi, Toshiro K.; Borowsky, Mark L.; Loranger, Stephanie; Quade, Bradley; Lage, Kasper; Miles, Judith; Wu, Bai-Lin; Shen, Yiping; Neale, Benjamin; Shaffer, Lisa G.; Daly, Mark J.; Morton, Cynthia C.; Gusella, James F.
2012-01-01
SUMMARY Balanced chromosomal abnormalities (BCAs) represent a reservoir of single gene disruptions in neurodevelopmental disorders (NDD). We sequenced BCAs in autism and related NDDs, revealing disruption of 33 loci in four general categories: 1) genes associated with abnormal neurodevelopment (e.g., AUTS2, FOXP1, CDKL5), 2) single gene contributors to microdeletion syndromes (MBD5, SATB2, EHMT1, SNURF-SNRPN), 3) novel risk loci (e.g., CHD8, KIRREL3, ZNF507), and 4) genes associated with later onset psychiatric disorders (e.g., TCF4, ZNF804A, PDE10A, GRIN2B, ANK3). We also discovered profoundly increased burden of copy number variants among 19,556 neurodevelopmental cases compared to 13,991 controls (p = 2.07×10−47) and enrichment of polygenic risk alleles from autism and schizophrenia genome-wide association studies (p = 0.0018 and 0.0009, respectively). Our findings suggest a polygenic risk model of autism incorporating loci of strong effect and indicate that some neurodevelopmental genes are sensitive to perturbation by multiple mutational mechanisms, leading to variable phenotypic outcomes that manifest at different life stages. PMID:22521361
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Jia, Peilin; Zhao, Zhongming
2014-01-01
A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data. PMID:24516372
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Jia, Peilin; Zhao, Zhongming
2014-02-01
A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.
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Li, Qian-Nan; Guo, Lei; Hou, Yi; Ou, Xiang-Hong; Liu, Zhonghua; Sun, Qing-Yuan
2018-06-22
Polycystic ovary syndrome (PCOS), a familial aggregation disease that causes anovulation in women, has well-recognised characteristics, two of which are hyperinsulinaemia and hyperandrogenaemia. To determine whether the DNA methylation status is altered in oocytes by high insulin and androgen levels, we generated a mouse model with hyperinsulinaemia and hyperandrogenaemia by injection of insulin and human chorionic gonadotrophin and investigated DNA methylation changes through single-cell level whole genome bisulphite sequencing. Our results showed that hyperinsulinaemia and hyperandrogenaemia had no significant effects on the global DNA methylation profile and different functional regions of genes, but did alter methylation status of some genes, which were significantly enriched in 17 gene ontology (GO) terms (P<0.05) by GO analysis. Among differently methylated genes, some were related to the occurrence of PCOS. Based on our results, we suggest that hyperinsulinaemia and hyperandrogenaemia may cause changes in some DNA methylation loci in oocytes.
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Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy
Young, Jonathan W; Locke, James C W; Altinok, Alphan; Rosenfeld, Nitzan; Bacarian, Tigran; Swain, Peter S; Mjolsness, Eric; Elowitz, Michael B
2014-01-01
Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1–2 d for progressing through the analysis procedure. PMID:22179594
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Bayesian Networks Predict Neuronal Transdifferentiation.
Ainsworth, Richard I; Ai, Rizi; Ding, Bo; Li, Nan; Zhang, Kai; Wang, Wei
2018-05-30
We employ the language of Bayesian networks to systematically construct gene-regulation topologies from deep-sequencing single-nucleus RNA-Seq data for human neurons. From the perspective of the cell-state potential landscape, we identify attractors that correspond closely to different neuron subtypes. Attractors are also recovered for cell states from an independent data set confirming our models accurate description of global genetic regulations across differing cell types of the neocortex (not included in the training data). Our model recovers experimentally confirmed genetic regulations and community analysis reveals genetic associations in common pathways. Via a comprehensive scan of all theoretical three-gene perturbations of gene knockout and overexpression, we discover novel neuronal trans-differrentiation recipes (including perturbations of SATB2, GAD1, POU6F2 and ADARB2) for excitatory projection neuron and inhibitory interneuron subtypes. Copyright © 2018, G3: Genes, Genomes, Genetics.
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CRISPR/Cas9-mediated gene targeting in Arabidopsis using sequential transformation.
Miki, Daisuke; Zhang, Wenxin; Zeng, Wenjie; Feng, Zhengyan; Zhu, Jian-Kang
2018-05-17
Homologous recombination-based gene targeting is a powerful tool for precise genome modification and has been widely used in organisms ranging from yeast to higher organisms such as Drosophila and mouse. However, gene targeting in higher plants, including the most widely used model plant Arabidopsis thaliana, remains challenging. Here we report a sequential transformation method for gene targeting in Arabidopsis. We find that parental lines expressing the bacterial endonuclease Cas9 from the egg cell- and early embryo-specific DD45 gene promoter can improve the frequency of single-guide RNA-targeted gene knock-ins and sequence replacements via homologous recombination at several endogenous sites in the Arabidopsis genome. These heritable gene targeting can be identified by regular PCR. Our approach enables routine and fine manipulation of the Arabidopsis genome.
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Jobst-Schwan, Tilman; Schmidt, Johanna Magdalena; Schneider, Ronen; Hoogstraten, Charlotte A; Ullmann, Jeremy F P; Schapiro, David; Majmundar, Amar J; Kolb, Amy; Eddy, Kaitlyn; Shril, Shirlee; Braun, Daniela A; Poduri, Annapurna; Hildebrandt, Friedhelm
2018-01-01
Until recently, morpholino oligonucleotides have been widely employed in zebrafish as an acute and efficient loss-of-function assay. However, off-target effects and reproducibility issues when compared to stable knockout lines have compromised their further use. Here we employed an acute CRISPR/Cas approach using multiple single guide RNAs targeting simultaneously different positions in two exemplar genes (osgep or tprkb) to increase the likelihood of generating mutations on both alleles in the injected F0 generation and to achieve a similar effect as morpholinos but with the reproducibility of stable lines. This multi single guide RNA approach resulted in median likelihoods for at least one mutation on each allele of >99% and sgRNA specific insertion/deletion profiles as revealed by deep-sequencing. Immunoblot showed a significant reduction for Osgep and Tprkb proteins. For both genes, the acute multi-sgRNA knockout recapitulated the microcephaly phenotype and reduction in survival that we observed previously in stable knockout lines, though milder in the acute multi-sgRNA knockout. Finally, we quantify the degree of mutagenesis by deep sequencing, and provide a mathematical model to quantitate the chance for a biallelic loss-of-function mutation. Our findings can be generalized to acute and stable CRISPR/Cas targeting for any zebrafish gene of interest.
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Single and multiple phenotype QTL analyses of downy mildew resistance in interspecific grapevines.
Divilov, Konstantin; Barba, Paola; Cadle-Davidson, Lance; Reisch, Bruce I
2018-05-01
Downy mildew resistance across days post-inoculation, experiments, and years in two interspecific grapevine F 1 families was investigated using linear mixed models and Bayesian networks, and five new QTL were identified. Breeding grapevines for downy mildew disease resistance has traditionally relied on qualitative gene resistance, which can be overcome by pathogen evolution. Analyzing two interspecific F 1 families, both having ancestry derived from Vitis vinifera and wild North American Vitis species, across 2 years and multiple experiments, we found multiple loci associated with downy mildew sporulation and hypersensitive response in both families using a single phenotype model. The loci explained between 7 and 17% of the variance for either phenotype, suggesting a complex genetic architecture for these traits in the two families studied. For two loci, we used RNA-Seq to detect differentially transcribed genes and found that the candidate genes at these loci were likely not NBS-LRR genes. Additionally, using a multiple phenotype Bayesian network analysis, we found effects between the leaf trichome density, hypersensitive response, and sporulation phenotypes. Moderate-high heritabilities were found for all three phenotypes, suggesting that selection for downy mildew resistance is an achievable goal by breeding for either physical- or non-physical-based resistance mechanisms, with the combination of the two possibly providing durable resistance.
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Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong
2015-01-01
Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.
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Seok, Junhee; Davis, Ronald W.; Xiao, Wenzhong
2015-01-01
Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. PMID:25933378
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Diopere, Eveline; Hellemans, Bart; Volckaert, Filip A M; Maes, Gregory E
2013-03-01
Genomic methodologies applied in evolutionary and fisheries research have been of great benefit to understand the marine ecosystem and the management of natural resources. Although single nucleotide polymorphisms (SNPs) are attractive for the study of local adaptation, spatial stock management and traceability, and investigating the effects of fisheries-induced selection, they have rarely been exploited in non-model organisms. This is partly due to difficulties in finding and validating SNPs in species with limited or no genomic resources. Complementary to random genome-scan approaches, a targeted candidate gene approach has the potential to unveil pre-selected functional diversity and provides more in depth information on the action of selection at specific genes. For example genes can be under selective pressure due to climate change and sustained periods of heavy fishing pressure. In this study, we applied a candidate gene approach in sole (Solea solea L.), an important member of the demersal ecosystem. As consumption flatfish it is heavy exploited and has experienced associated life-history changes over the last 60years. To discover novel genetic polymorphisms in or around genes linked to important life history traits in sole, we screened a total of 76 candidate genes related to growth and maturation using a targeted resequencing approach. We identified in total 86 putative SNPs in 22 genes and validated 29 SNPs using a multiplex single-base extension genotyping assay. We found 22 informative SNPs, of which two represent non-synonymous mutations, potentially of functional relevance. These novel markers should be rapidly and broadly applicable in analyses of natural sole populations, as a measure of the evolutionary signature of overfishing and for initiatives on marker assisted selection. Copyright © 2012 Elsevier B.V. All rights reserved.
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Ruhlman, Tracey A; Zhang, Jin; Blazier, John C; Sabir, Jamal S M; Jansen, Robert K
2017-04-01
There is a misinterpretation in the literature regarding the variable orientation of the small single copy region of plastid genomes (plastomes). The common phenomenon of small and large single copy inversion, hypothesized to occur through intramolecular recombination between inverted repeats (IR) in a circular, single unit-genome, in fact, more likely occurs through recombination-dependent replication (RDR) of linear plastome templates. If RDR can be primed through both intra- and intermolecular recombination, then this mechanism could not only create inversion isomers of so-called single copy regions, but also an array of alternative sequence arrangements. We used Illumina paired-end and PacBio single-molecule real-time (SMRT) sequences to characterize repeat structure in the plastome of Monsonia emarginata (Geraniaceae). We used OrgConv and inspected nucleotide alignments to infer ancestral nucleotides and identify gene conversion among repeats and mapped long (>1 kb) SMRT reads against the unit-genome assembly to identify alternative sequence arrangements. Although M. emarginata lacks the canonical IR, we found that large repeats (>1 kilobase; kb) represent ∼22% of the plastome nucleotide content. Among the largest repeats (>2 kb), we identified GC-biased gene conversion and mapping filtered, long SMRT reads to the M. emarginata unit-genome assembly revealed alternative, substoichiometric sequence arrangements. We offer a model based on RDR and gene conversion between long repeated sequences in the M. emarginata plastome and provide support that both intra-and intermolecular recombination between large repeats, particularly in repeat-rich plastomes, varies unit-genome structure while homogenizing the nucleotide sequence of repeats. © 2017 Botanical Society of America.
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Zhang, Shuo; Ji, Guofa; Liang, Yiqian; Zhang, Rui; Shi, Puyu; Guo, Dangshe; Li, Chunqi; Feng, Jing; Liu, Feng; Peng, Rong; Chen, Mingwei
2017-01-06
The role of telomere in genomic stability is an established fact. Variation in leukocyte telomere length (LTL) has been considered a crucial factor that associated with age-associated diseases. To elucidate the association between LTL variation and ischemic stroke (IS) risk, we selected ten single nucleotide polymorphisms (SNPs) in three genes (TERC, TERT and RTEL1) that previously reported link to LTL, and genotyped SNPs of these genes in a case-control study. The association between polymorphisms and IS risk were tested by Chi squared test and haplotype analysis. In allele association analysis, allele "C" in rs10936599 of TERC gene and allele "G" in rs2853677 of TERT gene were found to have an increased risk of IS when compared with allele "T" and "A", respectively. Model association analysis showed that genotype "G/A" in the overdominant model and genotypes "G/A" and "A/A" in the dominant model of rs2242652 presented a more likelihood to have IS. Another TERT locus (rs2853677) with genotype "G" was also found IS-related risky in the log-additive model. Taken together, our results suggest a potential association between LTL related TERC, TERT gene variants and ischemic stroke risk.
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Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models
Davis, Brian W.; Seabury, Christopher M.; Brashear, Wesley A.; Li, Gang; Roelke-Parker, Melody; Murphy, William J.
2015-01-01
The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented “rule of speciation.” We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the “Large X-Effect” in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation. PMID:26006188
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Klingler, John; Creasy, Robert; Gao, Lingling; Nair, Ramakrishnan M.; Calix, Alonso Suazo; Jacob, Helen Spafford; Edwards, Owain R.; Singh, Karam B.
2005-01-01
Aphids and related insects feed from a single cell type in plants: the phloem sieve element. Genetic resistance to Acyrthosiphon kondoi Shinji (bluegreen aphid or blue alfalfa aphid) has been identified in Medicago truncatula Gaert. (barrel medic) and backcrossed into susceptible cultivars. The status of M. truncatula as a model legume allows an in-depth study of defense against this aphid at physiological, biochemical, and molecular levels. In this study, two closely related resistant and susceptible genotypes were used to characterize the aphid-resistance phenotype. Resistance conditions antixenosis since migratory aphids were deterred from settling on resistant plants within 6 h of release, preferring to settle on susceptible plants. Analysis of feeding behavior revealed the trait affects A. kondoi at the level of the phloem sieve element. Aphid reproduction on excised shoots demonstrated that resistance requires an intact plant. Antibiosis against A. kondoi is enhanced by prior infestation, indicating induction of this phloem-specific defense. Resistance segregates as a single dominant gene, AKR (Acyrthosiphon kondoi resistance), in two mapping populations, which have been used to map the locus to a region flanked by resistance gene analogs predicted to encode the CC-NBS-LRR subfamily of resistance proteins. This work provides the basis for future molecular analysis of defense against phloem parasitism in a plant model system. PMID:15778464
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Reverse engineering gene regulatory networks from measurement with missing values.
Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong
2016-12-01
Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.
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Du, Qingzhang; Tian, Jiaxing; Yang, Xiaohui; Pan, Wei; Xu, Baohua; Li, Bailian; Ingvarsson, Pär K.; Zhang, Deqiang
2015-01-01
Economically important traits in many species generally show polygenic, quantitative inheritance. The components of genetic variation (additive, dominant and epistatic effects) of these traits conferred by multiple genes in shared biological pathways remain to be defined. Here, we investigated 11 full-length genes in cellulose biosynthesis, on 10 growth and wood-property traits, within a population of 460 unrelated Populus tomentosa individuals, via multi-gene association. To validate positive associations, we conducted single-marker analysis in a linkage population of 1,200 individuals. We identified 118, 121, and 43 associations (P< 0.01) corresponding to additive, dominant, and epistatic effects, respectively, with low to moderate proportions of phenotypic variance (R2). Epistatic interaction models uncovered a combination of three non-synonymous sites from three unique genes, representing a significant epistasis for diameter at breast height and stem volume. Single-marker analysis validated 61 associations (false discovery rate, Q ≤ 0.10), representing 38 SNPs from nine genes, and its average effect (R2 = 3.8%) nearly 2-fold higher than that identified with multi-gene association, suggesting that multi-gene association can capture smaller individual variants. Moreover, a structural gene–gene network based on tissue-specific transcript abundances provides a better understanding of the multi-gene pathway affecting tree growth and lignocellulose biosynthesis. Our study highlights the importance of pathway-based multiple gene associations to uncover the nature of genetic variance for quantitative traits and may drive novel progress in molecular breeding. PMID:25428896
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Gene transfer and gene mapping in mammalian cells in culture.
Shows, T B; Sakaguchi, A Y
1980-01-01
The ability to transfer mammalian genes parasexually has opened new possibilities for gene mapping and fine structure mapping and offers great potential for contributing to several aspects of mammalian biology, including gene expression and genetic engineering. The DNA transferred has ranged from whole genomes to single genes and smaller segments of DNA. The transfer of whole genomes by cell fusion forms cell hybrids, which has promoted the extensive mapping of human and mouse genes. Transfer, by cell fusion, of rearranged chromosomes has contributed significantly to determining close linkage and the assignment of genes to specific chromosomal regions. Transfer of single chromosomes has been achieved utilizing microcells fused to recipient cells. Metaphase chromosomes have been isolated and used to transfer single-to-multigenic DNA segments. DNA-mediated gene transfer, simulating bacterial transformation, has achieved transfer of single-copy genes. By utilizing DNA cleaved with restriction endonucleases, gene transfer is being empolyed as a bioassay for the purification of genes. Gene mapping and the fate of transferred genes can be examined now at the molecular level using sequence-specific probles. Recently, single genes have been cloned into eucaryotic and procaryotic vectors for transfer into mammalian cells. Moreover, recombinant libraries in which entire mammalian genomes are represented collectively are a rich new source of transferable genes. Methodology for transferring mammalian genetic information and applications for mapping mammalian genes is presented and prospects for the future discussed.
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Dominant resistance against plant viruses
de Ronde, Dryas; Butterbach, Patrick; Kormelink, Richard
2014-01-01
To establish a successful infection plant viruses have to overcome a defense system composed of several layers. This review will overview the various strategies plants employ to combat viral infections with main emphasis on the current status of single dominant resistance (R) genes identified against plant viruses and the corresponding avirulence (Avr) genes identified so far. The most common models to explain the mode of action of dominant R genes will be presented. Finally, in brief the hypersensitive response (HR) and extreme resistance (ER), and the functional and structural similarity of R genes to sensors of innate immunity in mammalian cell systems will be described. PMID:25018765
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Improved maize reference genome with single-molecule technologies.
Jiao, Yinping; Peluso, Paul; Shi, Jinghua; Liang, Tiffany; Stitzer, Michelle C; Wang, Bo; Campbell, Michael S; Stein, Joshua C; Wei, Xuehong; Chin, Chen-Shan; Guill, Katherine; Regulski, Michael; Kumari, Sunita; Olson, Andrew; Gent, Jonathan; Schneider, Kevin L; Wolfgruber, Thomas K; May, Michael R; Springer, Nathan M; Antoniou, Eric; McCombie, W Richard; Presting, Gernot G; McMullen, Michael; Ross-Ibarra, Jeffrey; Dawe, R Kelly; Hastie, Alex; Rank, David R; Ware, Doreen
2017-06-22
Complete and accurate reference genomes and annotations provide fundamental tools for characterization of genetic and functional variation. These resources facilitate the determination of biological processes and support translation of research findings into improved and sustainable agricultural technologies. Many reference genomes for crop plants have been generated over the past decade, but these genomes are often fragmented and missing complex repeat regions. Here we report the assembly and annotation of a reference genome of maize, a genetic and agricultural model species, using single-molecule real-time sequencing and high-resolution optical mapping. Relative to the previous reference genome, our assembly features a 52-fold increase in contig length and notable improvements in the assembly of intergenic spaces and centromeres. Characterization of the repetitive portion of the genome revealed more than 130,000 intact transposable elements, allowing us to identify transposable element lineage expansions that are unique to maize. Gene annotations were updated using 111,000 full-length transcripts obtained by single-molecule real-time sequencing. In addition, comparative optical mapping of two other inbred maize lines revealed a prevalence of deletions in regions of low gene density and maize lineage-specific genes.
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Plessy, Charles; Desbois, Linda; Fujii, Teruo; Carninci, Piero
2013-02-01
Tissues contain complex populations of cells. Like countries, which are comprised of mixed populations of people, tissues are not homogeneous. Gene expression studies that analyze entire populations of cells from tissues as a mixture are blind to this diversity. Thus, critical information is lost when studying samples rich in specialized but diverse cells such as tumors, iPS colonies, or brain tissue. High throughput methods are needed to address, model and understand the constitutive and stochastic differences between individual cells. Here, we describe microfluidics technologies that utilize a combination of molecular biology and miniaturized labs on chips to study gene expression at the single cell level. We discuss how the characterization of the transcriptome of each cell in a sample will open a new field in gene expression analysis, population transcriptomics, that will change the academic and biomedical analysis of complex samples by defining them as quantified populations of single cells. Copyright © 2013 WILEY Periodicals, Inc.
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Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters
NASA Astrophysics Data System (ADS)
Munsky, Brian; Trinh, Brooke; Khammash, Mustafa
2010-03-01
The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.
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Deciphering the Minimal Algorithm for Development and Information-genesis
NASA Astrophysics Data System (ADS)
Li, Zhiyuan; Tang, Chao; Li, Hao
During development, cells with identical genomes acquires different fates in a highly organized manner. In order to decipher the principles underlining development, we used C.elegans as the model organism. Based on a large set of microscopy imaging, we first constructed a ``standard worm'' in silico: from the single zygotic cell to about 500 cell stage, the lineage, position, cell-cell contact and gene expression dynamics are quantified for each cell in order to investigate principles underlining these intensive data. Next, we reverse-engineered the possible gene-gene/cell-cell interaction rules that are capable of running a dynamic model recapitulating the early fate decisions during C.elegans development. we further formulized the C.elegans embryogenesis in the language of information genesis. Analysis towards data and model uncovered the global landscape of development in the cell fate space, suggested possible gene regulatory architectures and cell signaling processes, revealed diversity and robustness as the essential trade-offs in development, and demonstrated general strategies in building multicellular organisms.
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Satellite cell proliferation in adult skeletal muscle
NASA Technical Reports Server (NTRS)
Morrison, Paul R. (Inventor); Thomason, Donald B. (Inventor); Stancel, George M. (Inventor); Booth, Frank W. (Inventor)
1995-01-01
Novel methods of retroviral-mediated gene transfer for the in vivo corporation and stable expression of eukaryotic or prokaryotic foreign genes in tissues of living animals is described. More specifically, methods of incorporating foreign genes into mitotically active cells are disclosed. The constitutive and stable expression of E. coli .beta.-galactosidase gene under the promoter control of the Moloney murine leukemia virus long terminal repeat is employed as a particularly preferred embodiment, by way of example, establishes the model upon which the incorporation of a foreign gene into a mitotically-active living eukaryotic tissue is based. Use of the described methods in therapeutic treatments for genetic diseases, such as those muscular degenerative diseases, is also presented. In muscle tissue, the described processes result in genetically-altered satellite cells which proliferate daughter myoblasts which preferentially fuse to form a single undamaged muscle fiber replacing damaged muscle tissue in a treated animal. The retroviral vector, by way of example, includes a dystrophin gene construct for use in treating muscular dystrophy. The present invention also comprises an experimental model utilizable in the study of the physiological regulation of skeletal muscle gene expression in intact animals.
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Effect of promoter architecture on the cell-to-cell variability in gene expression.
Sanchez, Alvaro; Garcia, Hernan G; Jones, Daniel; Phillips, Rob; Kondev, Jané
2011-03-01
According to recent experimental evidence, promoter architecture, defined by the number, strength and regulatory role of the operators that control transcription, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect variability in gene expression in a systematic rather than case-by-case fashion. In this article we make such a systematic investigation, based on a microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcriptional output from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can be used to test kinetic models of gene regulation. The emphasis of the discussion is on prokaryotic gene regulation, but our analysis can be extended to eukaryotic cells as well.
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Effect of Promoter Architecture on the Cell-to-Cell Variability in Gene Expression
Sanchez, Alvaro; Garcia, Hernan G.; Jones, Daniel; Phillips, Rob; Kondev, Jané
2011-01-01
According to recent experimental evidence, promoter architecture, defined by the number, strength and regulatory role of the operators that control transcription, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect variability in gene expression in a systematic rather than case-by-case fashion. In this article we make such a systematic investigation, based on a microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcriptional output from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can be used to test kinetic models of gene regulation. The emphasis of the discussion is on prokaryotic gene regulation, but our analysis can be extended to eukaryotic cells as well. PMID:21390269
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High-LET Patterns of DSBs in DNA Loops, the HPRT Gene and Phosphorylation Foci
NASA Technical Reports Server (NTRS)
Ponomarev, Artem L.; Huff, Janice L.; Cucinotta, Francis A.
2007-01-01
We present new results obtained with our model based on the track structure and chromatin geometry that predicts the DSB spatial and genomic distributions in a cell nucleus with the full genome represented. The model generates stochastic patterns of DSBs in the physical space of the nucleus filled with the realistic configuration of human chromosomes. The model was re-used to find the distribution of DSBs in a physical volume corresponding to a visible phosphorylation focus believed to be associated with a DSB. The data shows whether there must more than one DSB per foci due to finite size of the visible focus, even if a single DSB is radiochemically responsible for the phosphorylation of DNA in its vicinity. The same model can predict patterns of closely located DSBs in a given gene, or in a DNA loop, one of the large-scale chromatin structures. We demonstrated for the example of the HPRT gene, how different sorts of radiation lead to proximity effect in DSB locations, which is important for modeling gene deletions. The spectrum of intron deletions and total gene deletions was simulated for the HPRT gene. The same proximity effect of DSBs in a loop can hinder DSB restitutions, as parts of the loop between DSBs is deleted with a higher likelihood. The distributions of DSBs and deletions of DNA in a loop are presented.
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Behavioral phenotypes of genetic mouse models of autism
Kazdoba, T. M.; Leach, P. T.; Crawley, J. N.
2016-01-01
More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. PMID:26403076
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Jiang, Rong; French, John E.; Stober, Vandy P.; Kang-Sickel, Juei-Chuan C.; Zou, Fei
2012-01-01
Background: Individual genetic variation that results in differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models. Objective: We developed a strategy to investigate individual differences in single-nucleotide polymorphisms (SNPs) as genetic markers associated with naphthyl–keratin adduct (NKA) levels measured in the skin of workers exposed to naphthalene. Methods: The SNP-association analysis was conducted in PLINK using candidate-gene analysis and genome-wide analysis. We identified significant SNP–NKA associations and investigated the potential impact of these SNPs along with personal and workplace factors on NKA levels using a multiple linear regression model and the Pratt index. Results: In candidate-gene analysis, a SNP (rs4852279) located near the CYP26B1 gene contributed to the 2-naphthyl–keratin adduct (2NKA) level. In the multiple linear regression model, the SNP rs4852279, dermal exposure, exposure time, task replacing foam, age, and ethnicity all were significant predictors of 2NKA level. In genome-wide analysis, no single SNP reached genome-wide significance for NKA levels (all p ≥ 1.05 × 10–5). Pathway and network analyses of SNPs associated with NKA levels were predicted to be involved in the regulation of cellular processes and homeostasis. Conclusions: These results provide evidence that a quantitative biomarker can be used as an intermediate phenotype when investigating the association between genetic markers and exposure–dose relationship in a small, well-characterized exposed worker population. PMID:22391508
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Nakad, Rania; Snoek, L Basten; Yang, Wentao; Ellendt, Sunna; Schneider, Franziska; Mohr, Timm G; Rösingh, Lone; Masche, Anna C; Rosenstiel, Philip C; Dierking, Katja; Kammenga, Jan E; Schulenburg, Hinrich
2016-04-11
The invertebrate immune system comprises physiological mechanisms, physical barriers and also behavioral responses. It is generally related to the vertebrate innate immune system and widely believed to provide nonspecific defense against pathogens, whereby the response to different pathogen types is usually mediated by distinct signalling cascades. Recent work suggests that invertebrate immune defense can be more specific at least at the phenotypic level. The underlying genetic mechanisms are as yet poorly understood. We demonstrate in the model invertebrate Caenorhabditis elegans that a single gene, a homolog of the mammalian neuropeptide Y receptor gene, npr-1, mediates contrasting defense phenotypes towards two distinct pathogens, the Gram-positive Bacillus thuringiensis and the Gram-negative Pseudomonas aeruginosa. Our findings are based on combining quantitative trait loci (QTLs) analysis with functional genetic analysis and RNAseq-based transcriptomics. The QTL analysis focused on behavioral immune defense against B. thuringiensis, using recombinant inbred lines (RILs) and introgression lines (ILs). It revealed several defense QTLs, including one on chromosome X comprising the npr-1 gene. The wildtype N2 allele for the latter QTL was associated with reduced defense against B. thuringiensis and thus produced an opposite phenotype to that previously reported for the N2 npr-1 allele against P. aeruginosa. Analysis of npr-1 mutants confirmed these contrasting immune phenotypes for both avoidance behavior and nematode survival. Subsequent transcriptional profiling of C. elegans wildtype and npr-1 mutant suggested that npr-1 mediates defense against both pathogens through p38 MAPK signaling, insulin-like signaling, and C-type lectins. Importantly, increased defense towards P. aeruginosa seems to be additionally influenced through the induction of oxidative stress genes and activation of GATA transcription factors, while the repression of oxidative stress genes combined with activation of Ebox transcription factors appears to enhance susceptibility to B. thuringiensis. Our findings highlight the role of a single gene, npr-1, in fine-tuning nematode immune defense, showing the ability of the invertebrate immune system to produce highly specialized and potentially opposing immune responses via single regulatory genes.
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Zeng, Shaohua; Liu, Yongliang; Wu, Min; Liu, Xiaomin; Shen, Xiaofei; Liu, Chunzhao; Wang, Ying
2014-01-01
Lycium barbarum and L. ruthenicum are extensively used as traditional Chinese medicinal plants. Next generation sequencing technology provides a powerful tool for analyzing transcriptomic profiles of gene expression in non-model species. Such gene expression can then be confirmed with quantitative real-time polymerase chain reaction (qRT-PCR). Therefore, use of systematically identified suitable reference genes is a prerequisite for obtaining reliable gene expression data. Here, we calculated the expression stability of 18 candidate reference genes across samples from different tissues and grown under salt stress using geNorm and NormFinder procedures. The geNorm-determined rank of reference genes was similar to those defined by NormFinder with some differences. Both procedures confirmed that the single most stable reference gene was ACNTIN1 for L. barbarum fruits, H2B1 for L. barbarum roots, and EF1α for L. ruthenicum fruits. PGK3, H2B2, and PGK3 were identified as the best stable reference genes for salt-treated L. ruthenicum leaves, roots, and stems, respectively. H2B1 and GAPDH1+PGK1 for L. ruthenicum and SAMDC2+H2B1 for L. barbarum were the best single and/or combined reference genes across all samples. Finally, expression of salt-responsive gene NAC, fruit ripening candidate gene LrPG, and anthocyanin genes were investigated to confirm the validity of the selected reference genes. Suitable reference genes identified in this study provide a foundation for accurately assessing gene expression and further better understanding of novel gene function to elucidate molecular mechanisms behind particular biological/physiological processes in Lycium.
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Gene Expression Profiling Predicts the Development of Oral Cancer
Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li
2011-01-01
Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635
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Easi-CRISPR for creating knock-in and conditional knockout mouse models using long ssDNA donors.
Miura, Hiromi; Quadros, Rolen M; Gurumurthy, Channabasavaiah B; Ohtsuka, Masato
2018-01-01
CRISPR/Cas9-based genome editing can easily generate knockout mouse models by disrupting the gene sequence, but its efficiency for creating models that require either insertion of exogenous DNA (knock-in) or replacement of genomic segments is very poor. The majority of mouse models used in research involve knock-in (reporters or recombinases) or gene replacement (e.g., conditional knockout alleles containing exons flanked by LoxP sites). A few methods for creating such models have been reported that use double-stranded DNA as donors, but their efficiency is typically 1-10% and therefore not suitable for routine use. We recently demonstrated that long single-stranded DNAs (ssDNAs) serve as very efficient donors, both for insertion and for gene replacement. We call this method efficient additions with ssDNA inserts-CRISPR (Easi-CRISPR) because it is a highly efficient technology (efficiency is typically 30-60% and reaches as high as 100% in some cases). The protocol takes ∼2 months to generate the founder mice.
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Gong, Ping; Nan, Xiaofei; Barker, Natalie D; Boyd, Robert E; Chen, Yixin; Wilkins, Dawn E; Johnson, David R; Suedel, Burton C; Perkins, Edward J
2016-03-08
Chemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation. This refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R(2) = 0.71-0.82 was achievable at a relatively low model complexity with as few as 3-10 predictor genes per model. These results are much more encouraging than our previous ones. This study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings.
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Bernard, Clémence; Vincent, Clémentine; Testa, Damien; Bertini, Eva; Ribot, Jérôme; Di Nardo, Ariel A; Volovitch, Michel; Prochiantz, Alain
2016-05-01
During postnatal life the cerebral cortex passes through critical periods of plasticity allowing its physiological adaptation to the environment. In the visual cortex, critical period onset and closure are influenced by the non-cell autonomous activity of the Otx2 homeoprotein transcription factor, which regulates the maturation of parvalbumin-expressing inhibitory interneurons (PV cells). In adult mice, the maintenance of a non-plastic adult state requires continuous Otx2 import by PV cells. An important source of extra-cortical Otx2 is the choroid plexus, which secretes Otx2 into the cerebrospinal fluid. Otx2 secretion and internalization requires two small peptidic domains that are part of the DNA-binding domain. Thus, mutating these "transfer" sequences also modifies cell autonomous transcription, precluding this approach to obtain a cell autonomous-only mouse. Here, we develop a mouse model with inducible secretion of an anti-Otx2 single-chain antibody to trap Otx2 in the extracellular milieu. Postnatal secretion of this single-chain antibody by PV cells delays PV maturation and reduces plasticity gene expression. Induced adult expression of this single-chain antibody in cerebrospinal fluid decreases Otx2 internalization by PV cells, strongly induces plasticity gene expression and reopens physiological plasticity. We provide the first mammalian genetic evidence for a signaling mechanism involving intercellular transfer of a homeoprotein transcription factor. Our single-chain antibody mouse model is a valid strategy for extracellular neutralization that could be applied to other homeoproteins and signaling molecules within and beyond the nervous system.
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No control genes required: Bayesian analysis of qRT-PCR data.
Matz, Mikhail V; Wright, Rachel M; Scott, James G
2013-01-01
Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.
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Multi-Scale Modeling to Improve Single-Molecule, Single-Cell Experiments
NASA Astrophysics Data System (ADS)
Munsky, Brian; Shepherd, Douglas
2014-03-01
Single-cell, single-molecule experiments are producing an unprecedented amount of data to capture the dynamics of biological systems. When integrated with computational models, observations of spatial, temporal and stochastic fluctuations can yield powerful quantitative insight. We concentrate on experiments that localize and count individual molecules of mRNA. These high precision experiments have large imaging and computational processing costs, and we explore how improved computational analyses can dramatically reduce overall data requirements. In particular, we show how analyses of spatial, temporal and stochastic fluctuations can significantly enhance parameter estimation results for small, noisy data sets. We also show how full probability distribution analyses can constrain parameters with far less data than bulk analyses or statistical moment closures. Finally, we discuss how a systematic modeling progression from simple to more complex analyses can reduce total computational costs by orders of magnitude. We illustrate our approach using single-molecule, spatial mRNA measurements of Interleukin 1-alpha mRNA induction in human THP1 cells following stimulation. Our approach could improve the effectiveness of single-molecule gene regulation analyses for many other process.
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Normal uniform mixture differential gene expression detection for cDNA microarrays
Dean, Nema; Raftery, Adrian E
2005-01-01
Background One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic. Results We propose a simple method, Normal Uniform Differential Gene Expression (NUDGE) detection for finding differentially expressed genes in cDNA microarrays. The method uses a simple univariate normal-uniform mixture model, in combination with new normalization methods for spread as well as mean that extend the lowess normalization of Dudoit, Yang, Callow and Speed (2002) [1]. It takes account of multiple testing, and gives probabilities of differential expression as part of its output. It can be applied to either single-slide or replicated experiments, and it is very fast. Three datasets are analyzed using NUDGE, and the results are compared to those given by other popular methods: unadjusted and Bonferroni-adjusted t tests, Significance Analysis of Microarrays (SAM), and Empirical Bayes for microarrays (EBarrays) with both Gamma-Gamma and Lognormal-Normal models. Conclusion The method gives a high probability of differential expression to genes known/suspected a priori to be differentially expressed and a low probability to the others. In terms of known false positives and false negatives, the method outperforms all multiple-replicate methods except for the Gamma-Gamma EBarrays method to which it offers comparable results with the added advantages of greater simplicity, speed, fewer assumptions and applicability to the single replicate case. An R package called nudge to implement the methods in this paper will be made available soon at . PMID:16011807
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Sork, Victoria L; Squire, Kevin; Gugger, Paul F; Steele, Stephanie E; Levy, Eric D; Eckert, Andrew J
2016-01-01
The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models. The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change. © 2016 Botanical Society of America.
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Single Cell Gene Expression Profiling of Skeletal Muscle-Derived Cells.
Gatto, Sole; Puri, Pier Lorenzo; Malecova, Barbora
2017-01-01
Single cell gene expression profiling is a fundamental tool for studying the heterogeneity of a cell population by addressing the phenotypic and functional characteristics of each cell. Technological advances that have coupled microfluidic technologies with high-throughput quantitative RT-PCR analyses have enabled detailed analyses of single cells in various biological contexts. In this chapter, we describe the procedure for isolating the skeletal muscle interstitial cells termed Fibro-Adipogenic Progenitors (FAPs ) and their gene expression profiling at the single cell level. Moreover, we accompany our bench protocol with bioinformatics analysis designed to process raw data as well as to visualize single cell gene expression data. Single cell gene expression profiling is therefore a useful tool in the investigation of FAPs heterogeneity and their contribution to muscle homeostasis.
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Protein modelling of triterpene synthase genes from mangrove plants using Phyre2 and Swiss-model
NASA Astrophysics Data System (ADS)
Basyuni, M.; Wati, R.; Sulistiyono, N.; Hayati, R.; Sumardi; Oku, H.; Baba, S.; Sagami, H.
2018-03-01
Molecular cloning of five oxidosqualene cyclases (OSC) genes from Bruguiera gymnorrhiza, Kandelia candel, and Rhizophora stylosa had previously been cloned, characterized, and encoded mono and -multi triterpene synthases. The present study analyzed protein modelling of triterpene synthase genes from mangrove using Phyre2 and Swiss-model. The diversity was noted within protein modelling of triterpene synthases using Phyre2 from sequence identity (38-43%) and residue (696-703). RsM2 was distinguishable from others for template structure; it used lanosterol synthase as a template (PDB ID: w6j.1.A). By contrast, other genes used human lanosterol synthase (1w6k.1.A). The predicted bind sites were correlated with the product of triterpene synthase, the product of BgbAS was β-amyrin, while RsM1 contained a significant amount of β-amyrin. Similarly BgLUS and KcMS, both main products was lupeol, on the other hand, RsM2 with the outcome of taraxerol. Homology modelling revealed that 696 residues of BgbAS, BgLUS, RsM1, and RsM2 (91-92% of the amino acid sequence) had been modelled with 100% confidence by the single highest scoring template using Phyre2. This coverage was higher than Swiss-model (85-90%). The present study suggested that molecular cloning of triterpene genes provides useful tools for studying the protein modelling related regulation of isoprenoids biosynthesis in mangrove forests.
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Keebaugh, Alaine C.; Thomas, James W.
2010-01-01
Gene loss has been proposed to play a major role in adaptive evolution, and recent studies are beginning to reveal its importance in human evolution. However, the potential consequence of a single gene-loss event upon the fates of functionally interrelated genes is poorly understood. Here, we use the purine metabolic pathway as a model system in which to explore this important question. The loss of urate oxidase (UOX) activity, a necessary step in this pathway, has occurred independently in the hominoid and bird/reptile lineages. Because the loss of UOX would have removed the functional constraint upon downstream genes in this pathway, these downstream genes are generally assumed to have subsequently deteriorated. In this study, we used a comparative genomics approach to empirically determine the fate of UOX itself and the downstream genes in five hominoids, two birds, and a reptile. Although we found that the loss of UOX likely triggered the genetic deterioration of the immediate downstream genes in the hominoids, surprisingly in the birds and reptiles, the UOX locus itself and some of the downstream genes were present in the genome and predicted to encode proteins. To account for the variable pattern of gene retention and loss after the inactivation of UOX, we hypothesize that although gene loss is a common fate for genes that have been rendered obsolete due to the upstream loss of an enzyme a metabolic pathway, it is also possible that same lack of constraint will foster the evolution of new functions or allow the optimization of preexisting alternative functions in the downstream genes, thereby resulting in gene retention. Thus, adaptive single-gene losses have the potential to influence the long-term evolutionary fate of functionally interrelated genes. PMID:20106906
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Keebaugh, Alaine C; Thomas, James W
2010-06-01
Gene loss has been proposed to play a major role in adaptive evolution, and recent studies are beginning to reveal its importance in human evolution. However, the potential consequence of a single gene-loss event upon the fates of functionally interrelated genes is poorly understood. Here, we use the purine metabolic pathway as a model system in which to explore this important question. The loss of urate oxidase (UOX) activity, a necessary step in this pathway, has occurred independently in the hominoid and bird/reptile lineages. Because the loss of UOX would have removed the functional constraint upon downstream genes in this pathway, these downstream genes are generally assumed to have subsequently deteriorated. In this study, we used a comparative genomics approach to empirically determine the fate of UOX itself and the downstream genes in five hominoids, two birds, and a reptile. Although we found that the loss of UOX likely triggered the genetic deterioration of the immediate downstream genes in the hominoids, surprisingly in the birds and reptiles, the UOX locus itself and some of the downstream genes were present in the genome and predicted to encode proteins. To account for the variable pattern of gene retention and loss after the inactivation of UOX, we hypothesize that although gene loss is a common fate for genes that have been rendered obsolete due to the upstream loss of an enzyme a metabolic pathway, it is also possible that same lack of constraint will foster the evolution of new functions or allow the optimization of preexisting alternative functions in the downstream genes, thereby resulting in gene retention. Thus, adaptive single-gene losses have the potential to influence the long-term evolutionary fate of functionally interrelated genes.
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Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks
2015-01-01
Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems. PMID:26356485
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Novel approaches to models of Alzheimer's disease pathology for drug screening and development.
Shaughnessy, Laura; Chamblin, Beth; McMahon, Lori; Nair, Ayyappan; Thomas, Mary Beth; Wakefield, John; Koentgen, Frank; Ramabhadran, Ram
2004-01-01
Development of therapeutics for Alzheimer's disease (AD) requires appropriate cell culture models that reflect the errant biochemical pathways and animal models that reflect the pathological hallmarks of the disease as well as the clinical manifestations. In the past two decades AD research has benefited significantly from the use of genetically engineered cell lines expressing components of the amyloid-generating pathway, as well as from the study of transgenic mice that develop the pathological hallmarks of the disease, mainly neuritic plaques. The choice of certain cell types and the choice of mouse as the model organism have been mandated by the feasibility of introduction and expression of foreign genes into these model systems. We describe a universal and efficient gene-delivery system, using lentiviral vectors, that permits the development of relevant cell biological systems using neuronal cells, including primary neurons and animal models in mammalian species best suited for the study of AD. In addition, lentiviral gene delivery provides avenues for creation of novel models by direct and prolonged expression of genes in the brain in any vertebrate animal. TranzVector is a lentiviral vector optimized for efficiency and safety that delivers genes to cells in culture, in tissue explants, and in live animals regardless of the dividing or differentiated status of the cells. Genes can also be delivered efficiently to fertilized single-cell-stage embryos of a wide range of mammalian species, broadening the range of the model organism (from rats to nonhuman primates) for the study of disease mechanism as well as for development of therapeutics. Copyright 2004 Humana Press Inc.
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Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar
2015-03-30
Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.
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Single cell digital polymerase chain reaction on self-priming compartmentalization chip
Zhu, Qiangyuan; Qiu, Lin; Xu, Yanan; Li, Guang; Mu, Ying
2017-01-01
Single cell analysis provides a new framework for understanding biology and disease, however, an absolute quantification of single cell gene expression still faces many challenges. Microfluidic digital polymerase chain reaction (PCR) provides a unique method to absolutely quantify the single cell gene expression, but only limited devices are developed to analyze a single cell with detection variation. This paper describes a self-priming compartmentalization (SPC) microfluidic digital polymerase chain reaction chip being capable of performing single molecule amplification from single cell. The chip can be used to detect four single cells simultaneously with 85% of sample digitization. With the optimized protocol for the SPC chip, we first tested the ability, precision, and sensitivity of our SPC digital PCR chip by assessing β-actin DNA gene expression in 1, 10, 100, and 1000 cells. And the reproducibility of the SPC chip is evaluated by testing 18S rRNA of single cells with 1.6%–4.6% of coefficient of variation. At last, by detecting the lung cancer related genes, PLAU gene expression of A549 cells at the single cell level, the single cell heterogeneity was demonstrated. So, with the power-free, valve-free SPC chip, the gene copy number of single cells can be quantified absolutely with higher sensitivity, reduced labor time, and reagent. We expect that this chip will enable new studies for biology and disease. PMID:28191267
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Single cell digital polymerase chain reaction on self-priming compartmentalization chip.
Zhu, Qiangyuan; Qiu, Lin; Xu, Yanan; Li, Guang; Mu, Ying
2017-01-01
Single cell analysis provides a new framework for understanding biology and disease, however, an absolute quantification of single cell gene expression still faces many challenges. Microfluidic digital polymerase chain reaction (PCR) provides a unique method to absolutely quantify the single cell gene expression, but only limited devices are developed to analyze a single cell with detection variation. This paper describes a self-priming compartmentalization (SPC) microfluidic digital polymerase chain reaction chip being capable of performing single molecule amplification from single cell. The chip can be used to detect four single cells simultaneously with 85% of sample digitization. With the optimized protocol for the SPC chip, we first tested the ability, precision, and sensitivity of our SPC digital PCR chip by assessing β-actin DNA gene expression in 1, 10, 100, and 1000 cells. And the reproducibility of the SPC chip is evaluated by testing 18S rRNA of single cells with 1.6%-4.6% of coefficient of variation. At last, by detecting the lung cancer related genes, PLAU gene expression of A549 cells at the single cell level, the single cell heterogeneity was demonstrated. So, with the power-free, valve-free SPC chip, the gene copy number of single cells can be quantified absolutely with higher sensitivity, reduced labor time, and reagent. We expect that this chip will enable new studies for biology and disease.
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Arnedo, Mireia; Taffé, Patrick; Sahli, Roland; Furrer, Hansjakob; Hirschel, Bernard; Elzi, Luigia; Weber, Rainer; Vernazza, Pietro; Bernasconi, Enos; Darioli, Roger; Bergmann, Sven; Beckmann, Jacques S; Telenti, Amalio; Tarr, Philip E
2007-09-01
HIV-1 infected individuals have an increased cardiovascular risk which is partially mediated by dyslipidemia. Single nucleotide polymorphisms in multiple genes involved in lipid transport and metabolism are presumed to modulate the risk of dyslipidemia in response to antiretroviral therapy. The contribution to dyslipidemia of 20 selected single nucleotide polymorphisms of 13 genes reported in the literature to be associated with plasma lipid levels (ABCA1, ADRB2, APOA5, APOC3, APOE, CETP, LIPC, LIPG, LPL, MDR1, MTP, SCARB1, and TNF) was assessed by longitudinally modeling more than 4400 plasma lipid determinations in 438 antiretroviral therapy-treated participants during a median period of 4.8 years. An exploratory genetic score was tested that takes into account the cumulative contribution of multiple gene variants to plasma lipids. Variants of ABCA1, APOA5, APOC3, APOE, and CETP contributed to plasma triglyceride levels, particularly in the setting of ritonavir-containing antiretroviral therapy. Variants of APOA5 and CETP contributed to high-density lipoprotein-cholesterol levels. Variants of CETP and LIPG contributed to non-high-density lipoprotein-cholesterol levels, a finding not reported previously. Sustained hypertriglyceridemia and low high-density lipoprotein-cholesterol during the study period was significantly associated with the genetic score. Single nucleotide polymorphisms of ABCA1, APOA5, APOC3, APOE, and CETP contribute to plasma triglyceride and high-density lipoprotein-cholesterol levels during antiretroviral therapy exposure. Genetic profiling may contribute to the identification of patients at risk for antiretroviral therapy-related dyslipidemia.
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Nguyen, Quan; Lukowski, Samuel; Chiu, Han; Senabouth, Anne; Bruxner, Timothy; Christ, Angelika; Palpant, Nathan; Powell, Joseph
2018-05-11
Heterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method, and through this identified four subpopulations distinguishable on the basis of their pluripotent state including: a core pluripotent population (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). For each subpopulation we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four discrete predictor gene sets comprised of 165 unique genes that denote the specific pluripotency states; and using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to 3-fold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations, and support our conclusions with results from two orthogonal pseudotime trajectory methods. Published by Cold Spring Harbor Laboratory Press.
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Genome-wide and gene-based association implicates FRMD6 in Alzheimer disease.
Hong, Mun-Gwan; Reynolds, Chandra A; Feldman, Adina L; Kallin, Mikael; Lambert, Jean-Charles; Amouyel, Philippe; Ingelsson, Erik; Pedersen, Nancy L; Prince, Jonathan A
2012-03-01
Genome-wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene-based meta-analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene-based algorithm, we identified two loci apart from the region around APOE that achieved study-wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10(-14)) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10(-9)). Ontology-based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene-based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker-specific meta-analysis. © 2011 Wiley Periodicals, Inc.
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Beaulieu, Jean; Doerksen, Trevor; Boyle, Brian; Clément, Sébastien; Deslauriers, Marie; Beauseigle, Stéphanie; Blais, Sylvie; Poulin, Pier-Luc; Lenz, Patrick; Caron, Sébastien; Rigault, Philippe; Bicho, Paul; Bousquet, Jean; MacKay, John
2011-01-01
Marker-assisted selection holds promise for highly influencing tree breeding, especially for wood traits, by considerably reducing breeding cycles and increasing selection accuracy. In this study, we used a candidate gene approach to test for associations between 944 single-nucleotide polymorphism markers from 549 candidate genes and 25 wood quality traits in white spruce. A mixed-linear model approach, including a weak but nonsignificant population structure, was implemented for each marker–trait combination. Relatedness among individuals was controlled using a kinship matrix estimated either from the known half-sib structure or from the markers. Both additive and dominance effect models were tested. Between 8 and 21 single-nucleotide polymorphisms (SNPs) were found to be significantly associated (P ≤ 0.01) with each of earlywood, latewood, or total wood traits. After controlling for multiple testing (Q ≤ 0.10), 13 SNPs were still significant across as many genes belonging to different families, each accounting for between 3 and 5% of the phenotypic variance in 10 wood characters. Transcript accumulation was determined for genes containing SNPs associated with these traits. Significantly different transcript levels (P ≤ 0.05) were found among the SNP genotypes of a 1-aminocyclopropane-1-carboxylate oxidase, a β-tonoplast intrinsic protein, and a long-chain acyl-CoA synthetase 9. These results should contribute toward the development of efficient marker-assisted selection in an economically important tree species. PMID:21385726
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Transcription factor clusters regulate genes in eukaryotic cells
Hedlund, Erik G; Friemann, Rosmarie; Hohmann, Stefan
2017-01-01
Transcription is regulated through binding factors to gene promoters to activate or repress expression, however, the mechanisms by which factors find targets remain unclear. Using single-molecule fluorescence microscopy, we determined in vivo stoichiometry and spatiotemporal dynamics of a GFP tagged repressor, Mig1, from a paradigm signaling pathway of Saccharomyces cerevisiae. We find the repressor operates in clusters, which upon extracellular signal detection, translocate from the cytoplasm, bind to nuclear targets and turnover. Simulations of Mig1 configuration within a 3D yeast genome model combined with a promoter-specific, fluorescent translation reporter confirmed clusters are the functional unit of gene regulation. In vitro and structural analysis on reconstituted Mig1 suggests that clusters are stabilized by depletion forces between intrinsically disordered sequences. We observed similar clusters of a co-regulatory activator from a different pathway, supporting a generalized cluster model for transcription factors that reduces promoter search times through intersegment transfer while stabilizing gene expression. PMID:28841133
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Divergent cytosine DNA methylation patterns in single-cell, soybean root hairs.
Hossain, Md Shakhawat; Kawakatsu, Taiji; Kim, Kyung Do; Zhang, Ning; Nguyen, Cuong T; Khan, Saad M; Batek, Josef M; Joshi, Trupti; Schmutz, Jeremy; Grimwood, Jane; Schmitz, Robert J; Xu, Dong; Jackson, Scott A; Ecker, Joseph R; Stacey, Gary
2017-04-01
Chromatin modifications, such as cytosine methylation of DNA, play a significant role in mediating gene expression in plants, which affects growth, development, and cell differentiation. As root hairs are single-cell extensions of the root epidermis and the primary organs for water uptake and nutrients, we sought to use root hairs as a single-cell model system to measure the impact of environmental stress. We measured changes in cytosine DNA methylation in single-cell root hairs as compared with multicellular stripped roots, as well as in response to heat stress. Differentially methylated regions (DMRs) in each methylation context showed very distinct methylation patterns between cell types and in response to heat stress. Intriguingly, at normal temperature, root hairs were more hypermethylated than were stripped roots. However, in response to heat stress, both root hairs and stripped roots showed hypomethylation in each context, especially in the CHH context. Moreover, expression analysis of mRNA from similar tissues and treatments identified some associations between DMRs, genes and transposons. Taken together, the data indicate that changes in DNA methylation are directly or indirectly associated with expression of genes and transposons within the context of either specific tissues/cells or stress (heat). © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.
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Monitoring transcription initiation activities in rat and dog.
Lizio, Marina; Mukarram, Abdul Kadir; Ohno, Mizuho; Watanabe, Shoko; Itoh, Masayoshi; Hasegawa, Akira; Lassmann, Timo; Severin, Jessica; Harshbarger, Jayson; Abugessaisa, Imad; Kasukawa, Takeya; Hon, Chung Chau; Carninci, Piero; Hayashizaki, Yoshihide; Forrest, Alistair R R; Kawaji, Hideya
2017-11-28
The promoter landscape of several non-human model organisms is far from complete. As a part of FANTOM5 data collection, we generated 13 profiles of transcription initiation activities in dog and rat aortic smooth muscle cells, mesenchymal stem cells and hepatocytes by employing CAGE (Cap Analysis of Gene Expression) technology combined with single molecule sequencing. Our analyses show that the CAGE profiles recapitulate known transcription start sites (TSSs) consistently, in addition to uncover novel TSSs. Our dataset can be thus used with high confidence to support gene annotation in dog and rat species. We identified 28,497 and 23,147 CAGE peaks, or promoter regions, for rat and dog respectively, and associated them to known genes. This approach could be seen as a standard method for improvement of existing gene models, as well as discovery of novel genes. Given that the FANTOM5 data collection includes dog and rat matched cell types in human and mouse as well, this data would also be useful for cross-species studies.
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Integration of the Gene Ontology into an object-oriented architecture.
Shegogue, Daniel; Zheng, W Jim
2005-05-10
To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes.
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Integration of the Gene Ontology into an object-oriented architecture
Shegogue, Daniel; Zheng, W Jim
2005-01-01
Background To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Results Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). Conclusion We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes. PMID:15885145
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Inferring gene regression networks with model trees
2010-01-01
Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET. PMID:20950452
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Wu, Zheyang; Zhao, Hongyu
2012-01-01
For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies.
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Wu, Zheyang; Zhao, Hongyu
2013-01-01
For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies. PMID:23956610
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NASA Astrophysics Data System (ADS)
Munsky, Brian
2015-03-01
MAPK signal-activated transcription plays central roles in myriad biological processes including stress adaptation responses and cell fate decisions. Recent single-cell and single-molecule experiments have advanced our ability to quantify the spatial, temporal, and stochastic fluctuations for such signals and their downstream effects on transcription regulation. This talk explores how integrating such experiments with discrete stochastic computational analyses can yield quantitative and predictive understanding of transcription regulation in both space and time. We use single-molecule mRNA fluorescence in situ hybridization (smFISH) experiments to reveal locations and numbers of multiple endogenous mRNA species in 100,000's of individual cells, at different times and under different genetic and environmental perturbations. We use finite state projection methods to precisely and efficiently compute the full joint probability distributions of these mRNA, which capture measured spatial, temporal and correlative fluctuations. By combining these experimental and computational tools with uncertainty quantification, we systematically compare models of varying complexity and select those which give optimally precise and accurate predictions in new situations. We use these tools to explore two MAPK-activated gene regulation pathways. In yeast adaptation to osmotic shock, we analyze Hog1 kinase activation of transcription for three different genes STL1 (osmotic stress), CTT1 (oxidative stress) and HSP12 (heat shock). In human osteosarcoma cells under serum induction, we analyze ERK activation of c-Fos transcription.
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Wang, Xiyin; Guo, Hui; Wang, Jinpeng; Lei, Tianyu; Liu, Tao; Wang, Zhenyi; Li, Yuxian; Lee, Tae-Ho; Li, Jingping; Tang, Haibao; Jin, Dianchuan; Paterson, Andrew H
2016-02-01
The 'apparently' simple genomes of many angiosperms mask complex evolutionary histories. The reference genome sequence for cotton (Gossypium spp.) revealed a ploidy change of a complexity unprecedented to date, indeed that could not be distinguished as to its exact dosage. Herein, by developing several comparative, computational and statistical approaches, we revealed a 5× multiplication in the cotton lineage of an ancestral genome common to cotton and cacao, and proposed evolutionary models to show how such a decaploid ancestor formed. The c. 70% gene loss necessary to bring the ancestral decaploid to its current gene count appears to fit an approximate geometrical model; that is, although many genes may be lost by single-gene deletion events, some may be lost in groups of consecutive genes. Gene loss following cotton decaploidy has largely just reduced gene copy numbers of some homologous groups. We designed a novel approach to deconvolute layers of chromosome homology, providing definitive information on gene orthology and paralogy across broad evolutionary distances, both of fundamental value and serving as an important platform to support further studies in and beyond cotton and genomics communities. No claim to original US government works. New Phytologist © 2015 New Phytologist Trust.
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Das, Mandakini; Sharma, Santanu Kumar; Sekhon, Gaganpreet Singh; Mahanta, Jagadish; Phukan, Rup Kumar; Jalan, Bimal Kumar
2017-05-01
The high incidence of esophageal cancer in Northeast India and the unique ethnic background and dietary habits provide a great opportunity to study the molecular genetics behind esophageal squamous cell carcinoma in this part of the region. We hypothesized that in addition to currently known environmental risk factors for esophageal cancer, genetic and epigenetic factors are also involved in esophageal carcinogenesis in Northeast India. Therefore, in this study, we explored the possible association between the two important G1 cell cycle regulatory genes p16 and p53 and environmental risk factors and risk of esophageal carcinogenesis. A total of 100 newly diagnosed esophageal cancer cases along with equal number of age-, sex-, and ethnicity-matched controls were included in this study. Methylation-specific polymerase chain reaction was used to determine the p16 promoter methylation status. Single-nucleotide polymorphism at codon 72 of p53 gene was assessed by the polymerase chain reaction-restriction fragment length polymorphism method. Aberrant methylation of p16 gene was seen in 81% of esophageal cancer cases. Hypermethylation of p16 gene was not found in healthy controls. p53 Pro/Pro genotype was found to be a risk genotype in Northeast India compared with Arg/Pro and Arg/Arg. p53 variant/polymorphism was significantly associated with esophageal cancer risk in the study population under all three genetic models, namely, dominant model (Arg/Pro + Pro/Pro vs Arg/Arg odds ratio = 2.25, confidence interval = 1.19-4.26; p = 0.012), recessive model (Arg/Arg + Arg/Pro vs Pro/Pro odds ratio = 2.35, confidence interval = 1.24-4.44; p = 0.008), and homozygous model (Pro/Pro vs Arg/Arg odds ratio = 3.33, confidence interval = 1.54-7.20; p = 0.002). However, p53 variant/polymorphism was not statistically associated with esophageal cancer risk under the heterozygous model (Pro/Pro vs Arg/Pro). In the case-only analysis based on p16 methylation, the p53 variant/polymorphism (Pro/Pro or Arg/Pro) showed significant association for esophageal cancer risk (odds ratio = 3.33, confidence interval = 1.54-7.20; p = 0.002). Gene-gene and gene-environment interaction using the case-only approach revealed a strong association between p16 methylation, p53 single-nucleotide polymorphism, and environmental factors and esophageal cancer risk. Cases with p16 methylation and p53 variant/polymorphism (Pro/Pro or Arg/Pro) along with both betel quid and tobacco chewing habit (odds ratio = 8.29, confidence interval = 1.14-60.23; p = 0.037) conferred eightfold increased risk toward esophageal cancer development. This study reveals a synergistic interaction between epigenetic, genetic, and environmental factors and risk of esophageal cancer in this high-incidence region of Northeast India. The inactivation of either p16 or p53 in a majority of esophageal cancer cases in this study suggests the possible crosstalk between the important cell cycle genes.
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Uno, Narumi; Abe, Satoshi; Oshimura, Mitsuo; Kazuki, Yasuhiro
2018-02-01
Chromosome transfer technology, including chromosome modification, enables the introduction of Mb-sized or multiple genes to desired cells or animals. This technology has allowed innovative developments to be made for models of human disease and humanized animals, including Down syndrome model mice and humanized transchromosomic (Tc) immunoglobulin mice. Genome editing techniques are developing rapidly, and permit modifications such as gene knockout and knockin to be performed in various cell lines and animals. This review summarizes chromosome transfer-related technologies and the combined technologies of chromosome transfer and genome editing mainly for the production of cell/animal models of human disease and humanized animal models. Specifically, these include: (1) chromosome modification with genome editing in Chinese hamster ovary cells and mouse A9 cells for efficient transfer to desired cell types; (2) single-nucleotide polymorphism modification in humanized Tc mice with genome editing; and (3) generation of a disease model of Down syndrome-associated hematopoiesis abnormalities by the transfer of human chromosome 21 to normal human embryonic stem cells and the induction of mutation(s) in the endogenous gene(s) with genome editing. These combinations of chromosome transfer and genome editing open up new avenues for drug development and therapy as well as for basic research.
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The transcription factor titration effect dictates level of gene expression.
Brewster, Robert C; Weinert, Franz M; Garcia, Hernan G; Song, Dan; Rydenfelt, Mattias; Phillips, Rob
2014-03-13
Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number-in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle. Copyright © 2014 Elsevier Inc. All rights reserved.
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Natural Mutations in Streptococcus agalactiae Resulting in Abrogation of β Antigen Production
Vasilyeva, Anastasia; Santos Sanches, Ilda; Florindo, Carlos; Dmitriev, Alexander
2015-01-01
Streptococcus agalactiae genome encodes 21 two-component systems (TCS) and a variety of regulatory proteins in order to control gene expression. One of the TCS, BgrRS, comprising the BgrR DNA-binding regulatory protein and BgrS sensor histidine kinase, was discovered within a putative virulence island. BgrRS influences cell metabolism and positively control the expression of bac gene, coding for β antigen at transcriptional level. Inactivation of bgrR abrogated bac gene expression and increased virulence properties of S. agalactiae. In this study, a total of 140 strains were screened for the presence of bac gene, and the TCS bgrR and bgrS genes. A total of 53 strains carried the bac, bgrR and bgrS genes. Most of them (48 strains) expressed β antigen, while five strains did not express β antigen. Three strains, in which bac gene sequence was intact, while bgrR and/or bgrS genes had mutations, and expression of β antigen was absent, were complemented with a constructed plasmid pBgrRS(P) encoding functionally active bgrR and bgrS gene alleles. This procedure restored expression of β antigen indicating the crucial regulatory role of TCS BgrRS. The complemented strain A49V/BgrRS demonstrated attenuated virulence in intraperitoneal mice model of S. agalactiae infection compared to parental strain A49V. In conclusion we showed that disruption of β antigen expression is associated with: i) insertion of ISSa4 upstream the bac gene just after the ribosomal binding site; ii) point mutation G342A resulting a stop codon TGA within the bac gene and a truncated form of β antigen; iii) single deletion (G) in position 439 of the bgrR gene resulting in a frameshift and the loss of DNA-binding domain of the BgrR protein, and iv) single base substitutions in bgrR and bgrS genes causing single amino acid substitutions in BgrR (Arg187Lys) and BgrS (Arg252Gln). The fact that BgrRS negatively controls virulent properties of S. agalactiae gives a novel clue for understanding of S. agalactiae adaptation to the human. PMID:26047354
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Natural Mutations in Streptococcus agalactiae Resulting in Abrogation of β Antigen Production.
Vasilyeva, Anastasia; Santos Sanches, Ilda; Florindo, Carlos; Dmitriev, Alexander
2015-01-01
Streptococcus agalactiae genome encodes 21 two-component systems (TCS) and a variety of regulatory proteins in order to control gene expression. One of the TCS, BgrRS, comprising the BgrR DNA-binding regulatory protein and BgrS sensor histidine kinase, was discovered within a putative virulence island. BgrRS influences cell metabolism and positively control the expression of bac gene, coding for β antigen at transcriptional level. Inactivation of bgrR abrogated bac gene expression and increased virulence properties of S. agalactiae. In this study, a total of 140 strains were screened for the presence of bac gene, and the TCS bgrR and bgrS genes. A total of 53 strains carried the bac, bgrR and bgrS genes. Most of them (48 strains) expressed β antigen, while five strains did not express β antigen. Three strains, in which bac gene sequence was intact, while bgrR and/or bgrS genes had mutations, and expression of β antigen was absent, were complemented with a constructed plasmid pBgrRS(P) encoding functionally active bgrR and bgrS gene alleles. This procedure restored expression of β antigen indicating the crucial regulatory role of TCS BgrRS. The complemented strain A49V/BgrRS demonstrated attenuated virulence in intraperitoneal mice model of S. agalactiae infection compared to parental strain A49V. In conclusion we showed that disruption of β antigen expression is associated with: i) insertion of ISSa4 upstream the bac gene just after the ribosomal binding site; ii) point mutation G342A resulting a stop codon TGA within the bac gene and a truncated form of β antigen; iii) single deletion (G) in position 439 of the bgrR gene resulting in a frameshift and the loss of DNA-binding domain of the BgrR protein, and iv) single base substitutions in bgrR and bgrS genes causing single amino acid substitutions in BgrR (Arg187Lys) and BgrS (Arg252Gln). The fact that BgrRS negatively controls virulent properties of S. agalactiae gives a novel clue for understanding of S. agalactiae adaptation to the human.
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Sharma, Rahul; Beer, Katharina; Iwanov, Katharina; Schmöhl, Felix; Beckmann, Paula Indigo; Schröder, Reinhard
2015-06-15
The precise regulation of cell-cell communication by numerous signal-transduction pathways is fundamental for many different processes during embryonic development. One important signalling pathway is the evolutionary conserved fibroblast-growth-factor (FGF)-pathway that controls processes like cell migration, axis specification and mesoderm formation in vertebrate and invertebrate animals. In the model insect Drosophila, the FGF ligand / receptor combinations of FGF8 (Pyramus and Thisbe) / Heartless (Htl) and Branchless (Bnl) / Breathless (Btl) are required for the migration of mesodermal cells and for the formation of the tracheal network respectively with both the receptors functioning independently of each other. However, only a single fgf-receptor gene (Tc-fgfr) has been identified in the genome of the beetle Tribolium. We therefore asked whether both the ligands Fgf8 and Bnl could transduce their signal through a common FGF-receptor in Tribolium. Indeed, we found that the function of the single Tc-fgfr gene is essential for mesoderm differentiation as well as for the formation of the tracheal network during early development. Ligand specific RNAi for Tc-fgf8 and Tc-bnl resulted in two distinct non-overlapping phenotypes of impaired mesoderm differentiation and abnormal formation of the tracheal network in Tc-fgf8- and Tc-bnl(RNAi) embryos respectively. We further show that the single Tc-fgfr gene encodes at least two different receptor isoforms that are generated through alternative splicing. We in addition demonstrate through exon-specific RNAi their distinct tissue-specific functions. Finally, we discuss the structure of the fgf-receptor gene from an evolutionary perspective. Copyright © 2015 Elsevier Inc. All rights reserved.
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The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.
Lin, Zheng; Su, Yousong; Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong
2013-01-01
The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia.
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The Interaction of BDNF and NTRK2 Gene Increases the Susceptibility of Paranoid Schizophrenia
Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong
2013-01-01
The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia. PMID:24069289
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Anti-tumor effect of in vivo IL-2 and GM-CSF electrogene therapy in murine hepatoma model.
Chi, Chau-Hwa; Wang, Yu-Shan; Lai, Yen-Shuae; Chi, Kwan-Hwa
2003-01-01
We evaluated the effect of in vivo electrogene therapy (EGT), a newly-developed gene transfer method using electroporation on the induction of anti-cancer immunity. The in vivo EGT was carried out by direct injection of plasmid DNAs encoding mouse interleukin-2 (IL-2) and granulocyte-macrophage colony-stimulating factor (GM-CSF) in a subcutaneous murine hepatoma model of 1MEA.7R.1 cells. Six electric pulses were generated in situ from a square-wave electroporator fitted with a circular, six-needle electrode array. 1MEA.7R1 cells in vitro were modified to secret IL-2 (1MEA.7R.1/IL-2 cells). The 1MEA.7R.1/IL-2 cells had a similar cell doubling-time as their parent cells but showed a much slower growth rate on Balb/C mice. One, or 3 rounds of single gene EGT with IL-2 gene showed a dose-responsive effect of growth retardation. Co-administration of 3 rounds of IL-2/GM-CSF double genes EGT had a stronger growth inhibition effect than 3 rounds of IL-2 single gene EGT. Three rounds of IL-2/GM-CSF EGT rendered the tumor to a growth rate of stably transfected 1MEA.7R.1/IL-2 cells. Seven rounds of IL-2/GM-CSF EGT markedly inhibited the tumor growth. Reverse transciptase-polymerase chain reaction confirmed the expression of IL-2, GM-CSF and interferon-gamma within treated tumors. Systemic inhibitory effects can be demonstrated from tumor-re-challenged experiments on mice which received 3 rounds of double-gene EGT. The T cell proliferation assay revealed an increased T cell proliferation in double-gene EGT-treated mice. This experiment showed that partial systemic immunity can be provoked by IL-2/GM-CSF double-gene EGT. These findings suggest that our immuno-gene therapy protocol has the potential for future clinical applications.
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Comparative physical mapping between wheat chromosome arm 2BL and rice chromosome 4.
Lee, Tong Geon; Lee, Yong Jin; Kim, Dae Yeon; Seo, Yong Weon
2010-12-01
Physical maps of chromosomes provide a framework for organizing and integrating diverse genetic information. DNA microarrays are a valuable technique for physical mapping and can also be used to facilitate the discovery of single feature polymorphisms (SFPs). Wheat chromosome arm 2BL was physically mapped using a Wheat Genome Array onto near-isogenic lines (NILs) with the aid of wheat-rice synteny and mapped wheat EST information. Using high variance probe set (HVP) analysis, 314 HVPs constituting genes present on 2BL were identified. The 314 HVPs were grouped into 3 categories: HVPs that match only rice chromosome 4 (298 HVPs), those that match only wheat ESTs mapped on 2BL (1), and those that match both rice chromosome 4 and wheat ESTs mapped on 2BL (15). All HVPs were converted into gene sets, which represented either unique rice gene models or mapped wheat ESTs that matched identified HVPs. Comparative physical maps were constructed for 16 wheat gene sets and 271 rice gene sets. Of the 271 rice gene sets, 257 were mapped to the 18-35 Mb regions on rice chromosome 4. Based on HVP analysis and sequence similarity between the gene models in the rice chromosomes and mapped wheat ESTs, the outermost rice gene model that limits the translocation breakpoint to orthologous regions was identified.
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Huang, Xuena; Gao, Yangchun; Jiang, Bei; Zhou, Zunchun; Zhan, Aibin
2016-01-15
As invasive species have successfully colonized a wide range of dramatically different local environments, they offer a good opportunity to study interactions between species and rapidly changing environments. Gene expression represents one of the primary and crucial mechanisms for rapid adaptation to local environments. Here, we aim to select reference genes for quantitative gene expression analysis based on quantitative Real-Time PCR (qRT-PCR) for a model invasive ascidian, Ciona savignyi. We analyzed the stability of ten candidate reference genes in three tissues (siphon, pharynx and intestine) under two key environmental stresses (temperature and salinity) in the marine realm based on three programs (geNorm, NormFinder and delta Ct method). Our results demonstrated only minor difference for stability rankings among the three methods. The use of different single reference gene might influence the data interpretation, while multiple reference genes could minimize possible errors. Therefore, reference gene combinations were recommended for different tissues - the optimal reference gene combination for siphon was RPS15 and RPL17 under temperature stress, and RPL17, UBQ and TubA under salinity treatment; for pharynx, TubB, TubA and RPL17 were the most stable genes under temperature stress, while TubB, TubA and UBQ were the best under salinity stress; for intestine, UBQ, RPS15 and RPL17 were the most reliable reference genes under both treatments. Our results suggest that the necessity of selection and test of reference genes for different tissues under varying environmental stresses. The results obtained here are expected to reveal mechanisms of gene expression-mediated invasion success using C. savignyi as a model species. Copyright © 2015 Elsevier B.V. All rights reserved.
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Culture adaptation of malaria parasites selects for convergent loss-of-function mutants.
Claessens, Antoine; Affara, Muna; Assefa, Samuel A; Kwiatkowski, Dominic P; Conway, David J
2017-01-24
Cultured human pathogens may differ significantly from source populations. To investigate the genetic basis of laboratory adaptation in malaria parasites, clinical Plasmodium falciparum isolates were sampled from patients and cultured in vitro for up to three months. Genome sequence analysis was performed on multiple culture time point samples from six monoclonal isolates, and single nucleotide polymorphism (SNP) variants emerging over time were detected. Out of a total of five positively selected SNPs, four represented nonsense mutations resulting in stop codons, three of these in a single ApiAP2 transcription factor gene, and one in SRPK1. To survey further for nonsense mutants associated with culture, genome sequences of eleven long-term laboratory-adapted parasite strains were examined, revealing four independently acquired nonsense mutations in two other ApiAP2 genes, and five in Epac. No mutants of these genes exist in a large database of parasite sequences from uncultured clinical samples. This implicates putative master regulator genes in which multiple independent stop codon mutations have convergently led to culture adaptation, affecting most laboratory lines of P. falciparum. Understanding the adaptive processes should guide development of experimental models, which could include targeted gene disruption to adapt fastidious malaria parasite species to culture.
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Tidball, Andrew M; Dang, Louis T; Glenn, Trevor W; Kilbane, Emma G; Klarr, Daniel J; Margolis, Joshua L; Uhler, Michael D; Parent, Jack M
2017-09-12
Specifically ablating genes in human induced pluripotent stem cells (iPSCs) allows for studies of gene function as well as disease mechanisms in disorders caused by loss-of-function (LOF) mutations. While techniques exist for engineering such lines, we have developed and rigorously validated a method of simultaneous iPSC reprogramming while generating CRISPR/Cas9-dependent insertions/deletions (indels). This approach allows for the efficient and rapid formation of genetic LOF human disease cell models with isogenic controls. The rate of mutagenized lines was strikingly consistent across experiments targeting four different human epileptic encephalopathy genes and a metabolic enzyme-encoding gene, and was more efficient and consistent than using CRISPR gene editing of established iPSC lines. The ability of our streamlined method to reproducibly generate heterozygous and homozygous LOF iPSC lines with passage-matched isogenic controls in a single step provides for the rapid development of LOF disease models with ideal control lines, even in the absence of patient tissue. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
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Gene therapy decreases seizures in a model of Incontinentia pigmenti.
Dogbevia, Godwin K; Töllner, Kathrin; Körbelin, Jakob; Bröer, Sonja; Ridder, Dirk A; Grasshoff, Hanna; Brandt, Claudia; Wenzel, Jan; Straub, Beate K; Trepel, Martin; Löscher, Wolfgang; Schwaninger, Markus
2017-07-01
Incontinentia pigmenti (IP) is a genetic disease leading to severe neurological symptoms, such as epileptic seizures, but no specific treatment is available. IP is caused by pathogenic variants that inactivate the Nemo gene. Replacing Nemo through gene therapy might provide therapeutic benefits. In a mouse model of IP, we administered a single intravenous dose of the adeno-associated virus (AAV) vector, AAV-BR1-CAG-NEMO, delivering the Nemo gene to the brain endothelium. Spontaneous epileptic seizures and the integrity of the blood-brain barrier (BBB) were monitored. The endothelium-targeted gene therapy improved the integrity of the BBB. In parallel, it reduced the incidence of seizures and delayed their occurrence. Neonate mice intravenously injected with the AAV-BR1-CAG-NEMO vector developed no hepatocellular carcinoma or other major adverse effects 11 months after vector injection, demonstrating that the vector has a favorable safety profile. The data show that the BBB is a target of antiepileptic treatment and, more specifically, provide evidence for the therapeutic benefit of a brain endothelial-targeted gene therapy in IP. Ann Neurol 2017;82:93-104. © 2017 American Neurological Association.
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Synergistic Action of FOXP3 and TSC1 Pathways During Tumor Progression
2015-10-01
invasive carcinoma and, ultimately, metastatic disease [1-3]. Mouse models of PIN (mPIN) generated by a single- mutant gene in prostate do not progress...downstream target) is sufficient to significantly reduce the initiation of prostate cancer in the Pten conditional knockout mouse model [19-21...the possibility that these two genetic hits cooperate to promote tumor progression, and mouse models show that this cooperation accelerates
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Co-expression networks reveal the tissue-specific regulation of transcription and splicing
Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D.H.; Jo, Brian; Gao, Chuan; McDowell, Ian C.; Engelhardt, Barbara E.
2017-01-01
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. PMID:29021288
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Wang, Y; Smallwood, P M; Cowan, M; Blesh, D; Lawler, A; Nathans, J
1999-04-27
This study examines the mechanism of mutually exclusive expression of the human X-linked red and green visual pigment genes in their respective cone photoreceptors by asking whether this expression pattern can be produced in a mammal that normally carries only a single X-linked visual pigment gene. To address this question, we generated transgenic mice that carry a single copy of a minimal human X chromosome visual pigment gene array in which the red and green pigment gene transcription units were replaced, respectively, by alkaline phosphatase and beta-galactosidase reporters. As determined by histochemical staining, the reporters are expressed exclusively in cone photoreceptor cells. In 20 transgenic mice carrying any one of three independent transgene insertion events, an average of 63% of expressing cones have alkaline phosphatase activity, 10% have beta-galactosidase activity, and 27% have activity for both reporters. Thus, mutually exclusive expression of red and green pigment transgenes can be achieved in a large fraction of cones in a dichromat mammal, suggesting a facile evolutionary path for the development of trichromacy after visual pigment gene duplication. These observations are consistent with a model of visual pigment expression in which stochastic pairing occurs between a locus control region and either the red or the green pigment gene promotor.
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Asaf, Sajjad; Khan, Abdul Latif; Khan, Muhammad Aaqil; Waqas, Muhammad; Kang, Sang-Mo; Yun, Byung-Wook; Lee, In-Jung
2017-08-08
We investigated the complete chloroplast (cp) genomes of non-model Arabidopsis halleri ssp. gemmifera and Arabidopsis lyrata ssp. petraea using Illumina paired-end sequencing to understand their genetic organization and structure. Detailed bioinformatics analysis revealed genome sizes of both subspecies ranging between 154.4~154.5 kbp, with a large single-copy region (84,197~84,158 bp), a small single-copy region (17,738~17,813 bp) and pair of inverted repeats (IRa/IRb; 26,264~26,259 bp). Both cp genomes encode 130 genes, including 85 protein-coding genes, eight ribosomal RNA genes and 37 transfer RNA genes. Whole cp genome comparison of A. halleri ssp. gemmifera and A. lyrata ssp. petraea, along with ten other Arabidopsis species, showed an overall high degree of sequence similarity, with divergence among some intergenic spacers. The location and distribution of repeat sequences were determined, and sequence divergences of shared genes were calculated among related species. Comparative phylogenetic analysis of the entire genomic data set and 70 shared genes between both cp genomes confirmed the previous phylogeny and generated phylogenetic trees with the same topologies. The sister species of A. halleri ssp. gemmifera is A. umezawana, whereas the closest relative of A. lyrata spp. petraea is A. arenicola.
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Dalman, Kerstin; Himmelstrand, Kajsa; Olson, Åke; Lind, Mårten; Brandström-Durling, Mikael; Stenlid, Jan
2013-01-01
The dense single nucleotide polymorphisms (SNP) panels needed for genome wide association (GWA) studies have hitherto been expensive to establish and use on non-model organisms. To overcome this, we used a next generation sequencing approach to both establish SNPs and to determine genotypes. We conducted a GWA study on a fungal species, analysing the virulence of Heterobasidion annosum s.s., a necrotrophic pathogen, on its hosts Picea abies and Pinus sylvestris. From a set of 33,018 single nucleotide polymorphisms (SNP) in 23 haploid isolates, twelve SNP markers distributed on seven contigs were associated with virulence (P<0.0001). Four of the contigs harbour known virulence genes from other fungal pathogens and the remaining three harbour novel candidate genes. Two contigs link closely to virulence regions recognized previously by QTL mapping in the congeneric hybrid H. irregulare × H. occidentale. Our study demonstrates the efficiency of GWA studies for dissecting important complex traits of small populations of non-model haploid organisms with small genomes. PMID:23341945
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Herranz, Mari Carmen; Niehl, Annette; Rosales, Marlene; Fiore, Nicola; Zamorano, Alan; Granell, Antonio; Pallas, Vicente
2013-05-28
Microarray profiling is a powerful technique to investigate expression changes of large amounts of genes in response to specific environmental conditions. The majority of the studies investigating gene expression changes in virus-infected plants are limited to interactions between a virus and a model host plant, which usually is Arabidopsis thaliana or Nicotiana benthamiana. In the present work, we performed microarray profiling to explore changes in the expression profile of field-grown Prunus persica (peach) originating from Chile upon single and double infection with Prunus necrotic ringspot virus (PNRSV) and Peach latent mosaic viroid (PLMVd), worldwide natural pathogens of peach trees. Upon single PLMVd or PNRSV infection, the number of statistically significant gene expression changes was relatively low. By contrast, doubly-infected fruits presented a high number of differentially regulated genes. Among these, down-regulated genes were prevalent. Functional categorization of the gene expression changes upon double PLMVd and PNRSV infection revealed protein modification and degradation as the functional category with the highest percentage of repressed genes whereas induced genes encoded mainly proteins related to phosphate, C-compound and carbohydrate metabolism and also protein modification. Overrepresentation analysis upon double infection with PLMVd and PNRSV revealed specific functional categories over- and underrepresented among the repressed genes indicating active counter-defense mechanisms of the pathogens during infection. Our results identify a novel synergistic effect of PLMVd and PNRSV on the transcriptome of peach fruits. We demonstrate that mixed infections, which occur frequently in field conditions, result in a more complex transcriptional response than that observed in single infections. Thus, our data demonstrate for the first time that the simultaneous infection of a viroid and a plant virus synergistically affect the host transcriptome in infected peach fruits. These field studies can help to fully understand plant-pathogen interactions and to develop appropriate crop protection strategies.
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2013-01-01
Background Microarray profiling is a powerful technique to investigate expression changes of large amounts of genes in response to specific environmental conditions. The majority of the studies investigating gene expression changes in virus-infected plants are limited to interactions between a virus and a model host plant, which usually is Arabidopsis thaliana or Nicotiana benthamiana. In the present work, we performed microarray profiling to explore changes in the expression profile of field-grown Prunus persica (peach) originating from Chile upon single and double infection with Prunus necrotic ringspot virus (PNRSV) and Peach latent mosaic viroid (PLMVd), worldwide natural pathogens of peach trees. Results Upon single PLMVd or PNRSV infection, the number of statistically significant gene expression changes was relatively low. By contrast, doubly-infected fruits presented a high number of differentially regulated genes. Among these, down-regulated genes were prevalent. Functional categorization of the gene expression changes upon double PLMVd and PNRSV infection revealed protein modification and degradation as the functional category with the highest percentage of repressed genes whereas induced genes encoded mainly proteins related to phosphate, C-compound and carbohydrate metabolism and also protein modification. Overrepresentation analysis upon double infection with PLMVd and PNRSV revealed specific functional categories over- and underrepresented among the repressed genes indicating active counter-defense mechanisms of the pathogens during infection. Conclusions Our results identify a novel synergistic effect of PLMVd and PNRSV on the transcriptome of peach fruits. We demonstrate that mixed infections, which occur frequently in field conditions, result in a more complex transcriptional response than that observed in single infections. Thus, our data demonstrate for the first time that the simultaneous infection of a viroid and a plant virus synergistically affect the host transcriptome in infected peach fruits. These field studies can help to fully understand plant-pathogen interactions and to develop appropriate crop protection strategies. PMID:23710752
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Sun, F J; Zou, L Y; Tong, D M; Lu, X Y; Li, J; Deng, C B
2017-08-31
This study aimed to investigate the association between ADAM metallopeptidase domain 33 (ADAM33) gene polymorphisms and the risk of childhood asthma. The relevant studies about the relationship between ADAM33 gene polymorphisms and childhood asthma were searched from electronic databases and the deadline of retrieval was May 2016. The single nucleotide polymorphisms (SNPs) of ADAM33 (rs511898, rs2280092, rs3918396, rs528557, rs2853209, rs44707, rs2280091 and rs2280089) were analyzed based on several models including the allele, codominant, recessive and dominant models. The results showed that the ADAM33 rs2280091 polymorphism in all four genetic models was associated with an increased risk of childhood asthma. Positive associations were also found between the polymorphisms rs2280090, rs2787094, rs44707 and rs528557 and childhood asthma in some genetic models. This meta-analysis suggested that ADAM33 polymorphisms rs2280091, rs2280090, rs2787094, rs44707 and rs528557 were significantly associated with a high risk of childhood asthma.
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Ding, Jiarui; Condon, Anne; Shah, Sohrab P
2018-05-21
Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.
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Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.
Ezer, Daphne; Moignard, Victoria; Göttgens, Berthold; Adryan, Boris
2016-08-01
Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.
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IRAK1 variant is protective for orthodontic-induced external apical root resorption.
Pereira, S; Nogueira, L; Canova, F; Lopez, M; Silva, H C
2016-10-01
Interleukin-1 beta (IL1B) pathway is a key player in orthodontic-induced external apical root resorption (EARR). The aim of this work was to identify the genes related to the IL1 pathway as possible candidate genes for EARR, which might be included in an integrative predictive model of this complex phenotype. Using a stepwise multiple linear regression model, 195 patients who had undergone orthodontic treatment were assessed for clinical and genetic factors associated with %EARRmax (maximum %EARR value obtained for each patient). The four maxillary incisors and the two maxillary canines were assessed. Three functional single nucleotide polymorphisms (SNPs) were genotyped: rs1143634 in IL1B gene, rs315952 in IL1RN gene, and rs1059703 in X-linked IRAK1 gene. The model showed that four of the nine clinical variables and one SNP explained 30% of the %EARRmax variability. The most significant unique contributions to the model were gender (P = 0.001), treatment duration (P < 0.001), premolar extractions (P = 0.003), Hyrax appliance (P < 0.001), and homozygosity/hemizygosity for variant C from IRAK1 gene (P = 0.018), which proved to be a protective factor. IRAK1 polymorphism is proposed as a protective variant for EARR. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Schneider, Ronen; Hoogstraten, Charlotte A.; Schapiro, David; Majmundar, Amar J.; Kolb, Amy; Eddy, Kaitlyn; Shril, Shirlee; Braun, Daniela A.; Poduri, Annapurna
2018-01-01
Until recently, morpholino oligonucleotides have been widely employed in zebrafish as an acute and efficient loss-of-function assay. However, off-target effects and reproducibility issues when compared to stable knockout lines have compromised their further use. Here we employed an acute CRISPR/Cas approach using multiple single guide RNAs targeting simultaneously different positions in two exemplar genes (osgep or tprkb) to increase the likelihood of generating mutations on both alleles in the injected F0 generation and to achieve a similar effect as morpholinos but with the reproducibility of stable lines. This multi single guide RNA approach resulted in median likelihoods for at least one mutation on each allele of >99% and sgRNA specific insertion/deletion profiles as revealed by deep-sequencing. Immunoblot showed a significant reduction for Osgep and Tprkb proteins. For both genes, the acute multi-sgRNA knockout recapitulated the microcephaly phenotype and reduction in survival that we observed previously in stable knockout lines, though milder in the acute multi-sgRNA knockout. Finally, we quantify the degree of mutagenesis by deep sequencing, and provide a mathematical model to quantitate the chance for a biallelic loss-of-function mutation. Our findings can be generalized to acute and stable CRISPR/Cas targeting for any zebrafish gene of interest. PMID:29346415
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Cheng, Feixiong; Liu, Chuang; Lin, Chen-Ching; Zhao, Junfei; Jia, Peilin; Li, Wen-Hsiung; Zhao, Zhongming
2015-09-01
Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.
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Lin, Chen-Ching; Zhao, Junfei; Jia, Peilin; Li, Wen-Hsiung; Zhao, Zhongming
2015-01-01
Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics. PMID:26352260
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Mathematical modelling of radiotherapy strategies for early breast cancer.
Enderling, Heiko; Anderson, Alexander R A; Chaplain, Mark A J; Munro, Alastair J; Vaidya, Jayant S
2006-07-07
Targeted intraoperative radiotherapy (Targit) is a new concept of partial breast irradiation where single fraction radiotherapy is delivered directly to the tumour bed. Apart from logistic advantages, this strategy minimizes the risk of missing the tumour bed and avoids delay between surgery and radiotherapy. It is presently being compared with the standard fractionated external beam radiotherapy (EBRT) in randomized trials. In this paper we present a mathematical model for the growth and invasion of a solid tumour into a domain of tissue (in this case breast tissue), and then a model for surgery and radiation treatment of this tumour. We use the established linear-quadratic (LQ) model to compute the survival probabilities for both tumour cells and irradiated breast tissue and then simulate the effects of conventional EBRT and Targit. True local recurrence of the tumour could arise either from stray tumour cells, or the tumour bed that harbours morphologically normal cells having a predisposition to genetic changes, such as a loss of heterozygosity (LOH) in genes that are crucial for tumourigenesis, e.g. tumour suppressor genes (TSGs). Our mathematical model predicts that the single high dose of radiotherapy delivered by Targit would result in eliminating all these sources of recurrence, whereas the fractionated EBRT would eliminate stray tumour cells, but allow (by virtue of its very schedule) the cells with LOH in TSGs or cell-cycle checkpoint genes to pass on low-dose radiation-induced DNA damage and consequently mutations that may favour the development of a new tumour. The mathematical model presented here is an initial attempt to model a biologically complex phenomenon that has until now received little attention in the literature and provides a 'proof of principle' that it is possible to produce clinically testable hypotheses on the effects of different approaches of radiotherapy for breast cancer.
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Pappas, Jane J; Petropoulos, Sophie; Suderman, Matthew; Iqbal, Majid; Moisiadis, Vasilis; Turecki, Gustavo; Matthews, Stephen G; Szyf, Moshe
2014-01-01
The Multidrug Resistance 1 (MDR1; alternatively ABCB1) gene product P-glycoprotein (P-gp), an ATP binding cassette transporter, extrudes multiple endogenous and exogenous substrates from the cell, playing an important role in normal physiology and xenobiotic distribution and bioavailability. To date, the predominant animal models used to investigate the role of P-gp have been the mouse and rat, which have two distinct genes, Abcb1a and Abcb1b. In contrast, the human has a single gene, ABCB1, for which only a single isoform has been validated. We and others have previously shown important differences between Abcb1a and Abcb1b, limiting the extrapolation from rodent findings to the human. Since the guinea pig has a relatively long gestation, hemomonochorial placentation and neuroanatomically mature offspring, it is more similar to the human, and may provide a more comparable model for investigating the regulation of P-gp in the brain and placenta, however, to date, the Abcb1 gene in the guinea pig remains to be characterized. The placenta and fetal brain are barrier sites that express P-gp and that play a critical role of protection of the fetus and the fetal brain from maternally administered drugs and other xenobiotics. Using RNA sequencing (RNA-seq), reverse transcription-polymerase chain reaction (RT-PCR) and quantitative PCR (QPCR) to sequence the expressed isoforms of guinea pig Abcb1, we demonstrate that like the human, the guinea pig genome contains one gene for Abcb1 but that it is expressed as at least three different isoforms via alternative splicing and alternate exon usage. Further, we demonstrate that these isoforms are more closely related to human than to rat or mouse isoforms. This striking, overall similarity and evolutionary relatedness between guinea pig Abcb1 and human ABCB1 indicate that the guinea pig represents a relevant animal model for investigating the function and regulation of P-gp in the placenta and brain.
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Associations of polymorphisms in the Pit-1 gene with growth and carcass traits in Angus beef cattle.
Zhao, Q; Davis, M E; Hines, H C
2004-08-01
The Pit-1 gene was studied as a candidate for genetic markers of growth and carcass traits. Angus beef cattle that were divergently selected for high- or low-blood serum IGF-I concentration were used in this study. The single-strand conformation polymorphism method was used to identify polymorphism in the Pit-1 gene including regions from intron 2 to exon 6. Two polymorphisms, Pit1I3H (HinfI) and Pit1I3NL (NlaIII), were detected in intron 3 of the Pit-1 gene. One polymorphism, Pit1I4N (BstNI), was found in intron 4, and a single nucleotide polymorphism, Pit1I5, was found in intron 5. The previously reported polymorphism in exon 6, Pit1E6H (HinfI), was also studied in 416 Angus beef cattle. Associations of the polymorphisms with growth traits, carcass traits, and IGF-I concentration were analyzed using a general linear model procedure. No significant associations were observed between these polymorphisms and growth and carcass traits.
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Bi, Lianxiang; Wacker, Bradley K; Bueren, Emma; Ham, Ervin; Dronadula, Nagadhara; Dichek, David A
2017-12-15
Coronary artery bypass vein grafts are a mainstay of therapy for human atherosclerosis. Unfortunately, the long-term patency of vein grafts is limited by accelerated atherosclerosis. Gene therapy, directed at the vein graft wall, is a promising approach for preventing vein graft atherosclerosis. Because helper-dependent adenovirus (HDAd) efficiently transduces grafted veins and confers long-term transgene expression, HDAd is an excellent candidate for delivery of vein graft-targeted gene therapy. We developed a model of vein graft atherosclerosis in fat-fed rabbits and demonstrated long-term (≥20 weeks) persistence of HDAd genomes after graft transduction. This model enables quantitation of vein graft hemodynamics, wall structure, lipid accumulation, cellularity, vector persistence, and inflammatory markers on a single graft. Time-course experiments identified 12 weeks after transduction as an optimal time to measure efficacy of gene therapy on the critical variables of lipid and macrophage accumulation. We also used chow-fed rabbits to test whether HDAd infusion in vein grafts promotes intimal growth and inflammation. HDAd did not increase intimal growth, but had moderate-yet significant-pro-inflammatory effects. The vein graft atherosclerosis model will be useful for testing HDAd-mediated gene therapy; however, pro-inflammatory effects of HdAd remain a concern in developing HDAd as a therapy for vein graft disease.
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Behavioral phenotypes of genetic mouse models of autism.
Kazdoba, T M; Leach, P T; Crawley, J N
2016-01-01
More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.
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Dynamical Analysis of Density-dependent Selection in a Discrete one-island Migration Model
James H. Roberds; James F. Selgrade
2000-01-01
A system of non-linear difference equations is used to model the effects of density-dependent selection and migration in a population characterized by two alleles at a single gene locus. Results for the existence and stability of polymorphic equilibria are established. Properties for a genetically important class of equilibria associated with complete dominance in...
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CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.
Cestarelli, Valerio; Fiscon, Giulia; Felici, Giovanni; Bertolazzi, Paola; Weitschek, Emanuel
2016-03-01
Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class. We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates the power set of the genes present in the rules, iteratively eliminates those combinations from the data set, and performs again the classification procedure until a stopping criterion is verified. CAMUR includes an ad-hoc knowledge repository (database) and a querying tool.We analyze three different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain several reliable equivalent classification models, from which the most frequent genes, their relationships, and the relation with a particular cancer are deduced. dmb.iasi.cnr.it/camur.php emanuel@iasi.cnr.it Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.
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Rai1 duplication causes physical and behavioral phenotypes in a mouse model of dup(17)(p11.2p11.2)
Walz, Katherina; Paylor, Richard; Yan, Jiong; Bi, Weimin; Lupski, James R.
2006-01-01
Genomic disorders are conditions that result from DNA rearrangements, such as deletions or duplications. The identification of the dosage-sensitive gene(s) within the rearranged genomic interval is important for the elucidation of genes responsible for complex neurobehavioral phenotypes. Smith-Magenis syndrome is associated with a 3.7-Mb deletion in 17p11.2, and its clinical presentation is caused by retinoic acid inducible 1 (RAI1) haploinsufficiency. The reciprocal microduplication syndrome, dup(17)(p11.2p11.2), manifests several neurobehavioral abnormalities, but the responsible dosage-sensitive gene(s) remain undefined. We previously generated a mouse model for dup(17)(p11.2p11.2), Dp(11)17/+, that recapitulated most of the phenotypes observed in human patients. We have now analyzed compound heterozygous mice carrying a duplication [Dp(11)17] in one chromosome 11 along with a null allele of Rai1 in the other chromosome 11 homologue [Dp(11)17/Rai1– mice] in order to study the relationship between Rai1 gene copy number and the Dp(11)17/+ phenotypes. Normal disomic Rai1 gene dosage was sufficient to rescue the complex physical and behavioral phenotypes observed in Dp(11)17/+ mice, despite altered trisomic copy number of the other 18 genes present in the rearranged genomic interval. These data provide a model for variation in copy number of single genes that could influence common traits such as obesity and behavior. PMID:17024248
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Common rs5918 (PlA1/A2) polymorphism in the ITGB3 gene and risk of coronary artery disease
Heidari, Mohammad Mehdi; Soheilyfar, Sorour
2016-01-01
Introduction The T to C transition at nucleotide 1565 of the human glycoprotein IIIa (ITGB3) gene represents a genetic polymorphism (PlA1/A2) that can influence both platelet activation and aggregation and that has been associated with many types of disease. Here, we present a newly designed multiplex tetra-primer amplification refractory mutation system – polymerase chain reaction (T-ARMS-PCR) for genotyping a single nucleotide polymorphism (SNP) (dbSNP ID: rs5918) in the human ITGB3 gene. Material and methods We set up T-ARMS-PCR for the rs5918 SNP in a single-step PCR and the results were validated by the PCR-RFLP method in 132 coronary artery disease (CAD) patients and 122 unrelated healthy individuals. Results Full accordance was found for genotype determination by the PCR-RFLP method. The multiple logistic regression analysis showed a significant association of the rs5918 polymorphism and CAD according to dominant and recessive models (dominant model OR: 2.40, 95% CI: 1.33–4.35; p = 0.003, recessive model OR: 4.71, 95% CI: 1.32–16.80; p = 0.0067). Conclusions Our T-ARMS-PCR in comparison with RFLP and allele-specific PCR is more advantageous because this PCR method allows the evaluation of both the wild type and the mutant allele in the same tube. Our results suggest that the rs5918 (PlA1/A2) polymorphism in the ITGB3 gene may contribute to the susceptibility of sporadic Iranian coronary artery disease (CAD) patients. PMID:28905013
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Bhattarai, Dinesh; Chen, Xing; Ur Rehman, Zia; Hao, Xingjie; Ullah, Farman; Dad, Rahim; Talpur, Hira Sajjad; Kadariya, Ishwari; Cui, Lu; Fan, Mingxia; Zhang, Shujun
2017-02-01
The objective of the studies presented in this Research Communication was to investigate the association of single nucleotide polymorphisms present in the MAP4K4 gene with different milk traits in dairy cows. Based on previous QTL fine mapping results on bovine chromosome 11, the MAP4K4 gene was selected as a candidate gene to evaluate its effect on somatic cell count and milk traits in ChineseHolstein cows. Milk production traits including milk yield, fat percentage, and protein percentage of each cow were collected using 305 d lactation records. Association between MAP4K4 genotype and different traits and Somatic Cell Score (SCS) was performed using General Linear Regression Model of R. Two SNPs at exon 18 (c.2061T > G and c.2196T > C) with genotype TT in both SNPs were found significantly higher for somatic SCS. We found the significant effect of exon 18 (c.2061T > G) on protein percentage, milk yield and SCS. We identified SNPs at different location of MAP4K4 gene of the cattle and several of them were significantly associated with the somatic cell score and other different milk traits. Thus, MAP4K4 gene could be a useful candidate gene for selection of dairy cattle against mastitis and the identified polymorphisms might potentially be strong genetic markers.
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Mauchline, Tim H.; Knox, Rachel; Mohan, Sharad; Powers, Stephen J.; Kerry, Brian R.; Davies, Keith G.; Hirsch, Penny R.
2011-01-01
Protein-encoding and 16S rRNA genes of Pasteuria penetrans populations from a wide range of geographic locations were examined. Most interpopulation single nucleotide polymorphisms (SNPs) were detected in the 16S rRNA gene. However, in order to fully resolve all populations, these were supplemented with SNPs from protein-encoding genes in a multilocus SNP typing approach. Examination of individual 16S rRNA gene sequences revealed the occurrence of “cryptic” SNPs which were not present in the consensus sequences of any P. penetrans population. Additionally, hierarchical cluster analysis separated P. penetrans 16S rRNA gene clones into four groups, and one of which contained sequences from the most highly passaged population, demonstrating that it is possible to manipulate the population structure of this fastidious bacterium. The other groups were made from representatives of the other populations in various proportions. Comparison of sequences among three Pasteuria species, namely, P. penetrans, P. hartismeri, and P. ramosa, showed that the protein-encoding genes provided greater discrimination than the 16S rRNA gene. From these findings, we have developed a toolbox for the discrimination of Pasteuria at both the inter- and intraspecies levels. We also provide a model to monitor genetic variation in other obligate hyperparasites and difficult-to-culture microorganisms. PMID:21803895
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Mauchline, Tim H; Knox, Rachel; Mohan, Sharad; Powers, Stephen J; Kerry, Brian R; Davies, Keith G; Hirsch, Penny R
2011-09-01
Protein-encoding and 16S rRNA genes of Pasteuria penetrans populations from a wide range of geographic locations were examined. Most interpopulation single nucleotide polymorphisms (SNPs) were detected in the 16S rRNA gene. However, in order to fully resolve all populations, these were supplemented with SNPs from protein-encoding genes in a multilocus SNP typing approach. Examination of individual 16S rRNA gene sequences revealed the occurrence of "cryptic" SNPs which were not present in the consensus sequences of any P. penetrans population. Additionally, hierarchical cluster analysis separated P. penetrans 16S rRNA gene clones into four groups, and one of which contained sequences from the most highly passaged population, demonstrating that it is possible to manipulate the population structure of this fastidious bacterium. The other groups were made from representatives of the other populations in various proportions. Comparison of sequences among three Pasteuria species, namely, P. penetrans, P. hartismeri, and P. ramosa, showed that the protein-encoding genes provided greater discrimination than the 16S rRNA gene. From these findings, we have developed a toolbox for the discrimination of Pasteuria at both the inter- and intraspecies levels. We also provide a model to monitor genetic variation in other obligate hyperparasites and difficult-to-culture microorganisms.
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De La Torre, Amanda R; Roberts, David R; Aitken, Sally N
2014-01-01
The maintenance of species boundaries despite interspecific gene flow has been a continuous source of interest in evolutionary biology. Many hybridizing species have porous genomes with regions impermeable to introgression, conferring reproductive barriers between species. We used ecological niche modelling to study the glacial and postglacial recolonization patterns between the widely hybridizing spruce species Picea glauca and P. engelmannii in western North America. Genome-wide estimates of admixture based on a panel of 311 candidate gene single nucleotide polymorphisms (SNP) from 290 genes were used to assess levels of admixture and introgression and to identify loci putatively involved in adaptive differences or reproductive barriers between species. Our palaeoclimatic modelling suggests that these two closely related species have a long history of hybridization and introgression, dating to at least 21 000 years ago, yet species integrity is maintained by a combination of strong environmental selection and reduced current interspecific gene flow. Twenty loci showed evidence of divergent selection, including six loci that were both Fst outliers and associated with climatic gradients, and fourteen loci that were either outliers or showed associations with climate. These included genes responsible for carbohydrate metabolism, signal transduction and transcription factors. PMID:24597663
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A wing expressed sequence tag resource for Bicyclus anynana butterflies, an evo-devo model
Beldade, Patrícia; Rudd, Stephen; Gruber, Jonathan D; Long, Anthony D
2006-01-01
Background Butterfly wing color patterns are a key model for integrating evolutionary developmental biology and the study of adaptive morphological evolution. Yet, despite the biological, economical and educational value of butterflies they are still relatively under-represented in terms of available genomic resources. Here, we describe an Expression Sequence Tag (EST) project for Bicyclus anynana that has identified the largest available collection to date of expressed genes for any butterfly. Results By targeting cDNAs from developing wings at the stages when pattern is specified, we biased gene discovery towards genes potentially involved in pattern formation. Assembly of 9,903 ESTs from a subtracted library allowed us to identify 4,251 genes of which 2,461 were annotated based on BLAST analyses against relevant gene collections. Gene prediction software identified 2,202 peptides, of which 215 longer than 100 amino acids had no homology to any known proteins and, thus, potentially represent novel or highly diverged butterfly genes. We combined gene and Single Nucleotide Polymorphism (SNP) identification by constructing cDNA libraries from pools of outbred individuals, and by sequencing clones from the 3' end to maximize alignment depth. Alignments of multi-member contigs allowed us to identify over 14,000 putative SNPs, with 316 genes having at least one high confidence double-hit SNP. We furthermore identified 320 microsatellites in transcribed genes that can potentially be used as genetic markers. Conclusion Our project was designed to combine gene and sequence polymorphism discovery and has generated the largest gene collection available for any butterfly and many potential markers in expressed genes. These resources will be invaluable for exploring the potential of B. anynana in particular, and butterflies in general, as models in ecological, evolutionary, and developmental genetics. PMID:16737530
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Basson, Jacob; Sung, Yun Ju; de Las Fuentes, Lisa; Schwander, Karen L; Vazquez, Ana; Rao, Dabeeru C
2016-01-01
Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene-smoking interactions have detected novel BP loci in cross-sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene-smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree-based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single-visit analyses. The pedigree-based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene-smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data. © 2015 WILEY PERIODICALS, INC.
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Transcriptomic correlates of neuron electrophysiological diversity
Li, Brenna; Crichlow, Cindy-Lee; Mancarci, B. Ogan; Pavlidis, Paul
2017-01-01
How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity. PMID:29069078
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Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle
2014-01-01
Background This study was conducted to: (1) identify new SNPs for residual feed intake (RFI) and performance traits within candidate genes identified in a genome wide association study (GWAS); (2) estimate the proportion of variation in RFI explained by the detected SNPs; (3) estimate the effects of detected SNPs on carcass traits to avoid undesirable correlated effects on these economically important traits when selecting for feed efficiency; and (4) map the genes to biological mechanisms and pathways. A total number of 339 SNPs corresponding to 180 genes were tested for association with phenotypes using a single locus regression (SLRM) and genotypic model on 726 and 990 crossbred animals for feed efficiency and carcass traits, respectively. Results Strong evidence of associations for RFI were located on chromosomes 8, 15, 16, 18, 19, 21, and 28. The strongest association with RFI (P = 0.0017) was found with a newly discovered SNP located on BTA 8 within the ELP3 gene. SNPs rs41820824 and rs41821600 on BTA 16 within the gene HMCN1 were strongly associated with RFI (P = 0.0064 and P = 0.0033, respectively). A SNP located on BTA 18 within the ZNF423 gene provided strong evidence for association with RFI (P = 0.0028). Genomic estimated breeding values (GEBV) from 98 significant SNPs were moderately correlated (0.47) to the estimated breeding values (EBVs) from a mixed animal model. The significant (P < 0.05) SNPs (98) explained 26% of the genetic variance for RFI. In silico functional analysis for the genes suggested 35 and 39 biological processes and pathways, respectively for feed efficiency traits. Conclusions This study identified several positional and functional candidate genes involved in important biological mechanisms associated with feed efficiency and performance. Significant SNPs should be validated in other populations to establish their potential utilization in genetic improvement programs. PMID:24476087
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Sun, Lin; Ma, Jun; Mao, Qian; Yang, Yun-Long; Ma, Lin-Lin; Niu, Ling; Liu, Li-Feng
2018-06-29
The present study was conducted to explore the correlations between single nucleotide polymorphisms (SNPs) in the calcium channel CACNA 1A, CACNA 1C, and CACNA 1H genes and diabetic peripheral neuropathy (DPN) amongst the Chinese population. In total, 281 patients diagnosed with type 2 diabetes participated in the present study. These patients were divided into the case group, which was subdivided into the DPN (143 cases) and the non-DPN groups (138 cases). Subsequently, 180 healthy individuals that had undergone routine health examinations were also recruited and assigned to the control group. PCR-restriction fragment length polymorphism (PCR-RFLP) was used to detect the genotype and allele frequencies of CACNA 1A, CACNA 1C, and CACNA 1H genes; logistic regression analysis to investigate the association of gene polymorphisms with DNP. Gene-gene interactions were then detected by generalized multifactor dimensionality reduction (GMDR). The results revealed that CACNA 1A rs2248069 and rsl6030, CACNA 1C rs216008 and rs2239050, and CACNA 1H rs3794619, and rs7191246 SNPs were all associated with DPN, while rs2248069, rsl6030, rs2239050, and rs7191246 polymorphisms were attributed to the susceptibility to DPN. It was also observed that the optimal models were three-, four- and five-dimensional models with a prediction accuracy of 61.05% and the greatest consistency of cross-validation was 10/10. In summary, these findings demonstrated that the SNPs in the CACNA 1A, CACNA 1C, and CACNA 1H genes were involved in the pathophysiology of DPN. In addition, polymorphisms in the CACNA 1A, CACNA 1C, and CACNA 1H genes and their interactions also had effects on DPN. © 2018 The Author(s).
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Kim, Joo-Sung; Artymovich, Katherine A.; Hall, David F.; Smith, Eric J.; Fulton, Richard; Bell, Julia; Dybas, Leslie; Mansfield, Linda S.; Tempelman, Robert; Wilson, David L.
2012-01-01
Human illness due to Camplyobacter jejuni infection is closely associated with consumption of poultry products. We previously demonstrated a 50 % shift in allele frequency (phase variation) in contingency gene Cj1139 (wlaN) during passage of C. jejuni NCTC11168 populations through Ross 308 broiler chickens. We hypothesized that phase variation in contingency genes during chicken passage could promote subsequent colonization and disease in humans. To test this hypothesis, we passaged C. jejuni strains NCTC11168, 33292, 81-176, KanR4 and CamR2 through broiler chickens and analysed the ability of passaged and non-passaged populations to colonize C57BL6 IL-10-deficient mice, our model for human colonization and disease. We utilized fragment analysis and nucleotide sequence analysis to measure phase variation in contingency genes. Passage through the chicken reservoir promoted phase variation in five specific contingency genes, and these ‘successful’ populations colonized mice. When phase variation did not occur in these same five contingency genes during chicken passage, these ‘unsuccessful’ populations failed to colonize mice. Phase variation during chicken passage generated small insertions or deletions (indels) in the homopolymeric tract (HT) in contingency genes. Single-colony isolates of C. jejuni strain KanR4 carrying an allele of contingency gene Cj0170 with a10G HT colonized mice at high frequency and caused disease symptoms, whereas single-colony isolates carrying the 9G allele failed to colonize mice. Supporting results were observed for the successful 9G allele of Cj0045 in strain 33292. These data suggest that phase variation in Cj0170 and Cj0045 is strongly associated with mouse colonization and disease, and that the chicken reservoir can play an active role in natural selection, phase variation and disease. PMID:22343355
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Kim, Joo-Sung; Artymovich, Katherine A; Hall, David F; Smith, Eric J; Fulton, Richard; Bell, Julia; Dybas, Leslie; Mansfield, Linda S; Tempelman, Robert; Wilson, David L; Linz, John E
2012-05-01
Human illness due to Camplyobacter jejuni infection is closely associated with consumption of poultry products. We previously demonstrated a 50 % shift in allele frequency (phase variation) in contingency gene Cj1139 (wlaN) during passage of C. jejuni NCTC11168 populations through Ross 308 broiler chickens. We hypothesized that phase variation in contingency genes during chicken passage could promote subsequent colonization and disease in humans. To test this hypothesis, we passaged C. jejuni strains NCTC11168, 33292, 81-176, KanR4 and CamR2 through broiler chickens and analysed the ability of passaged and non-passaged populations to colonize C57BL6 IL-10-deficient mice, our model for human colonization and disease. We utilized fragment analysis and nucleotide sequence analysis to measure phase variation in contingency genes. Passage through the chicken reservoir promoted phase variation in five specific contingency genes, and these 'successful' populations colonized mice. When phase variation did not occur in these same five contingency genes during chicken passage, these 'unsuccessful' populations failed to colonize mice. Phase variation during chicken passage generated small insertions or deletions (indels) in the homopolymeric tract (HT) in contingency genes. Single-colony isolates of C. jejuni strain KanR4 carrying an allele of contingency gene Cj0170 with a10G HT colonized mice at high frequency and caused disease symptoms, whereas single-colony isolates carrying the 9G allele failed to colonize mice. Supporting results were observed for the successful 9G allele of Cj0045 in strain 33292. These data suggest that phase variation in Cj0170 and Cj0045 is strongly associated with mouse colonization and disease, and that the chicken reservoir can play an active role in natural selection, phase variation and disease.
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Tang, Yidan; Lu, Baiyang; Zhu, Zhentong; Li, Bingling
2018-01-21
The polymerase chain reaction and many isothermal amplifications are able to achieve super gene amplification. Unfortunately, most commonly-used transduction methods, such as dye staining and Taqman-like probing, still suffer from shortcomings including false signals or difficult probe design, or are incompatible with multi-analysis. Here a universal and rational gene detection strategy has been established by translating isothermal amplicons to enzyme-free strand displacement circuits via three-way junction-based remote transduction. An assistant transduction probe was imported to form a partial hybrid with the target single-stranded nucleic acid. After systematic optimization the hybrid could serve as an associative trigger to activate a downstream circuit detector via a strand displacement reaction across the three-way junction. By doing so, the detection selectivity can be double-guaranteed through both amplicon-transducer recognition and the amplicon-circuit reaction. A well-optimized circuit can be immediately applied to a new target detection through simply displacing only 10-12 nt on only one component, according to the target. More importantly, this property for the first time enables multi-analysis and logic-analysis in a single reaction, sharing a single fluorescence reporter. In an applicable model, trace amounts of Cronobacter and Enterobacteria genes have been clearly distinguished from samples with no bacteria or one bacterium, with ultra-high sensitivity and selectivity.
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Pelet, T; Curran, J; Kolakofsky, D
1991-01-01
The P gene of bovine parainfluenza virus 3 (bPIV3) contains two downstream overlapping ORFs, called V and D. By comparison with the mRNA editing sites of other paramyxoviruses, two editing sites were predicted for bPIV3; site a to express the D protein, and site b to express the V protein. Examination of the bPIV3 mRNAs, however, indicates that site b is non-functional whereas site a operates frequently. Insertions at site a give rise to both V and D protein mRNAs, because a very broad distribution of Gs is added when insertions occur. This broad distribution is very different from the editing sites of Sendai virus or SV5, where predominantly one form of edited mRNA containing either a one or two G insertion respectively is created, to access the single overlapping ORF of these viruses. A model is proposed to explain how paramyxoviruses control the range of G insertions on that fraction of the mRNAs where insertions occur. The bPIV3 P gene is unique as far as we know, in that a sizeable portion of the gene expresses all 3 reading frames as protein. bPIV3 apparently does this from a single editing site by removing the constraints which control the number of slippage rounds which take place. Images PMID:1846805
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Nicholas, B; Rudrasingham, V; Nash, S; Kirov, G; Owen, M J; Wimpory, D C
2007-06-01
Clock gene anomalies have been suggested as causative factors in autism. We screened eleven clock/clock-related genes in a predominantly high-functioning Autism Genetic Resource Exchange sample of strictly diagnosed autistic disorder progeny and their parents (110 trios) for association of clock gene variants with autistic disorder. We found significant association (P<0.05) for two single-nucleotide polymorphisms in per1 and two in npas2. Analysis of all possible combinations of two-marker haplotypes for each gene showed that in npas2 40 out of the 136 possible two-marker combinations were significant at the P<0.05 level, with the best result between markers rs1811399 and rs2117714, P=0.001. Haplotype analysis within per1 gave a single significant result: a global P=0.027 for the markers rs2253820-rs885747. No two-marker haplotype was significant in any of the other genes, despite the large number of tests performed. Our findings support the hypothesis that these epistatic clock genes may be involved in the etiology of autistic disorder. Problems in sleep, memory and timing are all characteristics of autistic disorder and aspects of sleep, memory and timing are each clock-gene-regulated in other species. We identify how our findings may be relevant to theories of autism that focus on the amygdala, cerebellum, memory and temporal deficits. We outline possible implications of these findings for developmental models of autism involving temporal synchrony/social timing.
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Marian, Ali J.; van Rooij, Eva; Roberts, Robert
2016-01-01
This is the first of 2 review papers on genetics and genomics appearing as part of the series on “omics.” Genomics pertains to all components of an organism’s genes, whereas genetics involves analysis of a specific gene(s) in the context of heredity. The paper provides introductory comments, describes the basis of human genetic diversity, and addresses the phenotypic consequences of genetic variants. Rare variants with large effect sizes are responsible for single-gene disorders, whereas complex polygenic diseases are typically due to multiple genetic variants, each exerting a modest effect size. To illustrate the clinical implications of genetic variants with large effect sizes, 3 common forms of hereditary cardiomyopathies are discussed as prototypic examples of single-gene disorders, including their genetics, clinical manifestations, pathogenesis, and treatment. The genetic basis of complex traits is discussed in a separate paper. PMID:28007145
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Kawakami, Shuji; Hasegawa, Takuya; Imachi, Hiroyuki; Yamaguchi, Takashi; Harada, Hideki; Ohashi, Akiyoshi; Kubota, Kengo
2012-02-01
In situ detection of functional genes with single-cell resolution is currently of interest to microbiologists. Here, we developed a two-pass tyramide signal amplification (TSA)-fluorescence in situ hybridization (FISH) protocol with PCR-derived polynucleotide probes for the detection of single-copy genes in prokaryotic cells. The mcrA gene and the apsA gene in methanogens and sulfate-reducing bacteria, respectively, were targeted. The protocol showed bright fluorescence with a good signal-to-noise ratio and achieved a high efficiency of detection (>98%). The discrimination threshold was approximately 82-89% sequence identity. Microorganisms possessing the mcrA or apsA gene in anaerobic sludge samples were successfully detected by two-pass TSA-FISH with polynucleotide probes. The developed protocol is useful for identifying single microbial cells based on functional gene sequences. Copyright © 2011 Elsevier B.V. All rights reserved.
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Swindell, William R; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P; Voorhees, John J; Elder, James T; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P; DiGiovanni, John; Pittelkow, Mark R; Ward, Nicole L; Gudjonsson, Johann E
2011-04-04
Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis.
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Reconstructing Cell Lineages from Single-Cell Gene Expression Data: A Pilot Study
2016-08-30
Reconstructing cell lineages from single- cell gene expression data: a pilot study The goal of this pilot study is to develop novel mathematical...methods, by leveraging tools developed in the bifurcation theory, to infer the underlying cell -state dynamics from single- cell gene expression data. Our...proposed method contains two steps. The first step is to reconstruct the temporal order of the cells from gene expression data, whereas the second
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Lobato, Robert L; White, William D; Mathew, Joseph P; Newman, Mark F; Smith, Peter K; McCants, Charles B; Alexander, John H; Podgoreanu, Mihai V
2011-09-13
We tested the hypothesis that genetic variation in thrombotic and inflammatory pathways is independently associated with long-term mortality after coronary artery bypass graft (CABG) surgery. Two separate cohorts of patients undergoing CABG surgery at a single institution were examined, and all-cause mortality between 30 days and 5 years after the index CABG was ascertained from the National Death Index. In a discovery cohort of 1018 patients, a panel of 90 single-nucleotide polymorphisms (SNPs) in 49 candidate genes was tested with Cox proportional hazard models to identify clinical and genomic multivariate predictors of incident death. After adjustment for multiple comparisons and clinical predictors of mortality, the homozygote minor allele of a common variant in the thrombomodulin (THBD) gene (rs1042579) was independently associated with significantly increased risk of all-cause mortality (hazard ratio, 2.26; 95% CI, 1.31 to 3.92; P=0.003). Six tag SNPs in the THBD gene, 1 of which (rs3176123) in complete linkage disequilibrium with rs1042579, were then assessed in an independent validation cohort of 930 patients. After multivariate adjustment for the clinical predictors identified in the discovery cohort and multiple testing, the homozygote minor allele of rs3176123 independently predicted all-cause mortality (hazard ratio, 3.6; 95% CI, 1.67 to 7.78; P=0.001). In 2 independent cardiac surgery cohorts, linked common allelic variants in the THBD gene are independently associated with increased long-term mortality risk after CABG and significantly improve the classification ability of traditional postoperative mortality prediction models.
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Jia, Cheng; Hu, Yu; Kelly, Derek; Kim, Junhyong; Li, Mingyao; Zhang, Nancy R
2017-11-02
Recent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Jia, Cheng; Hu, Yu; Kelly, Derek; Kim, Junhyong
2017-01-01
Abstract Recent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq. PMID:29036714
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Microfluidic guillotine for single-cell wound repair studies
NASA Astrophysics Data System (ADS)
Blauch, Lucas R.; Gai, Ya; Khor, Jian Wei; Sood, Pranidhi; Marshall, Wallace F.; Tang, Sindy K. Y.
2017-07-01
Wound repair is a key feature distinguishing living from nonliving matter. Single cells are increasingly recognized to be capable of healing wounds. The lack of reproducible, high-throughput wounding methods has hindered single-cell wound repair studies. This work describes a microfluidic guillotine for bisecting single Stentor coeruleus cells in a continuous-flow manner. Stentor is used as a model due to its robust repair capacity and the ability to perform gene knockdown in a high-throughput manner. Local cutting dynamics reveals two regimes under which cells are bisected, one at low viscous stress where cells are cut with small membrane ruptures and high viability and one at high viscous stress where cells are cut with extended membrane ruptures and decreased viability. A cutting throughput up to 64 cells per minute—more than 200 times faster than current methods—is achieved. The method allows the generation of more than 100 cells in a synchronized stage of their repair process. This capacity, combined with high-throughput gene knockdown in Stentor, enables time-course mechanistic studies impossible with current wounding methods.
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Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.
Tong, Dong Ling; Schierz, Amanda C
2011-09-01
Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ability of the method. These methods also require the microarray data to be preprocessed before analysis takes place. The objective of this study is to develop a hybrid genetic algorithm-neural network (GANN) model that emphasises feature selection and can operate on unpreprocessed microarray data. The GANN is a hybrid model where the fitness value of the genetic algorithm (GA) is based upon the number of samples correctly labelled by a standard feedforward artificial neural network (ANN). The model is evaluated by using two benchmark microarray datasets with different array platforms and differing number of classes (a 2-class oligonucleotide microarray data for acute leukaemia and a 4-class complementary DNA (cDNA) microarray dataset for SRBCTs (small round blue cell tumours)). The underlying concept of the GANN algorithm is to select highly informative genes by co-evolving both the GA fitness function and the ANN weights at the same time. The novel GANN selected approximately 50% of the same genes as the original studies. This may indicate that these common genes are more biologically significant than other genes in the datasets. The remaining 50% of the significant genes identified were used to build predictive models and for both datasets, the models based on the set of genes extracted by the GANN method produced more accurate results. The results also suggest that the GANN method not only can detect genes that are exclusively associated with a single cancer type but can also explore the genes that are differentially expressed in multiple cancer types. The results show that the GANN model has successfully extracted statistically significant genes from the unpreprocessed microarray data as well as extracting known biologically significant genes. We also show that assessing the biological significance of genes based on classification accuracy may be misleading and though the GANN's set of extra genes prove to be more statistically significant than those selected by other methods, a biological assessment of these genes is highly recommended to confirm their functionality. Copyright © 2011 Elsevier B.V. All rights reserved.
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Creating single-copy genetic circuits
Lee, Jeong Wook; Gyorgy, Andras; Cameron, D. Ewen; Pyenson, Nora; Choi, Kyeong Rok; Way, Jeffrey C.; Silver, Pamela A.; Del Vecchio, Domitilla; Collins, James J.
2017-01-01
SUMMARY Synthetic biology is increasingly used to develop sophisticated living devices for basic and applied research. Many of these genetic devices are engineered using multi-copy plasmids, but as the field progresses from proof-of-principle demonstrations to practical applications, it is important to develop single-copy synthetic modules that minimize consumption of cellular resources and can be stably maintained as genomic integrants. Here we use empirical design, mathematical modeling and iterative construction and testing to build single-copy, bistable toggle switches with improved performance and reduced metabolic load that can be stably integrated into the host genome. Deterministic and stochastic models led us to focus on basal transcription to optimize circuit performance and helped to explain the resulting circuit robustness across a large range of component expression levels. The design parameters developed here provide important guidance for future efforts to convert functional multi-copy gene circuits into optimized single-copy circuits for practical, real-world use. PMID:27425413
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Yang, Hong; Lin, Shan; Cui, Jingru
2014-02-10
Arsenic trioxide (ATO) is presently the most active single agent in the treatment of acute promyelocytic leukemia (APL). In order to explore the molecular mechanism of ATO in leukemia cells with time series, we adopted bioinformatics strategy to analyze expression changing patterns and changes in transcription regulation modules of time series genes filtered from Gene Expression Omnibus database (GSE24946). We totally screened out 1847 time series genes for subsequent analysis. The KEGG (Kyoto encyclopedia of genes and genomes) pathways enrichment analysis of these genes showed that oxidative phosphorylation and ribosome were the top 2 significantly enriched pathways. STEM software was employed to compare changing patterns of gene expression with assigned 50 expression patterns. We screened out 7 significantly enriched patterns and 4 tendency charts of time series genes. The result of Gene Ontology showed that functions of times series genes mainly distributed in profiles 41, 40, 39 and 38. Seven genes with positive regulation of cell adhesion function were enriched in profile 40, and presented the same first increased model then decreased model as profile 40. The transcription module analysis showed that they mainly involved in oxidative phosphorylation pathway and ribosome pathway. Overall, our data summarized the gene expression changes in ATO treated K562-r cell lines with time and suggested that time series genes mainly regulated cell adhesive. Furthermore, our result may provide theoretical basis of molecular biology in treating acute promyelocytic leukemia. Copyright © 2013 Elsevier B.V. All rights reserved.
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Xie, Gary; Johnson, Shannon Lyn; Davenport, Karen Walston; ...
2017-08-29
Here, the genetic make-up of most bacteria is encoded in a single chromosome while about 10% have more than one chromosome. Among these, Vibrio cholerae, with two chromosomes, has served as a model system to study various aspects of chromosome maintenance, mainly replication, and faithful partitioning of multipartite genomes. Here, we describe the genomic characterization of strains that are an exception to the two chromosome rules: naturally occurring single-chromosome V. cholerae. Whole genome sequence analyses of NSCV1 and NSCV2 (natural single-chromosome vibrio) revealed that the Chr1 and Chr2 fusion junctions contain prophages, IS elements, and direct repeats, in addition tomore » large-scale chromosomal rearrangements such as inversions, insertions, and long tandem repeats elsewhere in the chromosome compared to prototypical two chromosome V. cholerae genomes. Many of the known cholera virulence factors are absent. The two origins of replication and associated genes are generally intact with synonymous mutations in some genes, as arerecAand mismatch repair (MMR) genes dam, mutH, and mutL; MutS function is probably impaired in NSCV2. These strains are ideal tools for studying mechanistic aspects of maintenance of chromosomes with multiple origins and other rearrangements and the biological, functional, and evolutionary significance of multipartite genome architecture in general.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Xie, Gary; Johnson, Shannon Lyn; Davenport, Karen Walston
Here, the genetic make-up of most bacteria is encoded in a single chromosome while about 10% have more than one chromosome. Among these, Vibrio cholerae, with two chromosomes, has served as a model system to study various aspects of chromosome maintenance, mainly replication, and faithful partitioning of multipartite genomes. Here, we describe the genomic characterization of strains that are an exception to the two chromosome rules: naturally occurring single-chromosome V. cholerae. Whole genome sequence analyses of NSCV1 and NSCV2 (natural single-chromosome vibrio) revealed that the Chr1 and Chr2 fusion junctions contain prophages, IS elements, and direct repeats, in addition tomore » large-scale chromosomal rearrangements such as inversions, insertions, and long tandem repeats elsewhere in the chromosome compared to prototypical two chromosome V. cholerae genomes. Many of the known cholera virulence factors are absent. The two origins of replication and associated genes are generally intact with synonymous mutations in some genes, as arerecAand mismatch repair (MMR) genes dam, mutH, and mutL; MutS function is probably impaired in NSCV2. These strains are ideal tools for studying mechanistic aspects of maintenance of chromosomes with multiple origins and other rearrangements and the biological, functional, and evolutionary significance of multipartite genome architecture in general.« less
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Ramus, Sara Mankoc; Cilensek, Ines; Petrovic, Mojca Globocnik; Soucek, Miroslav; Kruzliak, Peter; Petrovic, Daniel
2016-03-01
Oxidative stress plays an important role in the pathogenesis of diabetes and its complications. The aim of this study was to examine the possible association between seven single nucleotide polymorphisms (SNPs) of the Trx2/TXNIP and TrxR2 genes encoding proteins involved in the thioredoxin antioxidant defence system and the risk of diabetic retinopthy (DR). Cross-sectional case-control study. A total of 802 Slovenian patients with Type 2 diabetes mellitus; 277 patients with DR and 525 with no DR were enrolled. Patients genotypes of the SNPs; including rs8140110, rs7211, rs7212, rs4755, rs1548357, rs4485648 and rs5748469 were determined by the competitive allele specific PCR method. Each genotype of examined SNPs was regressed in a logistic model, assuming the co-dominant, dominant and the recessive models of inheritance with covariates of duration of diabetes, HbA1c, insulin therapy, total cholesterol and LDL cholesterol levels. In the present study, for the first time we identified an association between the rs4485648 polymorphism of the TrxR2 gene and DR in Caucasians with Type 2 DM. The estimated ORs of adjusted logistic regression models were found to be as follows: 4.4 for CT heterozygotes, 4.3 for TT homozygotes (co-dominant genetic model) and 4.4 for CT+TT genotypes (dominant genetic model). In our case-control study we were not able to demonstrate any association between rs8140110, rs7211, rs7212, rs4755, rs1548357, and rs5748469 and DR, however, our findings provide evidence that the rs4485648 polymorphism of the TrxR2 gene might exert an independent effect on the development of DR. Copyright © 2016 Elsevier Inc. All rights reserved.
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Watanabe, Manabu; Kusano, Junko; Ohtaki, Shinsaku; Ishikura, Takashi; Katayama, Jin; Koguchi, Akira; Paumen, Michael; Hayashi, Yoshiharu
2014-09-01
Combining single-cell methods and next-generation sequencing should provide a powerful means to understand single-cell biology and obviate the effects of sample heterogeneity. Here we report a single-cell identification method and seamless cancer gene profiling using semiconductor-based massively parallel sequencing. A549 cells (adenocarcinomic human alveolar basal epithelial cell line) were used as a model. Single-cell capture was performed using laser capture microdissection (LCM) with an Arcturus® XT system, and a captured single cell and a bulk population of A549 cells (≈ 10(6) cells) were subjected to whole genome amplification (WGA). For cell identification, a multiplex PCR method (AmpliSeq™ SNP HID panel) was used to enrich 136 highly discriminatory SNPs with a genotype concordance probability of 10(31-35). For cancer gene profiling, we used mutation profiling that was performed in parallel using a hotspot panel for 50 cancer-related genes. Sequencing was performed using a semiconductor-based bench top sequencer. The distribution of sequence reads for both HID and Cancer panel amplicons was consistent across these samples. For the bulk population of cells, the percentages of sequence covered at coverage of more than 100 × were 99.04% for the HID panel and 98.83% for the Cancer panel, while for the single cell percentages of sequence covered at coverage of more than 100 × were 55.93% for the HID panel and 65.96% for the Cancer panel. Partial amplification failure or randomly distributed non-amplified regions across samples from single cells during the WGA procedures or random allele drop out probably caused these differences. However, comparative analyses showed that this method successfully discriminated a single A549 cancer cell from a bulk population of A549 cells. Thus, our approach provides a powerful means to overcome tumor sample heterogeneity when searching for somatic mutations.
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Analytical approximations for spatial stochastic gene expression in single cells and tissues
Smith, Stephen; Cianci, Claudia; Grima, Ramon
2016-01-01
Gene expression occurs in an environment in which both stochastic and diffusive effects are significant. Spatial stochastic simulations are computationally expensive compared with their deterministic counterparts, and hence little is currently known of the significance of intrinsic noise in a spatial setting. Starting from the reaction–diffusion master equation (RDME) describing stochastic reaction–diffusion processes, we here derive expressions for the approximate steady-state mean concentrations which are explicit functions of the dimensionality of space, rate constants and diffusion coefficients. The expressions have a simple closed form when the system consists of one effective species. These formulae show that, even for spatially homogeneous systems, mean concentrations can depend on diffusion coefficients: this contradicts the predictions of deterministic reaction–diffusion processes, thus highlighting the importance of intrinsic noise. We confirm our theory by comparison with stochastic simulations, using the RDME and Brownian dynamics, of two models of stochastic and spatial gene expression in single cells and tissues. PMID:27146686
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Digital signaling decouples activation probability and population heterogeneity.
Kellogg, Ryan A; Tian, Chengzhe; Lipniacki, Tomasz; Quake, Stephen R; Tay, Savaş
2015-10-21
Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.
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DU, Zhi-Heng; Liu, Zong-Yue; Bai, Xiu-Juan
2010-06-01
Using single-strand conformation polymorphism (PCR-SSCP) and DNA sequencing, single nucleotide polymorphisms (SNPs) of growth hormone receptor (GHR) gene were detected in an arctic fox population. Correlation analysis between GHR polymorphisms and growth traits were carried out using the appropriate model. Four SNPs, G3A in the 5'UTR, C99T in the first exon, T59C and G65A in the fifth exon were identified on the arctic fox GHR gene. The G3A and C99T polymorphisms of GHR were associated with female fox body weight (Pamp;0.05) and the T59C and G65A polymorphisms of GHR were associated with male fox body weight (Pamp;0.05) and the skin length of the female fox (Pamp;0.01). Therefore, marker assistant selection on body weight and skin length of arctic foxes using these SNPs can be applied to get big and high quality arctic foxes.
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Davey, Mark W; Graham, Neil S; Vanholme, Bartel; Swennen, Rony; May, Sean T; Keulemans, Johan
2009-01-01
Background 'Systems-wide' approaches such as microarray RNA-profiling are ideally suited to the study of the complex overlapping responses of plants to biotic and abiotic stresses. However, commercial microarrays are only available for a limited number of plant species and development costs are so substantial as to be prohibitive for most research groups. Here we evaluate the use of cross-hybridisation to Affymetrix oligonucleotide GeneChip® microarrays to profile the response of the banana (Musa spp.) leaf transcriptome to drought stress using a genomic DNA (gDNA)-based probe-selection strategy to improve the efficiency of detection of differentially expressed Musa transcripts. Results Following cross-hybridisation of Musa gDNA to the Rice GeneChip® Genome Array, ~33,700 gene-specific probe-sets had a sufficiently high degree of homology to be retained for transcriptomic analyses. In a proof-of-concept approach, pooled RNA representing a single biological replicate of control and drought stressed leaves of the Musa cultivar 'Cachaco' were hybridised to the Affymetrix Rice Genome Array. A total of 2,910 Musa gene homologues with a >2-fold difference in expression levels were subsequently identified. These drought-responsive transcripts included many functional classes associated with plant biotic and abiotic stress responses, as well as a range of regulatory genes known to be involved in coordinating abiotic stress responses. This latter group included members of the ERF, DREB, MYB, bZIP and bHLH transcription factor families. Fifty-two of these drought-sensitive Musa transcripts were homologous to genes underlying QTLs for drought and cold tolerance in rice, including in 2 instances QTLs associated with a single underlying gene. The list of drought-responsive transcripts also included genes identified in publicly-available comparative transcriptomics experiments. Conclusion Our results demonstrate that despite the general paucity of nucleotide sequence data in Musa and only distant phylogenetic relations to rice, gDNA probe-based cross-hybridisation to the Rice GeneChip® is a highly promising strategy to study complex biological responses and illustrates the potential of such strategies for gene discovery in non-model species. PMID:19758430
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[Estimation of individual breast cancer risk: relevance and limits of risk estimation models].
De Pauw, A; Stoppa-Lyonnet, D; Andrieu, N; Asselain, B
2009-10-01
Several risk estimation models for breast or ovarian cancers have been developed these last decades. All these models take into account the family history, with different levels of sophistication. Gail model was developed in 1989 taking into account the family history (0, 1 or > or = 2 affected relatives) and several environmental factors. In 1990, Claus model was the first to integrate explicit assumptions about genetic effects, assuming a single gene dominantly inherited occurring with a low frequency in the population. BRCAPRO model, posterior to the identification of BRCA1 and BRCA2, assumes a restricted transmission with only these two dominantly inherited genes. BOADICEA model adds the effect of a polygenic component to the effect of BRCA1 and BRCA2 to explain the residual clustering of breast cancer. At last, IBIS model assumes a third dominantly inherited gene to explain this residual clustering. Moreover, this model incorporates environmental factors. We applied the Claus, BRCAPRO, BOADICEA and IBIS models to four clinical situations, corresponding to more or less heavy family histories, in order to study the consistency of the risk estimates. The three more recent models (BRCAPRO, BOADICEA and IBIS) gave the closer estimations. These estimates could be useful in clinical practice in front of complex analysis of breast and/or ovarian cancers family history.
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Structural variation within the potato Ve gene locus and correlation with molecular marker analysis
USDA-ARS?s Scientific Manuscript database
The disconnect between single genotype model systems and plant breeding using wide crosses of diverse germplasm is often too great to affect progress in understanding complex phenotypes. Whole genome sequencing allows researchers and breeders to quickly and inexpensively resequence interesting indiv...
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Côté, Olivier; Viel, Laurent; Bienzle, Dorothee
2013-12-01
Secretoglobin family 1A member 1 (SCGB 1A1) is a small anti-inflammatory and immunomodulatory protein that is abundantly secreted in airway surface fluids. We recently reported the existence of three distinct SCGB1A1 genes in the domestic horse genome as opposed to the single gene copy consensus present in other mammals. The origin of SCGB1A1 gene triplication and the evolutionary relationship of the three genes amongst Equidae family members are unknown. For this study, SCGB1A1 genomic data were collected from various Equus individuals including E. caballus, E. przewalskii, E. asinus, E. grevyi, and E. quagga. Three SCGB1A1 genes in E. przewalskii, two SCGB1A1 genes in E. asinus, and a single SCGB1A1 gene in E. grevyi and E. quagga were identified. Sequence analysis revealed that the non-synonymous nucleotide substitutions between the different equid genes coded for 17 amino acid changes. Most of these changes localized to the SCGB 1A1 central cavity that binds hydrophobic ligands, suggesting that this area of SCGB 1A1 evolved to accommodate diverse molecular interactions. Three-dimensional modeling of the proteins revealed that the size of the SCGB 1A1 central cavity is larger than that of SCGB 1A1A. Altogether, these findings suggest that evolution of the SCGB1A1 gene may parallel the separation of caballine and non-caballine species amongst Equidae, and may indicate an expansion of function for SCGB1A1 gene products. Copyright © 2013 Elsevier Inc. All rights reserved.
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Elasmobranch qPCR reference genes: a case study of hypoxia preconditioned epaulette sharks
2010-01-01
Background Elasmobranch fishes are an ancient group of vertebrates which have high potential as model species for research into evolutionary physiology and genomics. However, no comparative studies have established suitable reference genes for quantitative PCR (qPCR) in elasmobranchs for any physiological conditions. Oxygen availability has been a major force shaping the physiological evolution of vertebrates, especially fishes. Here we examined the suitability of 9 reference candidates from various functional categories after a single hypoxic insult or after hypoxia preconditioning in epaulette shark (Hemiscyllium ocellatum). Results Epaulette sharks were caught and exposed to hypoxia. Tissues were collected from 10 controls, 10 individuals with single hypoxic insult and 10 individuals with hypoxia preconditioning (8 hypoxic insults, 12 hours apart). We produced sequence information for reference gene candidates and monitored mRNA expression levels in four tissues: cerebellum, heart, gill and eye. The stability of the genes was examined with analysis of variance, geNorm and NormFinder. The best ranking genes in our study were eukaryotic translation elongation factor 1 beta (eef1b), ubiquitin (ubq) and polymerase (RNA) II (DNA directed) polypeptide F (polr2f). The performance of the ribosomal protein L6 (rpl6) was tissue-dependent. Notably, in one tissue the analysis of variance indicated statistically significant differences between treatments for genes that were ranked as the most stable candidates by reference gene software. Conclusions Our results indicate that eef1b and ubq are generally the most suitable reference genes for the conditions and tissues in the present epaulette shark studies. These genes could also be potential reference gene candidates for other physiological studies examining stress in elasmobranchs. The results emphasise the importance of inter-group variation in reference gene evaluation. PMID:20416043
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Parallel logic gates in synthetic gene networks induced by non-Gaussian noise.
Xu, Yong; Jin, Xiaoqin; Zhang, Huiqing
2013-11-01
The recent idea of logical stochastic resonance is verified in synthetic gene networks induced by non-Gaussian noise. We realize the switching between two kinds of logic gates under optimal moderate noise intensity by varying two different tunable parameters in a single gene network. Furthermore, in order to obtain more logic operations, thus providing additional information processing capacity, we obtain in a two-dimensional toggle switch model two complementary logic gates and realize the transformation between two logic gates via the methods of changing different parameters. These simulated results contribute to improve the computational power and functionality of the networks.
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No Control Genes Required: Bayesian Analysis of qRT-PCR Data
Matz, Mikhail V.; Wright, Rachel M.; Scott, James G.
2013-01-01
Background Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. Results In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Conclusions Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R. PMID:23977043
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Hybrid Compounds as Anti-infective Agents.
Sbaraglini, María Laura; Talevi, Alan
2017-01-01
Hybrid drugs are multi-target chimeric chemicals combining two or more drugs or pharmacophores covalently linked in a single molecule. In the field of anti-infective agents, they have been proposed as a possible solution to drug resistance issues, presumably having a broader spectrum of activity and less probability of eliciting high level resistance linked to single gene product. Although less frequently explored, they could also be useful in the treatment of frequently occurring co-infections. Here, we overview recent advances in the field of hybrid antimicrobials. Furthermore, we discuss some cutting-edge approaches to face the development of designed multi-target agents in the era of omics and big data, namely analysis of gene signatures and multitask QSAR models. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Gene and domain duplication in the chordate Otx gene family: insights from amphioxus Otx.
Williams, N A; Holland, P W
1998-05-01
We report the genomic organization and deduced protein sequence of a cephalochordate member of the Otx homeobox gene family (AmphiOtx) and show its probable single-copy state in the genome. We also present molecular phylogenetic analysis indicating that there was single ancestral Otx gene in the first chordates which was duplicated in the vertebrate lineage after it had split from the lineage leading to the cephalochordates. Duplication of a C-terminal protein domain has occurred specifically in the vertebrate lineage, strengthening the case for a single Otx gene in an ancestral chordate whose gene structure has been retained in an extant cephalochordate. Comparative analysis of protein sequences and published gene expression patterns suggest that the ancestral chordate Otx gene had roles in patterning the anterior mesendoderm and central nervous system. These roles were elaborated following Otx gene duplication in vertebrates, accompanied by regulatory and structural divergence, particularly of Otx1 descendant genes.
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Optimizing information flow in small genetic networks. IV. Spatial coupling
NASA Astrophysics Data System (ADS)
Sokolowski, Thomas R.; Tkačik, Gašper
2015-06-01
We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.
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Co-expression networks reveal the tissue-specific regulation of transcription and splicing.
Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis
2017-11-01
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.
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Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie
2017-01-27
Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
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Jia, Chen
2017-09-01
Here we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain can be represented as the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We give the precise mathematical conditions for the bursting kinetics of both mRNAs and proteins. It turns out that random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind the bursting kinetics as an emergent behavior from the fundamental multiscale biochemical reaction kinetics of stochastic gene expression.
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NASA Astrophysics Data System (ADS)
Jia, Chen
2017-09-01
Here we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain can be represented as the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We give the precise mathematical conditions for the bursting kinetics of both mRNAs and proteins. It turns out that random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind the bursting kinetics as an emergent behavior from the fundamental multiscale biochemical reaction kinetics of stochastic gene expression.
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[Preimplantation genetic diagnosis of Duchenne muscular dystrophy by single cell triplex PCR].
Wu, Yue-Li; Wu, Ling-Qian; Li, Yan-Ping; Liu, Dong-E; Zeng, Qiao; Zhu, Hai-Yan; Pan, Qian; Liang, De-Sheng; Hu, Hao; Long, Zhi-Gao; Li, Juan; Dai, He-Ping; Xia, Kun; Xia, Jia-Hui
2007-04-01
To detect two exons of Duchenne muscular dystrophy (DMD) gene and a gender discrimination locus amelogenin gene by single cell triplex PCR, and to evaluate the possibility of this technique for preimplantation genetic diagnosis (PGD) in DMD family with DMD deletion mutation. Single lymphocytes from a normal male, a normal female, two DMD patients (exon 8 and 47 deleted, respectively) and single blastomeres from the couples treated by the in vitro fertilization pre-embryo transfer (IVF-ET) and without family history of DMD were obtained. Exons 8 and 47 of DMD gene were amplified by a triplex PCR assay, the amelogenin gene on X and Y chromosomes were co-amplified to analyze the correlation between embryo gender and deletion status. In the normal single lymphocytes, the amplification rate of exons 8 and 47 of DMD and amelogenin gene were 93.8%, 93.8%, and 95.3% respectively. The false positive rate was 3.3%. In the exon 8 deleted DMD patient, the amplification rate of exon 47 of DMD and amelogenin gene was 95.8%, and the false positive rate was 3.3%. In the exon 47 deleted DMD patient, the amplification rate of exon 8 of DMD and amelogenin gene was 95.8%, and the false positive rate was 0. In the single blastomeres, the amplification rate of exons 8 and 47 of DMD and amelogenin gene was 82.5%, 80.0% and 77.5%, respectively, and the false positive rate was 0. The single cell triplex PCR protocol for the detection of DMD and amelogenin gene is highly sensitive, specific and reliable, and can be used for PGD in those DMD families with DMD deletion mutation.
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de Arruda, Henrique Ferraz; Comin, Cesar Henrique; Miazaki, Mauro; Viana, Matheus Palhares; Costa, Luciano da Fontoura
2015-04-30
A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. To our best understanding, there are no similar analyses to compare with. A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity. Copyright © 2015. Published by Elsevier B.V.
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Mets, David G; Brainard, Michael S
2018-01-01
Abstract Background Vocal learning in songbirds has emerged as a powerful model for sensorimotor learning. Neurobehavioral studies of Bengalese finch (Lonchura striata domestica) song, naturally more variable and plastic than songs of other finch species, have demonstrated the importance of behavioral variability for initial learning, maintenance, and plasticity of vocalizations. However, the molecular and genetic underpinnings of this variability and the learning it supports are poorly understood. Findings To establish a platform for the molecular analysis of behavioral variability and plasticity, we generated an initial draft assembly of the Bengalese finch genome from a single male animal to 151× coverage and an N50 of 3.0 MB. Furthermore, we developed an initial set of gene models using RNA-seq data from 8 samples that comprise liver, muscle, cerebellum, brainstem/midbrain, and forebrain tissue from juvenile and adult Bengalese finches of both sexes. Conclusions We provide a draft Bengalese finch genome and gene annotation to facilitate the study of the molecular-genetic influences on behavioral variability and the process of vocal learning. These data will directly support many avenues for the identification of genes involved in learning, including differential expression analysis, comparative genomic analysis (through comparison to existing avian genome assemblies), and derivation of genetic maps for linkage analysis. Bengalese finch gene models and sequences will be essential for subsequent manipulation (molecular or genetic) of genes and gene products, enabling novel mechanistic investigations into the role of variability in learned behavior. PMID:29618046
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Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models.
Davis, Brian W; Seabury, Christopher M; Brashear, Wesley A; Li, Gang; Roelke-Parker, Melody; Murphy, William J
2015-10-01
The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented "rule of speciation." We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the "Large X-Effect" in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Colquitt, Bradley M; Mets, David G; Brainard, Michael S
2018-03-01
Vocal learning in songbirds has emerged as a powerful model for sensorimotor learning. Neurobehavioral studies of Bengalese finch (Lonchura striata domestica) song, naturally more variable and plastic than songs of other finch species, have demonstrated the importance of behavioral variability for initial learning, maintenance, and plasticity of vocalizations. However, the molecular and genetic underpinnings of this variability and the learning it supports are poorly understood. To establish a platform for the molecular analysis of behavioral variability and plasticity, we generated an initial draft assembly of the Bengalese finch genome from a single male animal to 151× coverage and an N50 of 3.0 MB. Furthermore, we developed an initial set of gene models using RNA-seq data from 8 samples that comprise liver, muscle, cerebellum, brainstem/midbrain, and forebrain tissue from juvenile and adult Bengalese finches of both sexes. We provide a draft Bengalese finch genome and gene annotation to facilitate the study of the molecular-genetic influences on behavioral variability and the process of vocal learning. These data will directly support many avenues for the identification of genes involved in learning, including differential expression analysis, comparative genomic analysis (through comparison to existing avian genome assemblies), and derivation of genetic maps for linkage analysis. Bengalese finch gene models and sequences will be essential for subsequent manipulation (molecular or genetic) of genes and gene products, enabling novel mechanistic investigations into the role of variability in learned behavior.
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Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
Dueck, Hannah; Khaladkar, Mugdha; Kim, Tae Kyung; Spaethling, Jennifer M; Francis, Chantal; Suresh, Sangita; Fisher, Stephen A; Seale, Patrick; Beck, Sheryl G; Bartfai, Tamas; Kuhn, Bernhard; Eberwine, James; Kim, Junhyong
2015-06-09
Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.
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Transcriptional alterations in the left ventricle of three hypertensive rat models.
Cerutti, Catherine; Kurdi, Mazen; Bricca, Giampiero; Hodroj, Wassim; Paultre, Christian; Randon, Jacques; Gustin, Marie-Paule
2006-11-27
Left ventricular hypertrophy (LVH) is commonly associated with hypertension and represents an independent cardiovascular risk factor. The aim of this study was to test the hypothesis that the cardiac overload related to hypertension is associated to a specific gene expression pattern independently of genetic background. Gene expression levels were obtained with microarrays for 15,866 transcripts from RNA of left ventricles from 12-wk-old rats of three hypertensive models [spontaneously hypertensive rat (SHR), Lyon hypertensive rat (LH), and heterozygous TGR(mRen2)27 rat] and their respective controls. More than 60% of the detected transcripts displayed significant changes between the three groups of normotensive rats, showing large interstrain variability. Expression data were analyzed with respect to hypertension, LVH, and chromosomal distribution. Only four genes had significantly modified expression in the three hypertensive models among which a single gene, coding for sialyltransferase 7A, was consistently overexpressed. Correlation analysis between expression data and left ventricular mass index (LVMI) over all rats identified a larger set of genes whose expression was continuously related with LVMI, including known genes associated with cardiac remodeling. Positioning the detected transcripts along the chromosomes pointed out high-density regions mostly located within blood pressure and cardiac mass quantitative trait loci. Although our study could not detect a unique reprogramming of cardiac cells involving specific genes at early stage of LVH, it allowed the identification of some genes associated with LVH regardless of genetic background. This study thus provides a set of potentially important genes contained within restricted chromosomal regions involved in cardiovascular diseases.
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Noise in gene expression is coupled to growth rate.
Keren, Leeat; van Dijk, David; Weingarten-Gabbay, Shira; Davidi, Dan; Jona, Ghil; Weinberger, Adina; Milo, Ron; Segal, Eran
2015-12-01
Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle-regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications. © 2015 Keren et al.; Published by Cold Spring Harbor Laboratory Press.
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Noise in gene expression is coupled to growth rate
Keren, Leeat; van Dijk, David; Weingarten-Gabbay, Shira; Davidi, Dan; Jona, Ghil; Weinberger, Adina; Milo, Ron; Segal, Eran
2015-01-01
Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle–regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications. PMID:26355006
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2018-01-01
ABSTRACT Primary infection with human cytomegalovirus (HCMV) results in a lifelong infection due to its ability to establish latent infection, with one characterized viral reservoir being hematopoietic cells. Although reactivation from latency causes serious disease in immunocompromised individuals, our molecular understanding of latency is limited. Here, we delineate viral gene expression during natural HCMV persistent infection by analyzing the massive transcriptome RNA sequencing (RNA-seq) atlas generated by the Genotype-Tissue Expression (GTEx) project. This systematic analysis reveals that HCMV persistence in vivo is prevalent in diverse tissues. Notably, we find only viral transcripts that resemble gene expression during various stages of lytic infection with no evidence of any highly restricted latency-associated viral gene expression program. To further define the transcriptional landscape during HCMV latent infection, we also used single-cell RNA-seq and a tractable experimental latency model. In contrast to some current views on latency, we also find no evidence for any highly restricted latency-associated viral gene expression program. Instead, we reveal that latency-associated gene expression largely mirrors a late lytic viral program, albeit at much lower levels of expression. Overall, our work has the potential to revolutionize our understanding of HCMV persistence and suggests that latency is governed mainly by quantitative changes, with a limited number of qualitative changes, in viral gene expression. PMID:29535194
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Regularized rare variant enrichment analysis for case-control exome sequencing data.
Larson, Nicholas B; Schaid, Daniel J
2014-02-01
Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next-generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base-level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here, we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene-level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene-based least absolute shrinkage and selection operator (LASSO) and exon-based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene-centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research. © 2013 WILEY PERIODICALS, INC.
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Xie, Jianbo; Shi, Haowen; Du, Zhenglin; Wang, Tianshu; Liu, Xiaomeng; Chen, Sanfeng
2016-01-01
Paenibacillus polymyxa has widely been studied as a model of plant-growth promoting rhizobacteria (PGPR). Here, the genome sequences of 9 P. polymyxa strains, together with 26 other sequenced Paenibacillus spp., were comparatively studied. Phylogenetic analysis of the concatenated 244 single-copy core genes suggests that the 9 P. polymyxa strains and 5 other Paenibacillus spp., isolated from diverse geographic regions and ecological niches, formed a closely related clade (here it is called Poly-clade). Analysis of single nucleotide polymorphisms (SNPs) reveals local diversification of the 14 Poly-clade genomes. SNPs were not evenly distributed throughout the 14 genomes and the regions with high SNP density contain the genes related to secondary metabolism, including genes coding for polyketide. Recombination played an important role in the genetic diversity of this clade, although the rate of recombination was clearly lower than mutation. Some genes relevant to plant-growth promoting traits, i.e. phosphate solubilization and IAA production, are well conserved, while some genes relevant to nitrogen fixation and antibiotics synthesis are evolved with diversity in this Poly-clade. This study reveals that both P. polymyxa and its closely related species have plant growth promoting traits and they have great potential uses in agriculture and horticulture as PGPR. PMID:26856413
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An autonomous molecular computer for logical control of gene expression.
Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud
2004-05-27
Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.
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Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation
Micali, Gabriele; Aquino, Gerardo; Richards, David M.; Endres, Robert G.
2015-01-01
Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms. PMID:26030820
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Efficient Credit Assignment through Evaluation Function Decomposition
NASA Technical Reports Server (NTRS)
Agogino, Adrian; Turner, Kagan; Mikkulainen, Risto
2005-01-01
Evolutionary methods are powerful tools in discovering solutions for difficult continuous tasks. When such a solution is encoded over multiple genes, a genetic algorithm faces the difficult credit assignment problem of evaluating how a single gene in a chromosome contributes to the full solution. Typically a single evaluation function is used for the entire chromosome, implicitly giving each gene in the chromosome the same evaluation. This method is inefficient because a gene will get credit for the contribution of all the other genes as well. Accurately measuring the fitness of individual genes in such a large search space requires many trials. This paper instead proposes turning this single complex search problem into a multi-agent search problem, where each agent has the simpler task of discovering a suitable gene. Gene-specific evaluation functions can then be created that have better theoretical properties than a single evaluation function over all genes. This method is tested in the difficult double-pole balancing problem, showing that agents using gene-specific evaluation functions can create a successful control policy in 20 percent fewer trials than the best existing genetic algorithms. The method is extended to more distributed problems, achieving 95 percent performance gains over tradition methods in the multi-rover domain.
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Adaptability of non-genetic diversity in bacterial chemotaxis
Frankel, Nicholas W; Pontius, William; Dufour, Yann S; Long, Junjiajia; Hernandez-Nunez, Luis; Emonet, Thierry
2014-01-01
Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability. DOI: http://dx.doi.org/10.7554/eLife.03526.001 PMID:25279698
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Of Men and Mice: Modeling the Fragile X Syndrome
Dahlhaus, Regina
2018-01-01
The Fragile X Syndrome (FXS) is one of the most common forms of inherited intellectual disability in all human societies. Caused by the transcriptional silencing of a single gene, the fragile x mental retardation gene FMR1, FXS is characterized by a variety of symptoms, which range from mental disabilities to autism and epilepsy. More than 20 years ago, a first animal model was described, the Fmr1 knock-out mouse. Several other models have been developed since then, including conditional knock-out mice, knock-out rats, a zebrafish and a drosophila model. Using these model systems, various targets for potential pharmaceutical treatments have been identified and many treatments have been shown to be efficient in preclinical studies. However, all attempts to turn these findings into a therapy for patients have failed thus far. In this review, I will discuss underlying difficulties and address potential alternatives for our future research. PMID:29599705
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Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.
Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich
2004-03-01
By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.
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Zagrijchuk, Elizaveta A.; Sabirov, Marat A.; Holloway, David M.; Spirov, Alexander V.
2014-01-01
Biological development depends on the coordinated expression of genes in time and space. Developmental genes have extensive cis-regulatory regions which control their expression. These regions are organized in a modular manner, with different modules controlling expression at different times and locations. Both how modularity evolved and what function it serves are open questions. We present a computational model for the cis-regulation of the hunchback (hb) gene in the fruit fly (Drosophila). We simulate evolution (using an evolutionary computation approach from computer science) to find the optimal cis-regulatory arrangements for fitting experimental hb expression patterns. We find that the cis-regulatory region tends to readily evolve modularity. These cis-regulatory modules (CRMs) do not tend to control single spatial domains, but show a multi-CRM/multi-domain correspondence. We find that the CRM-domain correspondence seen in Drosophila evolves with a high probability in our model, supporting the biological relevance of the approach. The partial redundancy resulting from multi-CRM control may confer some biological robustness against corruption of regulatory sequences. The technique developed on hb could readily be applied to other multi-CRM developmental genes. PMID:24712536
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3D chromosome rendering from Hi-C data using virtual reality
NASA Astrophysics Data System (ADS)
Zhu, Yixin; Selvaraj, Siddarth; Weber, Philip; Fang, Jennifer; Schulze, Jürgen P.; Ren, Bing
2015-01-01
Most genome browsers display DNA linearly, using single-dimensional depictions that are useful to examine certain epigenetic mechanisms such as DNA methylation. However, these representations are insufficient to visualize intrachromosomal interactions and relationships between distal genome features. Relationships between DNA regions may be difficult to decipher or missed entirely if those regions are distant in one dimension but could be spatially proximal when mapped to three-dimensional space. For example, the visualization of enhancers folding over genes is only fully expressed in three-dimensional space. Thus, to accurately understand DNA behavior during gene expression, a means to model chromosomes is essential. Using coordinates generated from Hi-C interaction frequency data, we have created interactive 3D models of whole chromosome structures and its respective domains. We have also rendered information on genomic features such as genes, CTCF binding sites, and enhancers. The goal of this article is to present the procedure, findings, and conclusions of our models and renderings.
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Functional analysis of regulatory single-nucleotide polymorphisms.
Pampín, Sandra; Rodríguez-Rey, José C
2007-04-01
The identification of regulatory polymorphisms has become a key problem in human genetics. In the past few years there has been a conceptual change in the way in which regulatory single-nucleotide polymorphisms are studied. We revise the new approaches and discuss how gene expression studies can contribute to a better knowledge of the genetics of common diseases. New techniques for the association of single-nucleotide polymorphisms with changes in gene expression have been recently developed. This, together with a more comprehensive use of the old in-vitro methods, has produced a great amount of genetic information. When added to current databases, it will help to design better tools for the detection of regulatory single-nucleotide polymorphisms. The identification of functional regulatory single-nucleotide polymorphisms cannot be done by the simple inspection of DNA sequence. In-vivo techniques, based on primer-extension, and the more recently developed 'haploChIP' allow the association of gene variants to changes in gene expression. Gene expression analysis by conventional in-vitro techniques is the only way to identify the functional consequences of regulatory single-nucleotide polymorphisms. The amount of information produced in the last few years will help to refine the tools for the future analysis of regulatory gene variants.
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Mean field analysis of a spatial stochastic model of a gene regulatory network.
Sturrock, M; Murray, P J; Matzavinos, A; Chaplain, M A J
2015-10-01
A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products. In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.
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Passive larval transport explains recent gene flow in a Mediterranean gorgonian
NASA Astrophysics Data System (ADS)
Padrón, Mariana; Costantini, Federica; Baksay, Sandra; Bramanti, Lorenzo; Guizien, Katell
2018-06-01
Understanding the patterns of connectivity is required by the Strategic Plan for Biodiversity 2011-2020 and will be used to guide the extension of marine protection measures. Despite the increasing accuracy of ocean circulation modelling, the capacity to model the population connectivity of sessile benthic species with dispersal larval stages can be limited due to the potential effect of filters acting before or after dispersal, which modulates offspring release or settlement, respectively. We applied an interdisciplinary approach that combined demographic surveys, genetic methods (assignment tests and coalescent-based analyses) and larval transport simulations to test the relative importance of demographics and ocean currents in shaping the recent patterns of gene flow among populations of a Mediterranean gorgonian ( Eunicella singularis) in a fragmented rocky habitat (Gulf of Lion, NW Mediterranean Sea). We show that larval transport is a dominant driver of recent gene flow among the populations, and significant correlations were found between recent gene flow and larval transport during an average single dispersal event when the pelagic larval durations (PLDs) ranged from 7 to 14 d. Our results suggest that PLDs that efficiently connect populations distributed over a fragmented habitat are filtered by the habitat layout within the species competency period. Moreover, a PLD ranging from 7 to 14 d is sufficient to connect the fragmented rocky substrate of the Gulf of Lion. The rocky areas located in the centre of the Gulf of Lion, which are currently not protected, were identified as essential hubs for the distribution of migrants in the region. We encourage the use of a range of PLDs instead of a single value when estimating larval transport with biophysical models to identify potential connectivity patterns among a network of Marine Protected Areas or even solely a seascape.
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USDA-ARS?s Scientific Manuscript database
Molecular factors enabling microbial pathogens to cause plant diseases have been sought with increasing efficacy over three research eras, which successively introduced the tools of disease physiology, single-gene molecular genetics, and genomics. From this work emerged a unified model of the intera...
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USDA-ARS?s Scientific Manuscript database
Cotton fibers represent the largest single cell in the plant kingdom, and they have been used as a model to study cell function, differentiation, maturation, and cell death. The cotton fiber transcriptome can be clustered into two genomic regions: conserved and recombination hotspots. Genetic link...
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Genomic landscape of fiber genes in fibered and non-fibered cottons
USDA-ARS?s Scientific Manuscript database
Cotton fiber is the largest single cell in the plant kingdom. It is the best model to study cell function, differentiation, maturation, and cell death. Cotton fiber transcriptome can be clustered into two types of regions: conservative areas and recombination hotspots. This study was to investig...
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Chean, Jennifer; Chen, Charng-Jui; Shively, John E
2017-10-01
The loss of expression of a single gene can revert normal tissue to a malignant phenotype. For example, while normal breast has high lumenal expression of CEACAM1, the majority of breast cancers exhibit the early loss of this gene with the concurrent loss of their lumenal phenotype. MCF7 cells that lack CEACAM1 expression and fail to form lumena in 3D culture, regain the normal phenotype when transfected with CEACAM1. In order to probe the mechanism of this gain of function, we treated these cells with the clinically relevant Jak2 inhibitor TG101348 (TG), expecting that disruption of the prolactin receptor signaling pathway would interfere with the positive effects of transfection of MCF7 cells with CEACAM1. Indeed, lumen formation was inhibited, resulting in the down regulation of a set of genes, likely involved in the complex process of lumen formation. As expected, inhibition of the expression of many of these genes also inhibited lumen formation, confirming their involvement in a single pathway. Among the genes identified by the inhibition assay, ETS transcription factor ELF5 stood out, since it has been identified as a master regulator of mammary morphogenesis, and is associated with prolactin receptor signaling. When ELF5 was transfected into the parental MCF7 cells that lack CEACAM1, lumen formation was restored, indicating that ELF5 can replace CEACAM1 in this model system of lumenogenesis. We conclude that the event(s) that led to the loss of expression of CEACAM1 is epistatic in that multiple genes associated with a critical pathway were affected, but that restoration of the normal phenotype can be achieved with reactivation of certain genes at various nodal points in tissue morphogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.
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Romiguier, Jonathan; Cameron, Sydney A; Woodard, S Hollis; Fischman, Brielle J; Keller, Laurent; Praz, Christophe J
2016-03-01
As increasingly large molecular data sets are collected for phylogenomics, the conflicting phylogenetic signal among gene trees poses challenges to resolve some difficult nodes of the Tree of Life. Among these nodes, the phylogenetic position of the honey bees (Apini) within the corbiculate bee group remains controversial, despite its considerable importance for understanding the emergence and maintenance of eusociality. Here, we show that this controversy stems in part from pervasive phylogenetic conflicts among GC-rich gene trees. GC-rich genes typically have a high nucleotidic heterogeneity among species, which can induce topological conflicts among gene trees. When retaining only the most GC-homogeneous genes or using a nonhomogeneous model of sequence evolution, our analyses reveal a monophyletic group of the three lineages with a eusocial lifestyle (honey bees, bumble bees, and stingless bees). These phylogenetic relationships strongly suggest a single origin of eusociality in the corbiculate bees, with no reversal to solitary living in this group. To accurately reconstruct other important evolutionary steps across the Tree of Life, we suggest removing GC-rich and GC-heterogeneous genes from large phylogenomic data sets. Interpreted as a consequence of genome-wide variations in recombination rates, this GC effect can affect all taxa featuring GC-biased gene conversion, which is common in eukaryotes. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Segregation analysis of urothelial cell carcinoma.
Aben, Katja K H; Baglietto, Laura; Baffoe-Bonnie, Agnes; Coebergh, Jan-Willem W; Bailey-Wilson, Joan E; Trink, Barry; Verbeek, André L M; Schoenberg, Mark P; Alfred Witjes, J; Kiemeney, Lambertus A
2006-07-01
A family history of urothelial cell carcinoma (UCC) confers an almost two-fold increased risk of developing UCC. It is unknown whether (part of) this aggregation of UCC has a Mendelian background. We performed complex segregation analyses on 1193 families ascertained through a proband with UCC of the bladder, ureter, renal pelvis or urethra, who were newly diagnosed between January 1, 1995 and December 31, 1997 and registered by two population-based cancer registries in the southeastern part of the Netherlands. Data were reported on 10 738 first-degree relatives by postal questionnaire; 101 of these relatives had UCC. All reported occurrences of UCC were verified (if possible) using medical records. Analyses were performed with the S.A.G.E. segregation package. Five restricted models (Mendelian dominant, Mendelian recessive, Mendelian co-dominant, 'no major gene' model and environmental model) were tested against the general unrestricted model. Sex and smoking status were incorporated as covariates. Strong evidence of Mendelian inheritance of UCC through a single major gene was not found in these 1 193 families. However, since none of the Mendelian models could be rejected, an inherited subtype of UCC cannot be excluded. A major gene may segregate in some families but this effect may have been masked in a background of high sporadic incidence. The 'no major gene' (or sporadic) model appeared to be the most parsimonious one to describe the occurrence of UCC in these families.
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Limit cycles in piecewise-affine gene network models with multiple interaction loops
NASA Astrophysics Data System (ADS)
Farcot, Etienne; Gouzé, Jean-Luc
2010-01-01
In this article, we consider piecewise affine differential equations modelling gene networks. We work with arbitrary decay rates, and under a local hypothesis expressed as an alignment condition of successive focal points. The interaction graph of the system may be rather complex (multiple intricate loops of any sign, multiple thresholds, etc.). Our main result is an alternative theorem showing that if a sequence of region is periodically visited by trajectories, then under our hypotheses, there exists either a unique stable periodic solution, or the origin attracts all trajectories in this sequence of regions. This result extends greatly our previous work on a single negative feedback loop. We give several examples and simulations illustrating different cases.
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Wollman, Adam J M; Leake, Mark C
2015-01-01
We present a single-molecule tool called the CoPro (concentration of proteins) method that uses millisecond imaging with convolution analysis, automated image segmentation and super-resolution localization microscopy to generate robust estimates for protein concentration in different compartments of single living cells, validated using realistic simulations of complex multiple compartment cell types. We demonstrate its utility experimentally on model Escherichia coli bacteria and Saccharomyces cerevisiae budding yeast cells, and use it to address the biological question of how signals are transduced in cells. Cells in all domains of life dynamically sense their environment through signal transduction mechanisms, many involving gene regulation. The glucose sensing mechanism of S. cerevisiae is a model system for studying gene regulatory signal transduction. It uses the multi-copy expression inhibitor of the GAL gene family, Mig1, to repress unwanted genes in the presence of elevated extracellular glucose concentrations. We fluorescently labelled Mig1 molecules with green fluorescent protein (GFP) via chromosomal integration at physiological expression levels in living S. cerevisiae cells, in addition to the RNA polymerase protein Nrd1 with the fluorescent protein reporter mCherry. Using CoPro we make quantitative estimates of Mig1 and Nrd1 protein concentrations in the cytoplasm and nucleus compartments on a cell-by-cell basis under physiological conditions. These estimates indicate a ∼4-fold shift towards higher values in the concentration of diffusive Mig1 in the nucleus if the external glucose concentration is raised, whereas equivalent levels in the cytoplasm shift to smaller values with a relative change an order of magnitude smaller. This compares with Nrd1 which is not involved directly in glucose sensing, and which is almost exclusively localized in the nucleus under high and low external glucose levels. CoPro facilitates time-resolved quantification of protein concentrations in single functional cells, and enables the distributions of concentrations across a cell population to be measured. This could be useful in investigating several cellular processes that are mediated by proteins, especially where changes in protein concentration in a single cell in response to changes in the extracellular chemical environment are subtle and rapid and may be smaller than the variability across a cell population.
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Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch.
Blanchard, Andrew E; Liao, Chen; Lu, Ting
2018-02-06
Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
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Precision Genome Editing for Treating Single-gene Disorders
NASA Astrophysics Data System (ADS)
Bao, Gang
There are an estimated 6,000 human single-gene disorders, most of them have no cure. This imposes a significant burden on human health worldwide. The recent advent in developing engineered nucleases, especially CRISPR/Cas9 (clustered, regularly interspaced, short palindromic repeats and CRISPR-associated protein 9) systems provides a powerful tool for precisely modifying the human genome, thus revolutionizing the treatment of single-gene disorders. In this talk, I will present recent work in my lab on developing new tools and methods for the design and optimization of CRISPR/Cas9 systems, and the efforts in developing a clinically applicable gene correction strategy for treating sickle cell disease (SCD), which is the first single-gene disorder with molecular understanding. We optimized CRISPR/Cas9 systems targeting the beta-globin gene, and systematically evaluated their on- and off-target cleavage in different cells. We also quantified the nuclease-induced gene modification rates in CD34+ cells from SCD patients, and demonstrated that CRISPR/Cas9 based genome editing is effective in generating normal hemoglobin (HbA) and reducing sickling hemoglobin (HbS). These studies significantly facilitated our pre-clinical investigation of SCD treatment using CRISPR/Cas9 and donor templates. The opportunities and challenges in developing nuclease-based genome editing strategies for treating single-gene disorders are discussed.
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Li, Qike; Schissler, A Grant; Gardeux, Vincent; Achour, Ikbel; Kenost, Colleen; Berghout, Joanne; Li, Haiquan; Zhang, Hao Helen; Lussier, Yves A
2017-05-24
Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.
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Estimating diversifying selection and functional constraint in the presence of recombination.
Wilson, Daniel J; McVean, Gilean
2006-03-01
Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques.
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Estimating Diversifying Selection and Functional Constraint in the Presence of Recombination
Wilson, Daniel J.; McVean, Gilean
2006-01-01
Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques. PMID:16387887
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Eronen, Lauri; Toivonen, Hannu
2012-06-06
Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.
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Wagner-Schuman, Melissa; Neitz, Jay; Rha, Jungtae; Williams, David R.; Neitz, Maureen; Carroll, Joseph
2010-01-01
Our understanding of the etiology of red-green color vision defects is evolving. While missense mutations within the long- (L-) and middle-wavelength sensitive (M-) photopigments and gross rearrangements within the L/M-opsin gene array are commonly associated with red-green defects, recent work using adaptive optics retinal imaging has shown that different genotypes can have distinct consequences for the cone mosaic. Here we examined the cone mosaic in red-green color deficient individuals with multiple X-chromosome opsin genes that encode L opsin, as well as individuals with a single X-chromosome opsin gene that encodes L opsin and a single patient with a novel premature termination codon in his M-opsin gene and a normal L-opsin gene. We observed no difference in cone density between normal trichomats and multiple or single gene dichromats. In addition, we demonstrate different phenotypic effects of a nonsense mutation versus the previously described deleterious polymorphism, (LIAVA), both of which differ from multiple and single gene dichromats. Our results help refine the relationship between opsin genotype and cone photoreceptor mosaic phenotype. PMID:20854834
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Simulated maximum likelihood method for estimating kinetic rates in gene expression.
Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin
2007-01-01
Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.
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Singh, Upinder; Brewer, Jeremy L; Boothroyd, John C
2002-05-01
Developmental switching in Toxoplasma gondii, from the virulent tachyzoite to the relatively quiescent bradyzoite stage, is responsible for disease propagation and reactivation. We have generated tachyzoite to bradyzoite differentiation (Tbd-) mutants in T. gondii and used these in combination with a cDNA microarray to identify developmental pathways in bradyzoite formation. Four independently generated Tbd- mutants were analysed and had defects in bradyzoite development in response to multiple bradyzoite-inducing conditions, a stable phenotype after in vivo passages and a markedly reduced brain cyst burden in a murine model of chronic infection. Transcriptional profiles of mutant and wild-type parasites, growing under bradyzoite conditions, revealed a hierarchy of developmentally regulated genes, including many bradyzoite-induced genes whose transcripts were reduced in all mutants. A set of non-developmentally regulated genes whose transcripts were less abundant in Tbd- mutants were also identified. These may represent genes that mediate downstream effects and/or whose expression is dependent on the same transcription factors as the bradyzoite-induced set. Using these data, we have generated a model of transcription regulation during bradyzoite development in T. gondii. Our approach shows the utility of this system as a model to study developmental biology in single-celled eukaryotes including protozoa and fungi.
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Mazo Lopera, Mauricio A; Coombes, Brandon J; de Andrade, Mariza
2017-09-27
Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.
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Equalizer reduces SNP bias in Affymetrix microarrays.
Quigley, David
2015-07-30
Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans. By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings. The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.
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Kao, Katy C.; Schwartz, Katja; Sherlock, Gavin
2010-01-01
The Dobzhansky-Muller (D-M) model of speciation by genic incompatibility is widely accepted as the primary cause of interspecific postzygotic isolation. Since the introduction of this model, there have been theoretical and experimental data supporting the existence of such incompatibilities. However, speciation genes have been largely elusive, with only a handful of candidate genes identified in a few organisms. The Saccharomyces sensu stricto yeasts, which have small genomes and can mate interspecifically to produce sterile hybrids, are thus an ideal model for studying postzygotic isolation. Among them, only a single D-M pair, comprising a mitochondrially targeted product of a nuclear gene and a mitochondrially encoded locus, has been found. Thus far, no D-M pair of nuclear genes has been identified between any sensu stricto yeasts. We report here the first detailed genome-wide analysis of rare meiotic products from an otherwise sterile hybrid and show that no classic D-M pairs of speciation genes exist between the nuclear genomes of the closely related yeasts S. cerevisiae and S. paradoxus. Instead, our analyses suggest that more complex interactions, likely involving multiple loci having weak effects, may be responsible for their post-zygotic separation. The lack of a nuclear encoded classic D-M pair between these two yeasts, yet the existence of multiple loci that may each exert a small effect through complex interactions suggests that initial speciation events might not always be mediated by D-M pairs. An alternative explanation may be that the accumulation of polymorphisms leads to gamete inviability due to the activities of anti-recombination mechanisms and/or incompatibilities between the species' transcriptional and metabolic networks, with no single pair at least initially being responsible for the incompatibility. After such a speciation event, it is possible that one or more D-M pairs might subsequently arise following isolation. PMID:20686707
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Remission in models of type 1 diabetes by gene therapy using a single-chain insulin analogue
NASA Astrophysics Data System (ADS)
Lee, Hyun Chul; Kim, Su-Jin; Kim, Kyung-Sup; Shin, Hang-Cheol; Yoon, Ji-Won
2000-11-01
A cure for diabetes has long been sought using several different approaches, including islet transplantation, regeneration of β cells and insulin gene therapy. However, permanent remission of type 1 diabetes has not yet been satisfactorily achieved. The development of type 1 diabetes results from the almost total destruction of insulin-producing pancreatic β cells by autoimmune responses specific to β cells. Standard insulin therapy may not maintain blood glucose concentrations within the relatively narrow range that occurs in the presence of normal pancreatic β cells. We used a recombinant adeno-associated virus (rAAV) that expresses a single-chain insulin analogue (SIA), which possesses biologically active insulin activity without enzymatic conversion, under the control of hepatocyte-specific L-type pyruvate kinase (LPK) promoter, which regulates SIA expression in response to blood glucose levels. Here we show that SIA produced from the gene construct rAAV-LPK-SIA caused remission of diabetes in streptozotocin-induced diabetic rats and autoimmune diabetic mice for a prolonged time without any apparent side effects. This new SIA gene therapy may have potential therapeutic value for the cure of autoimmune diabetes in humans.
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Making sense of snapshot data: ergodic principle for clonal cell populations
2017-01-01
Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations. PMID:29187636
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Making sense of snapshot data: ergodic principle for clonal cell populations.
Thomas, Philipp
2017-11-01
Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations. © 2017 The Author(s).
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Blakeslee, Weston W.; Demos-Davies, Kimberly M.; Lemon, Douglas D.; Lutter, Katharina M.; Cavasin, Maria A.; Payne, Sam; Nunley, Karin; Long, Carlin S.; McKinsey, Timothy A.; Miyamoto, Shelley D.
2017-01-01
Background Histone deacetylase (HDAC) inhibitors are promising therapeutics for various forms of cardiac disease. The purpose of this study was to assess cardiac HDAC catalytic activity and expression in children with single ventricle heart disease of right ventricular morphology (SV), as well as in a rodent model of right ventricular hypertrophy (RVH). Methods Homogenates of RV explants from non-failing controls and SV children were assayed for HDAC catalytic activity and HDAC isoform expression. Postnatal 1-day old rat pups were placed in hypoxic conditions and echocardiographic analysis, gene expression, HDAC catalytic activity and isoform expression studies of the RV were performed. Results Class I, IIa, and IIb HDAC catalytic activity and protein expression were elevated in hearts of SV children. Hypoxic neonatal rats demonstrated RVH, abnormal gene expression and elevated class I and class IIb HDAC catalytic activity and protein expression in the RV compared to control. Conclusions These data suggest that myocardial HDAC adaptations occur in the SV heart and could represent a novel therapeutic target. While further characterization of the hypoxic neonatal rat is needed, this animal model may be suitable for pre-clinical investigations of pediatric RV disease and could serve as a useful model for future mechanistic studies. PMID:28549058
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Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M
2012-01-01
Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.
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Integrative modeling of gene and genome evolution roots the archaeal tree of life
Szöllősi, Gergely J.; Spang, Anja; Foster, Peter G.; Heaps, Sarah E.; Boussau, Bastien; Ettema, Thijs J. G.; Embley, T. Martin
2017-01-01
A root for the archaeal tree is essential for reconstructing the metabolism and ecology of early cells and for testing hypotheses that propose that the eukaryotic nuclear lineage originated from within the Archaea; however, published studies based on outgroup rooting disagree regarding the position of the archaeal root. Here we constructed a consensus unrooted archaeal topology using protein concatenation and a multigene supertree method based on 3,242 single gene trees, and then rooted this tree using a recently developed model of genome evolution. This model uses evidence from gene duplications, horizontal transfers, and gene losses contained in 31,236 archaeal gene families to identify the most likely root for the tree. Our analyses support the monophyly of DPANN (Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea), a recently discovered cosmopolitan and genetically diverse lineage, and, in contrast to previous work, place the tree root between DPANN and all other Archaea. The sister group to DPANN comprises the Euryarchaeota and the TACK Archaea, including Lokiarchaeum, which our analyses suggest are monophyletic sister lineages. Metabolic reconstructions on the rooted tree suggest that early Archaea were anaerobes that may have had the ability to reduce CO2 to acetate via the Wood–Ljungdahl pathway. In contrast to proposals suggesting that genome reduction has been the predominant mode of archaeal evolution, our analyses infer a relatively small-genomed archaeal ancestor that subsequently increased in complexity via gene duplication and horizontal gene transfer. PMID:28533395
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Integrative modeling of gene and genome evolution roots the archaeal tree of life.
Williams, Tom A; Szöllősi, Gergely J; Spang, Anja; Foster, Peter G; Heaps, Sarah E; Boussau, Bastien; Ettema, Thijs J G; Embley, T Martin
2017-06-06
A root for the archaeal tree is essential for reconstructing the metabolism and ecology of early cells and for testing hypotheses that propose that the eukaryotic nuclear lineage originated from within the Archaea; however, published studies based on outgroup rooting disagree regarding the position of the archaeal root. Here we constructed a consensus unrooted archaeal topology using protein concatenation and a multigene supertree method based on 3,242 single gene trees, and then rooted this tree using a recently developed model of genome evolution. This model uses evidence from gene duplications, horizontal transfers, and gene losses contained in 31,236 archaeal gene families to identify the most likely root for the tree. Our analyses support the monophyly of DPANN (Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea), a recently discovered cosmopolitan and genetically diverse lineage, and, in contrast to previous work, place the tree root between DPANN and all other Archaea. The sister group to DPANN comprises the Euryarchaeota and the TACK Archaea, including Lokiarchaeum , which our analyses suggest are monophyletic sister lineages. Metabolic reconstructions on the rooted tree suggest that early Archaea were anaerobes that may have had the ability to reduce CO 2 to acetate via the Wood-Ljungdahl pathway. In contrast to proposals suggesting that genome reduction has been the predominant mode of archaeal evolution, our analyses infer a relatively small-genomed archaeal ancestor that subsequently increased in complexity via gene duplication and horizontal gene transfer.
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Design and optimization of reverse-transcription quantitative PCR experiments.
Tichopad, Ales; Kitchen, Rob; Riedmaier, Irmgard; Becker, Christiane; Ståhlberg, Anders; Kubista, Mikael
2009-10-01
Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR. We estimated the errors of sample withdrawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells. A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps contributed substantially to the overall error because the sampling noise was less pronounced. We recommend the use of sample replicates preferentially to any other replicates when working with solid tissue, cell cultures, and single cells, and we recommend the use of RT replicates when working with blood. We show how an optimal sampling plan can be calculated for a limited budget. .
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Mapping of single-copy genes by TSA-FISH in the codling moth, Cydia pomonella.
Carabajal Paladino, Leonela Z; Nguyen, Petr; Síchová, Jindra; Marec, František
2014-01-01
We work on the development of transgenic sexing strains in the codling moth, Cydia pomonella (Tortricidae), which would enable to produce male-only progeny for the population control of this pest using sterile insect technique (SIT). To facilitate this research, we have developed a number of cytogenetic and molecular tools, including a physical map of the codling moth Z chromosome using BAC-FISH (fluorescence in situ hybridization with bacterial artificial chromosome probes). However, chromosomal localization of unique, single-copy sequences such as a transgene cassette by conventional FISH remains challenging. In this study, we adapted a FISH protocol with tyramide signal amplification (TSA-FISH) for detection of single-copy genes in Lepidoptera. We tested the protocol with probes prepared from partial sequences of Z-linked genes in the codling moth. Using a modified TSA-FISH protocol we successfully mapped a partial sequence of the Acetylcholinesterase 1 (Ace-1) gene to the Z chromosome and confirmed thus its Z-linkage. A subsequent combination of BAC-FISH with BAC probes containing anticipated neighbouring Z-linked genes and TSA-FISH with the Ace-1 probe allowed the integration of Ace-1 in the physical map of the codling moth Z chromosome. We also developed a two-colour TSA-FISH protocol which enabled us simultaneous localization of two Z-linked genes, Ace-1 and Notch, to the expected regions of the Z chromosome. We showed that TSA-FISH represents a reliable technique for physical mapping of genes on chromosomes of moths and butterflies. Our results suggest that this technique can be combined with BAC-FISH and in the future used for physical localization of transgene cassettes on chromosomes of transgenic lines in the codling moth or other lepidopteran species. Furthermore, the developed protocol for two-colour TSA-FISH might become a powerful tool for synteny mapping in non-model organisms.
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Mapping of single-copy genes by TSA-FISH in the codling moth, Cydia pomonella
2014-01-01
Background We work on the development of transgenic sexing strains in the codling moth, Cydia pomonella (Tortricidae), which would enable to produce male-only progeny for the population control of this pest using sterile insect technique (SIT). To facilitate this research, we have developed a number of cytogenetic and molecular tools, including a physical map of the codling moth Z chromosome using BAC-FISH (fluorescence in situ hybridization with bacterial artificial chromosome probes). However, chromosomal localization of unique, single-copy sequences such as a transgene cassette by conventional FISH remains challenging. In this study, we adapted a FISH protocol with tyramide signal amplification (TSA-FISH) for detection of single-copy genes in Lepidoptera. We tested the protocol with probes prepared from partial sequences of Z-linked genes in the codling moth. Results Using a modified TSA-FISH protocol we successfully mapped a partial sequence of the Acetylcholinesterase 1 (Ace-1) gene to the Z chromosome and confirmed thus its Z-linkage. A subsequent combination of BAC-FISH with BAC probes containing anticipated neighbouring Z-linked genes and TSA-FISH with the Ace-1 probe allowed the integration of Ace-1 in the physical map of the codling moth Z chromosome. We also developed a two-colour TSA-FISH protocol which enabled us simultaneous localization of two Z-linked genes, Ace-1 and Notch, to the expected regions of the Z chromosome. Conclusions We showed that TSA-FISH represents a reliable technique for physical mapping of genes on chromosomes of moths and butterflies. Our results suggest that this technique can be combined with BAC-FISH and in the future used for physical localization of transgene cassettes on chromosomes of transgenic lines in the codling moth or other lepidopteran species. Furthermore, the developed protocol for two-colour TSA-FISH might become a powerful tool for synteny mapping in non-model organisms. PMID:25471491
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Swindell, William R.; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P.; Voorhees, John J.; Elder, James T.; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P.; DiGiovanni, John; Pittelkow, Mark R.; Ward, Nicole L.; Gudjonsson, Johann E.
2011-01-01
Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis. PMID:21483750
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2010-01-01
Background Recent experimental work has uncovered some of the genetic components required to maintain the Arabidopsis thaliana root stem cell niche (SCN) and its structure. Two main pathways are involved. One pathway depends on the genes SHORTROOT and SCARECROW and the other depends on the PLETHORA genes, which have been proposed to constitute the auxin readouts. Recent evidence suggests that a regulatory circuit, composed of WOX5 and CLE40, also contributes to the SCN maintenance. Yet, we still do not understand how the niche is dynamically maintained and patterned or if the uncovered molecular components are sufficient to recover the observed gene expression configurations that characterize the cell types within the root SCN. Mathematical and computational tools have proven useful in understanding the dynamics of cell differentiation. Hence, to further explore root SCN patterning, we integrated available experimental data into dynamic Gene Regulatory Network (GRN) models and addressed if these are sufficient to attain observed gene expression configurations in the root SCN in a robust and autonomous manner. Results We found that an SCN GRN model based only on experimental data did not reproduce the configurations observed within the root SCN. We developed several alternative GRN models that recover these expected stable gene configurations. Such models incorporate a few additional components and interactions in addition to those that have been uncovered. The recovered configurations are stable to perturbations, and the models are able to recover the observed gene expression profiles of almost all the mutants described so far. However, the robustness of the postulated GRNs is not as high as that of other previously studied networks. Conclusions These models are the first published approximations for a dynamic mechanism of the A. thaliana root SCN cellular pattering. Our model is useful to formally show that the data now available are not sufficient to fully reproduce root SCN organization and genetic profiles. We then highlight some experimental holes that remain to be studied and postulate some novel gene interactions. Finally, we suggest the existence of a generic dynamical motif that can be involved in both plant and animal SCN maintenance. PMID:20920363
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3D confocal reconstruction of gene expression in mouse.
Hecksher-Sørensen, J; Sharpe, J
2001-01-01
Three-dimensional computer reconstructions of gene expression data will become a valuable tool in biomedical research in the near future. However, at present the process of converting in situ expression data into 3D models is a highly specialized and time-consuming procedure. Here we present a method which allows rapid reconstruction of whole-mount in situ data from mouse embryos. Mid-gestation embryos were stained with the alkaline phosphotase substrate Fast Red, which can be detected using confocal laser scanning microscopy (CLSM), and cut into 70 microm sections. Each section was then scanned and digitally reconstructed. Using this method it took two days to section, digitize and reconstruct the full expression pattern of Shh in an E9.5 embryo (a 3D model of this embryo can be seen at genex.hgu.mrc.ac.uk). Additionally we demonstrate that this technique allows gene expression to be studied at the single cell level in intact tissue.
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Lobo, Daniel; Morokuma, Junji; Levin, Michael
2016-09-01
Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology. Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model. These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes. michael.levin@tufts.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Quality assessment of protein model-structures based on structural and functional similarities.
Konopka, Bogumil M; Nebel, Jean-Christophe; Kotulska, Malgorzata
2012-09-21
Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.
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Evidence for a large expansion and subfunctionalisation of globin genes in sea anemones.
Smith, Hayden L; Pavasovic, Ana; Surm, Joachim M; Phillips, Matthew J; Prentis, Peter J
2018-06-27
The globin gene superfamily has been well-characterised in vertebrates, however, there has been limited research in early-diverging lineages, such as phylum Cnidaria. This study aimed to identify globin genes in multiple cnidarian lineages, and use bioinformatic approaches to characterise the evolution, structure and expression of these genes. Phylogenetic analyses and in silico protein predictions showed that all cnidarians have undergone an expansion of globin genes, which likely have a hexacoordinate protein structure. Our protein modelling has also revealed the possibility of a single pentacoordinate globin lineage in anthozoan species. Some cnidarian globin genes displayed tissue and development specific expression with very few orthologous genes similarly expressed across species. Our phylogenetic analyses also revealed that eumetazoan globin genes form a polyphyletic relationship with vertebrate globin genes. Overall, our analyses suggest that a Ngb-like and GbX-like gene were most likely present in the globin gene repertoire for the last common ancestor of eumetazoans. The identification of a large-scale expansion and subfunctionalisation of globin genes in actiniarians provides an excellent starting point to further our understanding of the evolution and function of the globin gene superfamily in early-diverging lineages.
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Single cell gene expression profiling in Alzheimer's disease.
Ginsberg, Stephen D; Che, Shaoli; Counts, Scott E; Mufson, Elliott J
2006-07-01
Development and implementation of microarray techniques to quantify expression levels of dozens to hundreds to thousands of transcripts simultaneously within select tissue samples from normal control subjects and neurodegenerative diseased brains has enabled scientists to create molecular fingerprints of vulnerable neuronal populations in Alzheimer's disease (AD) and related disorders. A goal is to sample gene expression from homogeneous cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and nonneuronal cells. The precise resolution afforded by single cell and population cell RNA analysis in combination with microarrays and real-time quantitative polymerase chain reaction (qPCR)-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease progression. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models of neurodegeneration as well as postmortem human brain tissues. Gene expression analysis in postmortem AD brain regions including the hippocampal formation and neocortex reveals selectively vulnerable cell types share putative pathogenetic alterations in common classes of transcripts, for example, markers of glutamatergic neurotransmission, synaptic-related markers, protein phosphatases and kinases, and neurotrophins/neurotrophin receptors. Expression profiles of vulnerable regions and neurons may reveal important clues toward the understanding of the molecular pathogenesis of various neurological diseases and aid in identifying rational targets toward pharmacotherapeutic interventions for progressive, late-onset neurodegenerative disorders such as mild cognitive impairment (MCI) and AD.
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Lyubetsky, Vassily; Gershgorin, Roman; Gorbunov, Konstantin
2017-12-06
Chromosome structure is a very limited model of the genome including the information about its chromosomes such as their linear or circular organization, the order of genes on them, and the DNA strand encoding a gene. Gene lengths, nucleotide composition, and intergenic regions are ignored. Although highly incomplete, such structure can be used in many cases, e.g., to reconstruct phylogeny and evolutionary events, to identify gene synteny, regulatory elements and promoters (considering highly conserved elements), etc. Three problems are considered; all assume unequal gene content and the presence of gene paralogs. The distance problem is to determine the minimum number of operations required to transform one chromosome structure into another and the corresponding transformation itself including the identification of paralogs in two structures. We use the DCJ model which is one of the most studied combinatorial rearrangement models. Double-, sesqui-, and single-operations as well as deletion and insertion of a chromosome region are considered in the model; the single ones comprise cut and join. In the reconstruction problem, a phylogenetic tree with chromosome structures in the leaves is given. It is necessary to assign the structures to inner nodes of the tree to minimize the sum of distances between terminal structures of each edge and to identify the mutual paralogs in a fairly large set of structures. A linear algorithm is known for the distance problem without paralogs, while the presence of paralogs makes it NP-hard. If paralogs are allowed but the insertion and deletion operations are missing (and special constraints are imposed), the reduction of the distance problem to integer linear programming is known. Apparently, the reconstruction problem is NP-hard even in the absence of paralogs. The problem of contigs is to find the optimal arrangements for each given set of contigs, which also includes the mutual identification of paralogs. We proved that these problems can be reduced to integer linear programming formulations, which allows an algorithm to redefine the problems to implement a very special case of the integer linear programming tool. The results were tested on synthetic and biological samples. Three well-known problems were reduced to a very special case of integer linear programming, which is a new method of their solutions. Integer linear programming is clearly among the main computational methods and, as generally accepted, is fast on average; in particular, computation systems specifically targeted at it are available. The challenges are to reduce the size of the corresponding integer linear programming formulations and to incorporate a more detailed biological concept in our model of the reconstruction.
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From Saccharomyces cerevisiae to human: The important gene co-expression modules.
Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin
2017-08-01
Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.
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Leyva-Corona, Jose C; Reyna-Granados, Javier R; Zamorano-Algandar, Ricardo; Sanchez-Castro, Miguel A; Thomas, Milton G; Enns, R Mark; Speidel, Scott E; Medrano, Juan F; Rincon, Gonzalo; Luna-Nevarez, Pablo
2018-06-20
Prolactin (PRL), growth hormone (GH), and insulin-like growth factor-1 (IGF-1) are in hormone-response pathways involved in energy metabolism during thermoregulation processes in cattle. Objective herein was to study the association between single nucleotide polymorphisms (SNP) within genes of the PRL and GH/IGF-1 pathways with fertility traits such as services per conception (SPC) and days open (DO) in Holstein cattle lactating under a hot-humid climate. Ambient temperature and relative humidity were used to calculate the temperature-humidity index (THI) which revealed that the cows were exposed to heat stress conditions from June to November of 2012 in southern Sonora, Mexico. Individual blood samples from all cows were collected, spotted on FTA cards, and used to genotype a 179 tag SNP panel within 44 genes from the PRL and GH/IGF-1 pathways. The associative analyses among SNP genotypes and fertility traits were performed using mixed-effect models. Allele substitution effects were calculated using a regression model that included the genotype term as covariate. Single-SNP association analyses indicated that eight SNP within the genes IGF-1, IGF-1R, IGFBP5, PAPPA1, PMCH, PRLR, SOCS5, and SSTR2 were associated with SPC (P < 0.05), whereas four SNP in the genes GHR, PAPPA2, PRLR, and SOCS4 were associated with DO (P < 0.05). In conclusion, SNP within genes of the PRL and GH/IGF-1 pathways resulted as predictors of reproductive phenotypes in heat-stressed Holstein cows, and these SNP are proposed as candidates for a marker-assisted selection program intended to improve fertility of dairy cattle raised in warm climates.
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Annibale, Paolo; Gratton, Enrico
2015-01-01
Multi-cell biochemical assays and single cell fluorescence measurements revealed that the elongation rate of Polymerase II (PolII) in eukaryotes varies largely across different cell types and genes. However, there is not yet a consensus whether intrinsic factors such as the position, local mobility or the engagement by an active molecular mechanism of a genetic locus could be the determinants of the observed heterogeneity. Here by employing high-speed 3D fluorescence nanoimaging techniques we resolve and track at the single cell level multiple, distinct regions of mRNA synthesis within the model system of a large transgene array. We demonstrate that these regions are active transcription sites that release mRNA molecules in the nucleoplasm. Using fluctuation spectroscopy and the phasor analysis approach we were able to extract the local PolII elongation rate at each site as a function of time. We measured a four-fold variation in the average elongation between identical copies of the same gene measured simultaneously within the same cell, demonstrating a correlation between local transcription kinetics and the movement of the transcription site. Together these observations demonstrate that local factors, such as chromatin local mobility and the microenvironment of the transcription site, are an important source of transcription kinetics variability. PMID:25788248
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Majeske, Audrey J; Oren, Matan; Sacchi, Sandro; Smith, L Courtney
2014-12-01
Immune systems in animals rely on fast and efficient responses to a wide variety of pathogens. The Sp185/333 gene family in the purple sea urchin, Strongylocentrotus purpuratus, consists of an estimated 50 (±10) members per genome that share a basic gene structure but show high sequence diversity, primarily due to the mosaic appearance of short blocks of sequence called elements. The genes show significantly elevated expression in three subpopulations of phagocytes responding to marine bacteria. The encoded Sp185/333 proteins are highly diverse and have central effector functions in the immune system. In this study we report the Sp185/333 gene expression in single sea urchin phagocytes. Sea urchins challenged with heat-killed marine bacteria resulted in a typical increase in coelomocyte concentration within 24 h, which included an increased proportion of phagocytes expressing Sp185/333 proteins. Phagocyte fractions enriched from coelomocytes were used in limiting dilutions to obtain samples of single cells that were evaluated for Sp185/333 gene expression by nested RT-PCR. Amplicon sequences showed identical or nearly identical Sp185/333 amplicon sequences in single phagocytes with matches to six known Sp185/333 element patterns, including both common and rare element patterns. This suggested that single phagocytes show restricted expression from the Sp185/333 gene family and infers a diverse, flexible, and efficient response to pathogens. This type of expression pattern from a family of immune response genes in single cells has not been identified previously in other invertebrates. Copyright © 2014 by The American Association of Immunologists, Inc.
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Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng
2013-01-01
Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984
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Circadian Enhancers Coordinate Multiple Phases of Rhythmic Gene Transcription In Vivo
Fang, Bin; Everett, Logan J.; Jager, Jennifer; Briggs, Erika; Armour, Sean M.; Feng, Dan; Roy, Ankur; Gerhart-Hines, Zachary; Sun, Zheng; Lazar, Mitchell A.
2014-01-01
SUMMARY Mammalian transcriptomes display complex circadian rhythms with multiple phases of gene expression that cannot be accounted for by current models of the molecular clock. We have determined the underlying mechanisms by measuring nascent RNA transcription around the clock in mouse liver. Unbiased examination of eRNAs that cluster in specific circadian phases identified functional enhancers driven by distinct transcription factors (TFs). We further identify on a global scale the components of the TF cistromes that function to orchestrate circadian gene expression. Integrated genomic analyses also revealed novel mechanisms by which a single circadian factor controls opposing transcriptional phases. These findings shed new light on the diversity and specificity of TF function in the generation of multiple phases of circadian gene transcription in a mammalian organ. PMID:25416951
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Circadian enhancers coordinate multiple phases of rhythmic gene transcription in vivo.
Fang, Bin; Everett, Logan J; Jager, Jennifer; Briggs, Erika; Armour, Sean M; Feng, Dan; Roy, Ankur; Gerhart-Hines, Zachary; Sun, Zheng; Lazar, Mitchell A
2014-11-20
Mammalian transcriptomes display complex circadian rhythms with multiple phases of gene expression that cannot be accounted for by current models of the molecular clock. We have determined the underlying mechanisms by measuring nascent RNA transcription around the clock in mouse liver. Unbiased examination of enhancer RNAs (eRNAs) that cluster in specific circadian phases identified functional enhancers driven by distinct transcription factors (TFs). We further identify on a global scale the components of the TF cistromes that function to orchestrate circadian gene expression. Integrated genomic analyses also revealed mechanisms by which a single circadian factor controls opposing transcriptional phases. These findings shed light on the diversity and specificity of TF function in the generation of multiple phases of circadian gene transcription in a mammalian organ.
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Necesankova, Michaela; Vychodilova, Leona; Albrechtova, Katerina; Kennedy, Lorna J; Hlavac, Jan; Sedlak, Kamil; Modry, David; Janova, Eva; Vyskocil, Mirko; Horin, Petr
2016-12-01
The purpose of this study was to seek associations between immunity-related molecular markers and endemic infections in a model population of African village dogs from Northern Kenya with no veterinary care and no selective breeding. A population of village dogs from Northern Kenya composed of three sub-populations from three different areas (84, 50 and 55 dogs) was studied. Canine distemper virus (CDV), Hepatozoon canis, Microfilariae (Acantocheilonema dracunculoides, Acantocheilonema reconditum) and Neospora caninum were the pathogens studied. The presence of antibodies (CDV, Neospora), light microscopy (Hepatozoon) and diagnostic PCR (Microfilariae) were the methods used for diagnosing infection. Genes involved in innate immune mechanisms, NOS3, IL6, TLR1, TLR2, TLR4, TLR7, TLR9, LY96, MYD88, and three major histocompatibility genes class II genes were selected as candidates. Single nucleotide polymorphism (SNP) markers were detected by Sanger sequencing, next generation sequencing and PCR-RFLP. The Fisher´s exact test for additive and non-additive models was used for association analyses. Three SNPs within the MYD88 gene and one TLR4 SNP marker were associated with more than one infection. Combined genotypes and further markers identified by next generation sequencing confirmed associations observed for individual genes. The genes associated with infection and their combinations in specific genotypes match well our knowledge on their biological role and on the role of the relevant biological pathways, respectively. Associations with multiple infections observed between the MYD88 and TLR4 genes suggest their involvement in the mechanisms of anti-infectious defenses in dogs.
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Fragmentation of the large subunit ribosomal RNA gene in oyster mitochondrial genomes.
Milbury, Coren A; Lee, Jung C; Cannone, Jamie J; Gaffney, Patrick M; Gutell, Robin R
2010-09-02
Discontinuous genes have been observed in bacteria, archaea, and eukaryotic nuclei, mitochondria and chloroplasts. Gene discontinuity occurs in multiple forms: the two most frequent forms result from introns that are spliced out of the RNA and the resulting exons are spliced together to form a single transcript, and fragmented gene transcripts that are not covalently attached post-transcriptionally. Within the past few years, fragmented ribosomal RNA (rRNA) genes have been discovered in bilateral metazoan mitochondria, all within a group of related oysters. In this study, we have characterized this fragmentation with comparative analysis and experimentation. We present secondary structures, modeled using comparative sequence analysis of the discontinuous mitochondrial large subunit rRNA genes of the cupped oysters C. virginica, C. gigas, and C. hongkongensis. Comparative structure models for the large subunit rRNA in each of the three oyster species are generally similar to those for other bilateral metazoans. We also used RT-PCR and analyzed ESTs to determine if the two fragmented LSU rRNAs are spliced together. The two segments are transcribed separately, and not spliced together although they still form functional rRNAs and ribosomes. Although many examples of discontinuous ribosomal genes have been documented in bacteria and archaea, as well as the nuclei, chloroplasts, and mitochondria of eukaryotes, oysters are some of the first characterized examples of fragmented bilateral animal mitochondrial rRNA genes. The secondary structures of the oyster LSU rRNA fragments have been predicted on the basis of previous comparative metazoan mitochondrial LSU rRNA structure models.
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Programming Morphogenesis through Systems and Synthetic Biology.
Velazquez, Jeremy J; Su, Emily; Cahan, Patrick; Ebrahimkhani, Mo R
2018-04-01
Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Heinrich, Stephanie; Derrer, Carina Patrizia; Lari, Azra; Weis, Karsten; Montpetit, Ben
2017-01-01
The transport of messenger RNAs (mRNAs) from the nucleus to cytoplasm is an essential step in the gene expression program of all eukaryotes. Recent technological advances in the areas of RNA-labeling, microscopy, and sequencing are leading to novel insights about mRNA biogenesis and export. This includes quantitative single molecule imaging (SMI) of RNA molecules in live cells, which is providing knowledge of the spatial and temporal dynamics of the export process. As this information becomes available, it leads to new questions, the reinterpretation of previous findings, and revised models of mRNA export. In this review, we will briefly highlight some of these recent findings and discuss how live cell SMI approaches may be used to further our current understanding of mRNA export and gene expression. PMID:28052353
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Draft Genome of the Scarab Beetle Oryctes borbonicus on La Réunion Island
Meyer, Jan M.; Markov, Gabriel V.; Baskaran, Praveen; Herrmann, Matthias; Sommer, Ralf J.; Rödelsperger, Christian
2016-01-01
Beetles represent the largest insect order and they display extreme morphological, ecological and behavioral diversity, which makes them ideal models for evolutionary studies. Here, we present the draft genome of the scarab beetle Oryctes borbonicus, which has a more basal phylogenetic position than the two previously sequenced pest species Tribolium castaneum and Dendroctonus ponderosae providing the potential for sequence polarization. Oryctes borbonicus is endemic to La Réunion, an island located in the Indian Ocean, and is the host of the nematode Pristionchus pacificus, a well-established model organism for integrative evolutionary biology. At 518 Mb, the O. borbonicus genome is substantially larger and encodes more genes than T. castaneum and D. ponderosae. We found that only 25% of the predicted genes of O. borbonicus are conserved as single copy genes across the nine investigated insect genomes, suggesting substantial gene turnover within insects. Even within beetles, up to 21% of genes are restricted to only one species, whereas most other genes have undergone lineage-specific duplications and losses. We illustrate lineage-specific duplications using detailed phylogenetic analysis of two gene families. This study serves as a reference point for insect/coleopteran genomics, although its original motivation was to find evidence for potential horizontal gene transfer (HGT) between O. borbonicus and P. pacificus. The latter was previously shown to be the recipient of multiple horizontally transferred genes including some genes from insect donors. However, our study failed to provide any clear evidence for additional HGTs between the two species. PMID:27289092
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Blanchard, Raymond K.; Moore, J. Bernadette; Green, Calvert L.; Cousins, Robert J.
2001-01-01
Mammalian nutritional status affects the homeostatic balance of multiple physiological processes and their associated gene expression. Although DNA array analysis can monitor large numbers of genes, there are no reports of expression profiling of a micronutrient deficiency in an intact animal system. In this report, we have tested the feasibility of using cDNA arrays to compare the global changes in expression of genes of known function that occur in the early stages of rodent zinc deficiency. The gene-modulating effects of this deficiency were demonstrated by real-time quantitative PCR measurements of altered mRNA levels for metallothionein 1, zinc transporter 2, and uroguanylin, all of which have been previously documented as zinc-regulated genes. As a result of the low level of inherent noise within this model system and application of a recently reported statistical tool for statistical analysis of microarrays [Tusher, V.G., Tibshirani, R. & Chu, G. (2001) Proc. Natl. Acad. Sci. USA 98, 5116–5121], we demonstrate the ability to reproducibly identify the modest changes in mRNA abundance produced by this single micronutrient deficiency. Among the genes identified by this array profile are intestinal genes that influence signaling pathways, growth, transcription, redox, and energy utilization. Additionally, the influence of dietary zinc supply on the expression of some of these genes was confirmed by real-time quantitative PCR. Overall, these data support the effectiveness of cDNA array expression profiling to investigate the pleiotropic effects of specific nutrients and may provide an approach to establishing markers for assessment of nutritional status. PMID:11717422
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Pappas, Jane J.; Petropoulos, Sophie; Suderman, Matthew; Iqbal, Majid; Moisiadis, Vasilis; Turecki, Gustavo; Matthews, Stephen G.; Szyf, Moshe
2014-01-01
The Multidrug Resistance 1 (MDR1; alternatively ABCB1) gene product P-glycoprotein (P-gp), an ATP binding cassette transporter, extrudes multiple endogenous and exogenous substrates from the cell, playing an important role in normal physiology and xenobiotic distribution and bioavailability. To date, the predominant animal models used to investigate the role of P-gp have been the mouse and rat, which have two distinct genes, Abcb1a and Abcb1b. In contrast, the human has a single gene, ABCB1, for which only a single isoform has been validated. We and others have previously shown important differences between Abcb1a and Abcb1b, limiting the extrapolation from rodent findings to the human. Since the guinea pig has a relatively long gestation, hemomonochorial placentation and neuroanatomically mature offspring, it is more similar to the human, and may provide a more comparable model for investigating the regulation of P-gp in the brain and placenta, however, to date, the Abcb1 gene in the guinea pig remains to be characterized. The placenta and fetal brain are barrier sites that express P-gp and that play a critical role of protection of the fetus and the fetal brain from maternally administered drugs and other xenobiotics. Using RNA sequencing (RNA-seq), reverse transcription-polymerase chain reaction (RT-PCR) and quantitative PCR (QPCR) to sequence the expressed isoforms of guinea pig Abcb1, we demonstrate that like the human, the guinea pig genome contains one gene for Abcb1 but that it is expressed as at least three different isoforms via alternative splicing and alternate exon usage. Further, we demonstrate that these isoforms are more closely related to human than to rat or mouse isoforms. This striking, overall similarity and evolutionary relatedness between guinea pig Abcb1 and human ABCB1 indicate that the guinea pig represents a relevant animal model for investigating the function and regulation of P-gp in the placenta and brain. PMID:25353162
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Gray, Stanton B; Howard, Timothy D; Langefeld, Carl D; Hawkins, Gregory A; Diallo, Abdoulaye F; Wagner, Janice D
2009-01-01
Tumor necrosis factor is a cytokine that plays critical roles in inflammation, the innate immune response, and a variety of other physiologic and pathophysiologic processes. In addition, TNF has recently been shown to mediate an intersection of chronic, low-grade inflammation and concurrent metabolic dysregulation associated with obesity and its comorbidities. As part of an ongoing initiative to further characterize vervet monkeys originating from St Kitts as an animal model of obesity and inflammation, we sequenced and genotyped the human ortholog vervet TNF gene and approximately 1 kb of the flanking 3′ and 5′ regions from 265 monkeys in a closed, pedigreed colony. This process revealed a total of 11 single-nucleotide polymorphisms (SNPs) and a single 4-bp insertion–deletion, with minor allele frequencies of 0.08 to 0.39. Many of these polymorphisms were in strong or complete linkage disequilibrium with each other, and all but 1 were contained within a single haplotype block, comprising 5 haplotypes with frequencies of 0.075 to 0.298. Using sequences from humans, chimpanzees, vervets, baboons, and rhesus macaques, phylogenetic shadowing of the TNF promoter region revealed that vervet SNPs, like the SNPs in related species, were clustered nonrandomly and nonuniformly around conserved transcription factor binding sites. These data, combined with previously defined heritable phenotypes, permit future association analyses in this nonhuman primate model and have great potential to help dissect the genetic and nongenetic contributions to complex diseases like obesity. More broadly, the sequence data and comparative analyses reported herein facilitates study of the evolution of regulatory sequences of inflammatory and immune-related genes. PMID:20034434
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Single cell transcriptomics of neighboring hyphae of Aspergillus niger
2011-01-01
Single cell profiling was performed to assess differences in RNA accumulation in neighboring hyphae of the fungus Aspergillus niger. A protocol was developed to isolate and amplify RNA from single hyphae or parts thereof. Microarray analysis resulted in a present call for 4 to 7% of the A. niger genes, of which 12% showed heterogeneous RNA levels. These genes belonged to a wide range of gene categories. PMID:21816052
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Preston, Jill C.; Martinez, Ciera C.; Hileman, Lena C.
2011-01-01
Angiosperms exhibit staggering diversity in floral form, and evolution of floral morphology is often correlated with changes in pollination syndrome. The showy, bilaterally symmetrical flowers of the model species Antirrhinum majus (Plantaginaceae) are highly specialized for bee pollination. In A. majus, CYCLOIDEA (CYC), DICHOTOMA (DICH), RADIALIS (RAD), and DIVARICATA (DIV) specify the development of floral bilateral symmetry. However, it is unclear to what extent evolution of these genes has resulted in flower morphological divergence among closely related members of Plantaginaceae differing in pollination syndrome. We compared floral symmetry genes from insect-pollinated Digitalis purpurea, which has bilaterally symmetrical flowers, with those from closely related Aragoa abietina and wind-pollinated Plantago major, both of which have radially symmetrical flowers. We demonstrate that Plantago, but not Aragoa, species have lost a dorsally expressed CYC-like gene and downstream targets RAD and DIV. Furthermore, the single P. major CYC-like gene is expressed across all regions of the flower, similar to expression of its ortholog in closely related Veronica serpyllifolia. We propose that changes in the expression of duplicated CYC-like genes led to the evolution of radial flower symmetry in Aragoa/Plantago, and that further disintegration of the symmetry gene pathway resulted in the wind-pollination syndrome of Plantago. This model underscores the potential importance of gene loss in the evolution of ecologically important traits. PMID:21282634
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In Silico Analysis of Single Nucleotide Polymorphism (SNPs) in Human β-Globin Gene
Alanazi, Mohammed; Abduljaleel, Zainularifeen; Khan, Wajahatullah; Warsy, Arjumand S.; Elrobh, Mohamed; Khan, Zahid; Amri, Abdullah Al; Bazzi, Mohammad D.
2011-01-01
Single amino acid substitutions in the globin chain are the most common forms of genetic variations that produce hemoglobinopathies- the most widespread inherited disorders worldwide. Several hemoglobinopathies result from homozygosity or compound heterozygosity to beta-globin (HBB) gene mutations, such as that producing sickle cell hemoglobin (HbS), HbC, HbD and HbE. Several of these mutations are deleterious and result in moderate to severe hemolytic anemia, with associated complications, requiring lifelong care and management. Even though many hemoglobinopathies result from single amino acid changes producing similar structural abnormalities, there are functional differences in the generated variants. Using in silico methods, we examined the genetic variations that can alter the expression and function of the HBB gene. Using a sequence homology-based Sorting Intolerant from Tolerant (SIFT) server we have searched for the SNPs, which showed that 200 (80%) non-synonymous polymorphism were found to be deleterious. The structure-based method via PolyPhen server indicated that 135 (40%) non-synonymous polymorphism may modify protein function and structure. The Pupa Suite software showed that the SNPs will have a phenotypic consequence on the structure and function of the altered protein. Structure analysis was performed on the key mutations that occur in the native protein coded by the HBB gene that causes hemoglobinopathies such as: HbC (E→K), HbD (E→Q), HbE (E→K) and HbS (E→V). Atomic Non-Local Environment Assessment (ANOLEA), Yet Another Scientific Artificial Reality Application (YASARA), CHARMM-GUI webserver for macromolecular dynamics and mechanics, and Normal Mode Analysis, Deformation and Refinement (NOMAD-Ref) of Gromacs server were used to perform molecular dynamics simulations and energy minimization calculations on β-Chain residue of the HBB gene before and after mutation. Furthermore, in the native and altered protein models, amino acid residues were determined and secondary structures were observed for solvent accessibility to confirm the protein stability. The functional study in this investigation may be a good model for additional future studies. PMID:22028795
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Gangavarapu, Kalyan J; Miller, Austin; Huss, Wendy J
2016-09-01
Defining biological signals at the single cell level can identify cancer initiating driver mutations. Techniques to isolate single cells such as microfluidics sorting and magnetic capturing systems have limitations such as: high cost, labor intense, and the requirement of a large number of cells. Therefore, the goal of our current study is to identify a cost and labor effective, reliable, and reproducible technique that allows single cell isolation for analysis to promote regular laboratory use, including standard reverse transcription PCR (RT-PCR). In the current study, we utilized single prostate cells isolated from the CWR-R1 prostate cancer cell line and human prostate clinical specimens, based on the ATP binding cassette (ABC) transporter efflux of dye cycle violet (DCV), side population assay. Expression of four genes: ABCG2; Aldehyde dehydrogenase1A1 (ALDH1A1); androgen receptor (AR); and embryonic stem cell marker, Oct-4, were determined. Results from the current study in the CWR-R1 cell line showed ABCG2 and ALDH1A1 gene expression in 67% of single side population cells and in 17% or 100% of non-side population cells respectively. Studies using single cells isolated from clinical specimens showed that the Oct-4 gene is detected in only 22% of single side population cells and in 78% of single non-side population cells. Whereas, AR gene expression is in 100% single side population and non-side population cells isolated from the same human prostate clinical specimen. These studies show that performing RT-PCR on single cells isolated by FACS can be successfully conducted to determine gene expression in single cells from cell lines and enzymatically digested tissue. While these studies provide a simple yes/no expression readout, the more sensitive quantitative RT-PCR would be able to provide even more information if necessary.
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Gangavarapu, Kalyan J; Miller, Austin; Huss, Wendy J
2016-01-01
Defining biological signals at the single cell level can identify cancer initiating driver mutations. Techniques to isolate single cells such as microfluidics sorting and magnetic capturing systems have limitations such as: high cost, labor intense, and the requirement of a large number of cells. Therefore, the goal of our current study is to identify a cost and labor effective, reliable, and reproducible technique that allows single cell isolation for analysis to promote regular laboratory use, including standard reverse transcription PCR (RT-PCR). In the current study, we utilized single prostate cells isolated from the CWR-R1 prostate cancer cell line and human prostate clinical specimens, based on the ATP binding cassette (ABC) transporter efflux of dye cycle violet (DCV), side population assay. Expression of four genes: ABCG2; Aldehyde dehydrogenase1A1 (ALDH1A1); androgen receptor (AR); and embryonic stem cell marker, Oct-4, were determined. Results from the current study in the CWR-R1 cell line showed ABCG2 and ALDH1A1 gene expression in 67% of single side population cells and in 17% or 100% of non-side population cells respectively. Studies using single cells isolated from clinical specimens showed that the Oct-4 gene is detected in only 22% of single side population cells and in 78% of single non-side population cells. Whereas, AR gene expression is in 100% single side population and non-side population cells isolated from the same human prostate clinical specimen. These studies show that performing RT-PCR on single cells isolated by FACS can be successfully conducted to determine gene expression in single cells from cell lines and enzymatically digested tissue. While these studies provide a simple yes/no expression readout, the more sensitive quantitative RT-PCR would be able to provide even more information if necessary. PMID:27785389
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Winterhoff, Boris J; Maile, Makayla; Mitra, Amit Kumar; Sebe, Attila; Bazzaro, Martina; Geller, Melissa A; Abrahante, Juan E; Klein, Molly; Hellweg, Raffaele; Mullany, Sally A; Beckman, Kenneth; Daniel, Jerry; Starr, Timothy K
2017-03-01
The purpose of this study was to determine the level of heterogeneity in high grade serous ovarian cancer (HGSOC) by analyzing RNA expression in single epithelial and cancer associated stromal cells. In addition, we explored the possibility of identifying subgroups based on pathway activation and pre-defined signatures from cancer stem cells and chemo-resistant cells. A fresh, HGSOC tumor specimen derived from ovary was enzymatically digested and depleted of immune infiltrating cells. RNA sequencing was performed on 92 single cells and 66 of these single cell datasets passed quality control checks. Sequences were analyzed using multiple bioinformatics tools, including clustering, principle components analysis, and geneset enrichment analysis to identify subgroups and activated pathways. Immunohistochemistry for ovarian cancer, stem cell and stromal markers was performed on adjacent tumor sections. Analysis of the gene expression patterns identified two major subsets of cells characterized by epithelial and stromal gene expression patterns. The epithelial group was characterized by proliferative genes including genes associated with oxidative phosphorylation and MYC activity, while the stromal group was characterized by increased expression of extracellular matrix (ECM) genes and genes associated with epithelial-to-mesenchymal transition (EMT). Neither group expressed a signature correlating with published chemo-resistant gene signatures, but many cells, predominantly in the stromal subgroup, expressed markers associated with cancer stem cells. Single cell sequencing provides a means of identifying subpopulations of cancer cells within a single patient. Single cell sequence analysis may prove to be critical for understanding the etiology, progression and drug resistance in ovarian cancer. Copyright © 2017 Elsevier Inc. All rights reserved.
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Single-Cell and Single-Molecule Analysis of Gene Expression Regulation.
Vera, Maria; Biswas, Jeetayu; Senecal, Adrien; Singer, Robert H; Park, Hye Yoon
2016-11-23
Recent advancements in single-cell and single-molecule imaging technologies have resolved biological processes in time and space that are fundamental to understanding the regulation of gene expression. Observations of single-molecule events in their cellular context have revealed highly dynamic aspects of transcriptional and post-transcriptional control in eukaryotic cells. This approach can relate transcription with mRNA abundance and lifetimes. Another key aspect of single-cell analysis is the cell-to-cell variability among populations of cells. Definition of heterogeneity has revealed stochastic processes, determined characteristics of under-represented cell types or transitional states, and integrated cellular behaviors in the context of multicellular organisms. In this review, we discuss novel aspects of gene expression of eukaryotic cells and multicellular organisms revealed by the latest advances in single-cell and single-molecule imaging technology.
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Yang, Libin; Qu, Bo; Xia, Xun; Kuang, Yongqin; Li, Jian; Fan, Kexia; Guo, Heng; Zheng, Hui; Ma, Yuan
2017-06-06
To investigate the impact of CCND1 and EFEMP1 gene polymorphism, and additional their gene-gene interactions and haplotype within EFEMP1 gene on glioma risk based on Chinese population. Logistic regression was performed to investigate association between single-nucleotide polymorphisms (SNP) and glioma risk and generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-gene interaction. Glioma risks were higher in carriers of homozygous mutant of rs603965 within CCND1 gene, rs1346787 and rs3791679 in EFEMP1 gene than those with wild-type homozygotes, OR (95%CI) were 1.67 (1.23-2.02), 1.59 (1.25-2.01) and 1.42 (1.15-1.82), respectively. GMDR analysis indicated a significant two-locus model (p=0.0010) involving rs603965 within CCND1 gene and rs1346787 within EFEMP1 gene. Overall, the cross-validation consistency of the two- locus models was 10\\ 10, and the testing accuracy is 60.17%. Participants with rs603965 - GA or AA and rs1346787- AG or GG genotype have the highest glioma risk, compared to participants with rs603965 - GG and rs1346787- AA genotype, OR (95%CI) was 3.65 (1.81-5.22). We conducted haplotype analysis for rs1346787 and rs3791679, because D' value between rs1346787 and rs3791679 was more than 0.8. The most common haplotype was rs1346787 - A and rs3791679- G haplotype, the frequency of which was 0.4905 and 0.4428 in case and control group. Polymorphism in rs603965 within CCND1 gene and rs1346787 within EFEMP1 gene and its gene- gene interaction were associated with increased glioma risk.
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Single-target regulators form a minor group of transcription factors in Escherichia coli K-12.
Shimada, Tomohiro; Ogasawara, Hiroshi; Ishihama, Akira
2018-05-04
The identification of regulatory targets of all TFs is critical for understanding the entire network of the genome regulation. The lac regulon of Escherichia coli K-12 W3110 is composed of the lacZYA operon and its repressor lacI gene, and has long been recognized as the seminal model of transcription regulation in bacteria with only one highly preferred target. After the Genomic SELEX screening in vitro of more than 200 transcription factors (TFs) from E. coli K-12, however, we found that most TFs regulate multiple target genes. With respect to the number of regulatory targets, a total of these 200 E. coli TFs form a hierarchy ranging from a single target to as many as 1000 targets. Here we focus a total of 13 single-target TFs, 9 known TFs (BetI, KdpE, LacI, MarR, NanR, RpiR, TorR, UlaR and UxuR) and 4 uncharacterized TFs (YagI, YbaO, YbiH and YeaM), altogether forming only a minor group of TFs in E. coli. These single-target TFs were classified into three groups based on their functional regulation.
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Single-target regulators form a minor group of transcription factors in Escherichia coli K-12
Shimada, Tomohiro; Ogasawara, Hiroshi; Ishihama, Akira
2018-01-01
Abstract The identification of regulatory targets of all TFs is critical for understanding the entire network of the genome regulation. The lac regulon of Escherichia coli K-12 W3110 is composed of the lacZYA operon and its repressor lacI gene, and has long been recognized as the seminal model of transcription regulation in bacteria with only one highly preferred target. After the Genomic SELEX screening in vitro of more than 200 transcription factors (TFs) from E. coli K-12, however, we found that most TFs regulate multiple target genes. With respect to the number of regulatory targets, a total of these 200 E. coli TFs form a hierarchy ranging from a single target to as many as 1000 targets. Here we focus a total of 13 single-target TFs, 9 known TFs (BetI, KdpE, LacI, MarR, NanR, RpiR, TorR, UlaR and UxuR) and 4 uncharacterized TFs (YagI, YbaO, YbiH and YeaM), altogether forming only a minor group of TFs in E. coli. These single-target TFs were classified into three groups based on their functional regulation. PMID:29529243
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Han, Xiaoping; Chen, Haide; Huang, Daosheng; Chen, Huidong; Fei, Lijiang; Cheng, Chen; Huang, He; Yuan, Guo-Cheng; Guo, Guoji
2018-04-05
Human pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved. We use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells. Our single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.
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Gerstner, Arpad; DeFord, James H; Papaconstantinou, John
2003-07-25
Ames dwarfism is caused by a homozygous single nucleotide mutation in the pituitary specific prop-1 gene, resulting in combined pituitary hormone deficiency, reduced growth and extended lifespan. Thus, these mice serve as an important model system for endocrinological, aging and longevity studies. Because the phenotype of wild type and heterozygous mice is undistinguishable, it is imperative for successful breeding to accurately genotype these animals. Here we report a novel, yet simple, approach for prop-1 genotyping using PCR-based allele-specific amplification (PCR-ASA). We also compare this method to other potential genotyping techniques, i.e. PCR-based restriction fragment length polymorphism analysis (PCR-RFLP) and fluorescence automated DNA sequencing. We demonstrate that the single-step PCR-ASA has several advantages over the classical PCR-RFLP because the procedure is simple, less expensive and rapid. To further increase the specificity and sensitivity of the PCR-ASA, we introduced a single-base mismatch at the 3' penultimate position of the mutant primer. Our results also reveal that the fluorescence automated DNA sequencing has limitations for detecting a single nucleotide polymorphism in the prop-1 gene, particularly in heterozygotes.
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Nik-Ahd, Farnoosh; Bertoni, Carmen
2014-07-01
Duchenne muscular dystrophy (DMD) is a fatal disease caused by mutations in the dystrophin gene, which result in the complete absence of dystrophin protein throughout the body. Gene correction strategies hold promise to treating DMD. Our laboratory has previously demonstrated the ability of peptide nucleic acid single-stranded oligodeoxynucleotides (PNA-ssODNs) to permanently correct single-point mutations at the genomic level. In this study, we show that PNA-ssODNs can target and correct muscle satellite cells (SCs), a population of stem cells capable of self-renewing and differentiating into muscle fibers. When transplanted into skeletal muscles, SCs transfected with correcting PNA-ssODNs were able to engraft and to restore dystrophin expression. The number of dystrophin-positive fibers was shown to significantly increase over time. Expression was confirmed to be the result of the activation of a subpopulation of SCs that had undergone repair as demonstrated by immunofluorescence analyses of engrafted muscles using antibodies specific to full-length dystrophin transcripts and by genomic DNA analysis of dystrophin-positive fibers. Furthermore, the increase in dystrophin expression detected over time resulted in a significant improvement in muscle morphology. The ability of transplanted cells to return into quiescence and to activate upon demand was confirmed in all engrafted muscles following injury. These results demonstrate the feasibility of using gene editing strategies to target and correct SCs and further establish the therapeutic potential of this approach to permanently restore dystrophin expression into muscle of DMD patients. © 2014 AlphaMed Press.
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Serie, Daniel J.; Crook, Julia E.; Necela, Brian M.; Axenfeld, Bianca C.; Dockter, Travis J.; Colon-Otero, Gerardo; Perez, Edith A.; Thompson, E. Aubrey; Norton, Nadine
2017-01-01
Doxorubicin and the ERBB2 targeted therapy, trastuzumab, are routinely used in the treatment of HER2+ breast cancer. In mouse models, doxorubicin is known to cause cardiomyopathy and conditional cardiac knock out of Erbb2 results in dilated cardiomyopathy and increased sensitivity to doxorubicin-induced cell death. In humans, these drugs also result in cardiac phenotypes, but severity and reversibility is highly variable. We examined the association of decline in left ventricular ejection fraction (LVEF) at 15,204 single nucleotide polymorphisms (SNPs) spanning 72 cardiomyopathy genes, in 800 breast cancer patients who received doxorubicin and trastuzumab. For 7033 common SNPs (minor allele frequency (MAF) > 0.01) we performed single marker linear regression. For all SNPs, we performed gene-based testing with SNP-set (Sequence) Kernel Association Tests: SKAT, SKAT-O and SKAT-common/rare under rare variant non-burden; rare variant optimized burden and non-burden tests; and a combination of rare and common variants respectively. Single marker analyses identified seven missense variants in OBSCN (p = 0.0045–0.0009, MAF = 0.18–0.50) and two in TTN (both p = 0.04, MAF = 0.22). Gene-based rare variant analyses, SKAT and SKAT-O, performed very similarly (ILK, TCAP, DSC2, VCL, FXN, DSP and KCNQ1, p = 0.042–0.006). Gene-based tests of rare/common variants were significant at the nominal 5% level for OBSCN as well as TCAP, DSC2, VCL, NEXN, KCNJ2 and DMD (p = 0.044–0.008). Our results suggest that rare and common variants in OBSCN, as well as in other genes, could have modifying effects in cardiomyopathy. PMID:29367538
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Bortolussi, Giulia; Zentilin, Lorena; Baj, Gabriele; Giraudi, Pablo; Bellarosa, Cristina; Giacca, Mauro; Tiribelli, Claudio; Muro, Andrés F.
2012-01-01
Crigler-Najjar type I (CNI) syndrome is a recessively inherited disorder characterized by severe unconjugated hyperbilirubinemia caused by uridine diphosphoglucuronosyltransferase 1A1 (UGT1A1) deficiency. The disease is lethal due to bilirubin-induced neurological damage unless phototherapy is applied from birth. However, treatment becomes less effective during growth, and liver transplantation is required. To investigate the pathophysiology of the disease and therapeutic approaches in mice, we generated a mouse model by introducing a premature stop codon in the UGT1a1 gene, which results in an inactive enzyme. Homozygous mutant mice developed severe jaundice soon after birth and died within 11 d, showing significant cerebellar alterations. To rescue neonatal lethality, newborns were injected with a single dose of adeno-associated viral vector 9 (AAV9) expressing the human UGT1A1. Gene therapy treatment completely rescued all AAV-treated mutant mice, accompanied by lower plasma bilirubin levels and normal brain histology and motor coordination. Our mouse model of CNI reproduces genetic and phenotypic features of the human disease. We have shown, for the first time, the full recovery of the lethal effects of neonatal hyperbilirubinemia. We believe that, besides gene-addition-based therapies, our mice could represent a very useful model to develop and test novel technologies based on gene correction by homologous recombination.—Bortolussi, G., Zentilin, L., Baj, G., Giraudi, P., Bellarosa, C., Giacca, M., Tiribelli, C., Muro, A. F. Rescue of bilirubin-induced neonatal lethality in a mouse model of Crigler-Najjar syndrome type I by AAV9-mediated gene transfer. PMID:22094718
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Hood, Heather M.; Ocasio, Linda R.; Sachs, Matthew S.; Galagan, James E.
2013-01-01
The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms. PMID:23935467
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Bialk, Pawel; Rivera-Torres, Natalia; Strouse, Bryan; Kmiec, Eric B.
2015-01-01
Single-stranded DNA oligonucleotides (ssODNs) can direct the repair of a single base mutation in human genes. While the regulation of this gene editing reaction has been partially elucidated, the low frequency with which repair occurs has hampered development toward clinical application. In this work a CRISPR/Cas9 complex is employed to induce double strand DNA breakage at specific sites surrounding the nucleotide designated for exchange. The result is a significant elevation in ssODN-directed gene repair, validated by a phenotypic readout. By analysing reaction parameters, we have uncovered restrictions on gene editing activity involving CRISPR/Cas9 complexes. First, ssODNs that hybridize to the non-transcribed strand direct a higher level of gene repair than those that hybridize to the transcribed strand. Second, cleavage must be proximal to the targeted mutant base to enable higher levels of gene editing. Third, DNA cleavage enables a higher level of gene editing activity as compared to single-stranded DNA nicks, created by modified Cas9 (Nickases). Fourth, we calculated the hybridization potential and free energy levels of ssODNs that are complementary to the guide RNA sequences of CRISPRs used in this study. We find a correlation between free energy potential and the capacity of single-stranded oligonucleotides to inhibit specific DNA cleavage activity, thereby indirectly reducing gene editing activity. Our data provide novel information that might be taken into consideration in the design and usage of CRISPR/Cas9 systems with ssODNs for gene editing. PMID:26053390
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Bialk, Pawel; Rivera-Torres, Natalia; Strouse, Bryan; Kmiec, Eric B
2015-01-01
Single-stranded DNA oligonucleotides (ssODNs) can direct the repair of a single base mutation in human genes. While the regulation of this gene editing reaction has been partially elucidated, the low frequency with which repair occurs has hampered development toward clinical application. In this work a CRISPR/Cas9 complex is employed to induce double strand DNA breakage at specific sites surrounding the nucleotide designated for exchange. The result is a significant elevation in ssODN-directed gene repair, validated by a phenotypic readout. By analysing reaction parameters, we have uncovered restrictions on gene editing activity involving CRISPR/Cas9 complexes. First, ssODNs that hybridize to the non-transcribed strand direct a higher level of gene repair than those that hybridize to the transcribed strand. Second, cleavage must be proximal to the targeted mutant base to enable higher levels of gene editing. Third, DNA cleavage enables a higher level of gene editing activity as compared to single-stranded DNA nicks, created by modified Cas9 (Nickases). Fourth, we calculated the hybridization potential and free energy levels of ssODNs that are complementary to the guide RNA sequences of CRISPRs used in this study. We find a correlation between free energy potential and the capacity of single-stranded oligonucleotides to inhibit specific DNA cleavage activity, thereby indirectly reducing gene editing activity. Our data provide novel information that might be taken into consideration in the design and usage of CRISPR/Cas9 systems with ssODNs for gene editing.
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Haus, Sylvia; Jabbari, Sara; Millat, Thomas; Janssen, Holger; Fischer, Ralf-Jörg; Bahl, Hubert; King, John R; Wolkenhauer, Olaf
2011-01-19
Clostridium acetobutylicum is an anaerobic bacterium which is known for its solvent-producing capabilities, namely regarding the bulk chemicals acetone and butanol, the latter being a highly efficient biofuel. For butanol production by C. acetobutylicum to be optimized and exploited on an industrial scale, the effect of pH-induced gene regulation on solvent production by C. acetobutylicum in continuous culture must be understood as fully as possible. We present an ordinary differential equation model combining the metabolic network governing solvent production with regulation at the genetic level of the enzymes required for this process. Parameterizing the model with experimental data from continuous culture, we demonstrate the influence of pH upon fermentation products: at high pH (pH 5.7) acids are the dominant product while at low pH (pH 4.5) this switches to solvents. Through steady-state analyses of the model we focus our investigations on how alteration in gene expression of C. acetobutylicum could be exploited to increase butanol yield in a continuous culture fermentation. Incorporating gene regulation into the model of solvent production by C. acetobutylicum enables an accurate representation of the pH-induced switch to solvent production to be obtained and theoretical investigations of possible synthetic-biology approaches to be pursued. Steady-state analyses suggest that, to increase butanol yield, alterations in the expression of single solvent-associated genes are insufficient; a more complex approach targeting two or more genes is required.
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2011-01-01
Background Clostridium acetobutylicum is an anaerobic bacterium which is known for its solvent-producing capabilities, namely regarding the bulk chemicals acetone and butanol, the latter being a highly efficient biofuel. For butanol production by C. acetobutylicum to be optimized and exploited on an industrial scale, the effect of pH-induced gene regulation on solvent production by C. acetobutylicum in continuous culture must be understood as fully as possible. Results We present an ordinary differential equation model combining the metabolic network governing solvent production with regulation at the genetic level of the enzymes required for this process. Parameterizing the model with experimental data from continuous culture, we demonstrate the influence of pH upon fermentation products: at high pH (pH 5.7) acids are the dominant product while at low pH (pH 4.5) this switches to solvents. Through steady-state analyses of the model we focus our investigations on how alteration in gene expression of C. acetobutylicum could be exploited to increase butanol yield in a continuous culture fermentation. Conclusions Incorporating gene regulation into the model of solvent production by C. acetobutylicum enables an accurate representation of the pH-induced switch to solvent production to be obtained and theoretical investigations of possible synthetic-biology approaches to be pursued. Steady-state analyses suggest that, to increase butanol yield, alterations in the expression of single solvent-associated genes are insufficient; a more complex approach targeting two or more genes is required. PMID:21247470
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Interleukin gene polymorphisms in Chinese Han population with breast cancer, a case-control study.
Zuo, Xiaoxiao; Li, Miao; Yang, Ya; Liang, Tiansong; Yang, Hongyao; Zhao, Xinhan; Yang, Daoke
2018-04-06
Cytokines are known as important regulators of the cancer involved in inflammatory and immunological responses. This fact and plethora of gene polymorphism data prompted us to investigate IL1 gene polymorphisms in breast cancer (BC) patients. Totally, 530 patients with BC and 628 healthy control women were studied. The genetic polymorphisms for IL1 were analyzed by Massarray Sequencing method. Three single nucleotide polymorphisms (SNPs) identified in IL1B, IL1R1 gene are thought to influence breast cancer risk. The results of the association between IL-1B, IL1R1 polymorphisms and breast cancer risk have significant. We found that the variant TT genotype of rs10490571 was associated with a significantly increased breast cancer risk (TT vs. CC: OR = 2.82, 95% CI = 1.12-7.08, P = 0.047 for the codominant model). For rs16944 (AG vs. GG: OR = 0.60, 95% CI = 0.41-0.90, P = 0.034 for the codominant model) and rs1143623 (CG vs. CC: OR = 0.65, 95% CI = 0.45-0.94, P = 0.023 for the codominant model) have significant associations were found in genetic models. In conclusion, the present analysis suggests a correlation of polymorphic markers within the IL-1 gene locus with the risk in developing breast cancer. Taken together with our finding that IL1B, IL1R1 gene three SNP are also associated with the risk for the disease, we suggest that inflammation via innate and adaptive immunity contributes to multifactorial hereditary predisposition to pathogenesis of the breast cancer.
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Gurramkonda, Venkatesh Babu; Syed, Altaf Hussain; Murthy, Jyotsna; Lakkakula, Bhaskar V K S
2017-06-26
Transcription factors are very diverse family of proteins involved in activating or repressing the transcription of a gene at a given time. Several studies using animal models demonstrated the role of transcription factor genes in craniofacial development. We aimed to investigate the association of IRF6 intron-6 polymorphism in the non-syndromic cleft lip with or without Palate in a south Indian population. 173 unrelated nonsyndromic cleft lip with or without Palate patients and 176 controls without clefts patients were genotyped for IRF6 rs2235375 variant by allele-specific amplification using the KASPar single nucleotide polymorphism genotyping system. The association between interferon regulatory factor-6 gene intron-6 dbSNP208032210:g.G>C (rs2235375) single nucleotide polymorphism and non-syndromic cleft lip with or without palate risk was investigated by chi-square test. There were significant differences in genotype or allele frequencies of rs2235375 single nucleotide polymorphism between controls and cases with non-syndromic cleft lip with or without palate. IRF6 rs2235375 variant was significantly associated with increased risk of non-syndromic cleft lip with or without palate in co-dominant, dominant (OR: 1.19; 95% CI 1.03-2.51; p=0.034) and allelic models (OR: 1.40; 95% CI 1.04-1.90; p=0.028). When subset analysis was applied significantly increased risk was observed in cleft palate only group (OR dominant: 4.33; 95% CI 1.44-12.97; p=0.005). These results suggest that IRF6 rs2235375 SNP play a major role in the pathogenesis and risk of developing non-syndromic cleft lip with or without palate. Copyright © 2017 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.
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McDonald, Jacqueline U.; Kaforou, Myrsini; Clare, Simon; Hale, Christine; Ivanova, Maria; Huntley, Derek; Dorner, Marcus; Wright, Victoria J.; Levin, Michael; Martinon-Torres, Federico; Herberg, Jethro A.
2016-01-01
ABSTRACT Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases. PMID:27822537
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Sczyrba, Alex
2018-02-13
DOE JGI's Alex Sczyrba on "Evaluation of the Cow Rumen Metagenome" and "Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sczyrba, Alex
2011-10-13
DOE JGI's Alex Sczyrba on "Evaluation of the Cow Rumen Metagenome" and "Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.
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Patel, Vidushi S; Cooper, Steven J B; Deakin, Janine E; Fulton, Bob; Graves, Tina; Warren, Wesley C; Wilson, Richard K; Graves, Jennifer A M
2008-07-25
Vertebrate alpha (alpha)- and beta (beta)-globin gene families exemplify the way in which genomes evolve to produce functional complexity. From tandem duplication of a single globin locus, the alpha- and beta-globin clusters expanded, and then were separated onto different chromosomes. The previous finding of a fossil beta-globin gene (omega) in the marsupial alpha-cluster, however, suggested that duplication of the alpha-beta cluster onto two chromosomes, followed by lineage-specific gene loss and duplication, produced paralogous alpha- and beta-globin clusters in birds and mammals. Here we analyse genomic data from an egg-laying monotreme mammal, the platypus (Ornithorhynchus anatinus), to explore haemoglobin evolution at the stem of the mammalian radiation. The platypus alpha-globin cluster (chromosome 21) contains embryonic and adult alpha- globin genes, a beta-like omega-globin gene, and the GBY globin gene with homology to cytoglobin, arranged as 5'-zeta-zeta'-alphaD-alpha3-alpha2-alpha1-omega-GBY-3'. The platypus beta-globin cluster (chromosome 2) contains single embryonic and adult globin genes arranged as 5'-epsilon-beta-3'. Surprisingly, all of these globin genes were expressed in some adult tissues. Comparison of flanking sequences revealed that all jawed vertebrate alpha-globin clusters are flanked by MPG-C16orf35 and LUC7L, whereas all bird and mammal beta-globin clusters are embedded in olfactory genes. Thus, the mammalian alpha- and beta-globin clusters are orthologous to the bird alpha- and beta-globin clusters respectively. We propose that alpha- and beta-globin clusters evolved from an ancient MPG-C16orf35-alpha-beta-GBY-LUC7L arrangement 410 million years ago. A copy of the original beta (represented by omega in marsupials and monotremes) was inserted into an array of olfactory genes before the amniote radiation (>315 million years ago), then duplicated and diverged to form orthologous clusters of beta-globin genes with different expression profiles in different lineages.
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Holen, Elisabeth; He, Juyun; Espe, Marit; Chen, Liqiou; Araujo, Pedro
2015-08-01
Future feed for farmed fish are based on untraditional feed ingredients, which will change nutrient profiles compared to traditional feed based on marine ingredients. To understand the impact of oils from different sources on fish health, n-6 and n-3 polyunsaturated fatty acids (PUFAs) were added to salmon head kidney cells, in a fully crossed design, to monitor their individual and combined effects on gene expression. Exposing salmon head kidney cells to single fatty acids, arachidonic acid (AA) or decosahexaenoic acid (DHA), resulted in down-regulation of cell signaling pathway genes and specific fatty acid metabolism genes as well as reduced prostaglandin E2 (PGE2) secretion. Eicosapentaenoic acid (EPA) had no impact on gene transcription in this study, but reduced the cell secretion of PGE2. The combined effect of AA + EPA resulted in up-regulation of eicosanoid pathway genes and the pro-inflammatory cytokine, tumor necrosis factor alpha (TNF-α), Bclx (an inducer of apoptosis) and fatty acid translocase (CD36) as well as increased cell secretion of PGE2 into the media. Adding single fatty acids to salmon head kidney cells decreased inflammation markers in this model. The combination AA + EPA acted differently than the rest of the fatty acid combinations by increasing the inflammation markers in these cells. The concentration of fatty acid used in this experiment did not induce any lipid peroxidation responses. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Efficient strategy for detecting gene × gene joint action and its application in schizophrenia.
Won, Sungho; Kwon, Min-Seok; Mattheisen, Manuel; Park, Suyeon; Park, Changsoon; Kihara, Daisuke; Cichon, Sven; Ophoff, Roel; Nöthen, Markus M; Rietschel, Marcella; Baur, Max; Uitterlinden, Andre G; Hofmann, A; Lange, Christoph
2014-01-01
We propose a new approach to detect gene × gene joint action in genome-wide association studies (GWASs) for case-control designs. This approach offers an exhaustive search for all two-way joint action (including, as a special case, single gene action) that is computationally feasible at the genome-wide level and has reasonable statistical power under most genetic models. We found that the presence of any gene × gene joint action may imply differences in three types of genetic components: the minor allele frequencies and the amounts of Hardy-Weinberg disequilibrium may differ between cases and controls, and between the two genetic loci the degree of linkage disequilibrium may differ between cases and controls. Using Fisher's method, it is possible to combine the different sources of genetic information in an overall test for detecting gene × gene joint action. The proposed statistical analysis is efficient and its simplicity makes it applicable to GWASs. In the current study, we applied the proposed approach to a GWAS on schizophrenia and found several potential gene × gene interactions. Our application illustrates the practical advantage of the proposed method. © 2013 WILEY PERIODICALS, INC.
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Genome-wide transcriptomics of aging in the rotifer Brachionus manjavacas, an emerging model system.
Gribble, Kristin E; Mark Welch, David B
2017-03-01
Understanding gene expression changes over lifespan in diverse animal species will lead to insights to conserved processes in the biology of aging and allow development of interventions to improve health. Rotifers are small aquatic invertebrates that have been used in aging studies for nearly 100 years and are now re-emerging as a modern model system. To provide a baseline to evaluate genetic responses to interventions that change health throughout lifespan and a framework for new hypotheses about the molecular genetic mechanisms of aging, we examined the transcriptome of an asexual female lineage of the rotifer Brachionus manjavacas at five life stages: eggs, neonates, and early-, late-, and post-reproductive adults. There are widespread shifts in gene expression over the lifespan of B. manjavacas; the largest change occurs between neonates and early reproductive adults and is characterized by down-regulation of developmental genes and up-regulation of genes involved in reproduction. The expression profile of post-reproductive adults was distinct from that of other life stages. While few genes were significantly differentially expressed in the late- to post-reproductive transition, gene set enrichment analysis revealed multiple down-regulated pathways in metabolism, maintenance and repair, and proteostasis, united by genes involved in mitochondrial function and oxidative phosphorylation. This study provides the first examination of changes in gene expression over lifespan in rotifers. We detected differential expression of many genes with human orthologs that are absent in Drosophila and C. elegans, highlighting the potential of the rotifer model in aging studies. Our findings suggest that small but coordinated changes in expression of many genes in pathways that integrate diverse functions drive the aging process. The observation of simultaneous declines in expression of genes in multiple pathways may have consequences for health and longevity not detected by single- or multi-gene knockdown in otherwise healthy animals. Investigation of subtle but genome-wide change in these pathways during aging is an important area for future study.
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A CD133-expressing murine liver oval cell population with bilineage potential.
Rountree, C Bart; Barsky, Lora; Ge, Shundi; Zhu, Judy; Senadheera, Shantha; Crooks, Gay M
2007-10-01
Although oval cells are postulated to be adult liver stem cells, a well-defined phenotype of a bipotent liver stem cell remains elusive. The heterogeneity of cells within the oval cell fraction has hindered lineage potential studies. Our goal was to identify an enriched population of bipotent oval cells using a combination of flow cytometry and single cell gene expression in conjunction with lineage-specific liver injury models. Expression of cell surface markers on nonparenchymal, nonhematopoietic (CD45-) cells were characterized. Cell populations were isolated by flow cytometry for gene expression studies. 3,5-Diethoxycarbonyl-1,4-dihydrocollidine toxic injury induced cell cycling and expansion specifically in the subpopulation of oval cells in the periportal zone that express CD133. CD133+CD45- cells expressed hepatoblast and stem cell-associated genes, and single cells coexpressed both hepatocyte and cholangiocyte-associated genes, indicating bilineage potential. CD133+CD45- cells proliferated in response to liver injury. Following toxic hepatocyte damage, CD133+CD45- cells demonstrated upregulated expression of the hepatocyte gene Albumin. In contrast, toxic cholangiocyte injury resulted in upregulation of the cholangiocyte gene Ck19. After 21-28 days in culture, CD133+CD45- cells continued to generate cells of both hepatocyte and cholangiocyte lineages. Thus, CD133 expression identifies a population of oval cells in adult murine liver with the gene expression profile and function of primitive, bipotent liver stem cells. In response to lineage-specific injury, these cells demonstrate a lineage-appropriate genetic response. Disclosure of potential conflicts of interest is found at the end of this article.
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Roberts, Michelle R.; Sucheston-Campbell, Lara E.; Zirpoli, Gary R.; Higgins, Michael; Freudenheim, Jo L.; Bandera, Elisa V.; Ambrosone, Christine B.; Yao, Song
2017-01-01
Background Single nucleotide polymorphisms (SNPs) in pathways influencing lymph node (LN) metastasis and estrogen receptor (ER) status in breast cancer may partially explain inter-patient variability in prognosis. We examined 154 SNPs in 12 metastasis-related genes for associations with breast cancer risk, stratified by LN and ER status, in European-American (EA) and African-American (AA) women. Methods 2,671 women enrolled in the Women’s Circle of Health Study were genotyped. Pathway analyses were conducted using the adaptive rank truncated product (ARTP) method, with pARTP≤0.10 as significant. Multi-allelic risk scores were created for the ARTP-significant gene(s). Single-SNP and risk score associations were modeled using logistic regression, with false discovery rate (FDR) p-value adjustment. Results Although single-SNP associations were not significant at pFDR<0.05, several genes were significant in the ARTP analyses. In AA women, significant ARTP gene-level associations included CDH1 with LN+ (pARTP=0.10; multi-allelic OR=1.13, 95% CI 1.07–1.19, pFDR=0.0003) and SIPA1 with ER− breast cancer (pARTP=0.10; multi-allelic OR=1.16, 95% CI 1.02–1.31, pFDR=0.03). In EA women, MTA2 was associated with overall breast cancer risk (pARTP=0.004), regardless of ER status, and with LN− disease (pARTP=0.01). Also significant were SATB1 in ER− (pARTP=0.03; multi-allelic OR=1.12, 95% CI 1.05–1.20, pFDR=0.003) and KISS1 in LN− (pARTP=0.10; multi-allelic OR=1.18, 95% CI 1.08–1.29, pFDR=0.002) analyses. Among LN+ cases, significant ARTP associations were observed for SNAI1, CD82, NME1, and CTNNB1 (multi-allelic OR=1.09, 95% CI 1.04–1.14, pFDR=0.001). Conclusion Our findings suggest that variants in several metastasis genes may affect breast cancer risk by LN or ER status, although verification in larger studies is required. PMID:27597141
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Mutation spectrum of the rhodopsin gene among patients with autosomal dominant retinitis pigmentosa
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dryja, T.P.; Han, L.B.; Cowley, G.S.
1991-10-15
The authors searched for point mutations in every exon of the rhodopsin gene in 150 patients from separate families with autosomal dominant retinitis pigmentosa. Including the 4 mutations the authors reported previously, they found a total of 17 different mutations that correlate with the disease. Each of these mutations is a single-base substitution corresponding to a single amino acid substitution. Based on current models for the structure of rhodopsin, 3 of the 17 mutant amino acids are normally located on the cytoplasmic side of the protein, 6 in transmembrane domains, and 8 on the intradiscal side. Forty-three of the 150more » patients (29%) carry 1 of these mutations, and no patient has more than 1 mutation. In every family with a mutation so far analyzed, the mutation cosegregates with the disease. They found one instance of a mutation in an affected patient that was absent in both unaffected parents (i.e., a new germ-line mutation), indicating that some isolate cases of retinitis pigmentosa carry a mutation of the rhodopsin gene.« less
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Human cerebral organoids recapitulate gene expression programs of fetal neocortex development
Camp, J. Gray; Badsha, Farhath; Florio, Marta; Kanton, Sabina; Gerber, Tobias; Wilsch-Bräuninger, Michaela; Lewitus, Eric; Sykes, Alex; Hevers, Wulf; Lancaster, Madeline; Knoblich, Juergen A.; Lachmann, Robert; Pääbo, Svante; Huttner, Wieland B.; Treutlein, Barbara
2015-01-01
Cerebral organoids—3D cultures of human cerebral tissue derived from pluripotent stem cells—have emerged as models of human cortical development. However, the extent to which in vitro organoid systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo remains unclear. Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex. Covariation network analysis using the fetal neocortex data reveals known and previously unidentified interactions among genes central to neural progenitor proliferation and neuronal differentiation. In the organoid, we detect diverse progenitors and differentiated cell types of neuronal and mesenchymal lineages and identify cells that derived from regions resembling the fetal neocortex. We find that these organoid cortical cells use gene expression programs remarkably similar to those of the fetal tissue to organize into cerebral cortex-like regions. Our comparison of in vivo and in vitro cortical single-cell transcriptomes illuminates the genetic features underlying human cortical development that can be studied in organoid cultures. PMID:26644564
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TNF-alpha inhibits insulin action in liver and adipose tissue: A model of metabolic syndrome.
Solomon, S S; Odunusi, O; Carrigan, D; Majumdar, G; Kakoola, D; Lenchik, N I; Gerling, I C
2010-02-01
Several studies suggest that TNF-alpha contributes to the development of insulin resistance (IR). We compared transcriptional profiles of rat H-411E liver cells exposed to insulin in the absence or presence of TNF-alpha. We identified 33 genes whose expression was altered by insulin, and then reversed by TNF-alpha. Twenty-six of these 33 genes created a single network centered around: insulin, TNF-alpha, p38-MAPK, TGFb1; SMAD and STAT1; and enzymes and cytokines involved in apoptosis (CASP3, GADD45B, IL2, TNF-alpha, etc.). We analyzed our data together with other data of gene expression in adipocytes and found a number of processes common to both, for example, cell death and inflammation; intercellular signaling and metabolism; G-Protein, IL-10 and PTEN signaling. Moreover, the two datasets combined generated a single molecular network that further identified PTEN (a phosphatase) as a unique new link between insulin signaling, IR, and apoptosis reflecting the pathophysiology of "metabolic syndrome". Georg Thieme Verlag KG Stuttgart * New York.
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The Obligate Human Pathogen, Neisseria gonorrhoeae, Is Polyploid
Tobiason, Deborah M; Seifert, H. Steven
2006-01-01
We show using several methodologies that the Gram-negative, diplococcal-bacterium Neisseria gonorrhoeae has more than one complete genome copy per cell. Gene dosage measurements demonstrated that only a single replication initiation event per chromosome occurs per round of cell division, and that there is a single origin of replication. The region containing the origin does not encode any genes previously associated with bacterial origins of replication. Quantitative PCR results showed that there are on average three genome copies per coccal cell unit. These findings allow a model for gonococcal DNA replication and cell division to be proposed, in which a minimum of two chromosomal copies exist per coccal unit within a monococcal or diplococcal cell, and these chromosomes replicate in unison to produce four chromosomal copies during cell division. Immune evasion via antigenic variation is an important mechanism that allows these organisms to continually infect a high risk population of people. We propose that polyploidy may be necessary for the high frequency gene conversion system that mediates pilin antigenic variation and the propagation of N. gonorrhoeae within its human hosts. PMID:16719561
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Kenny, Nathan J.; Sin, Yung Wa; Shen, Xin; Zhe, Qu; Wang, Wei; Chan, Ting Fung; Tobe, Stephen S.; Shimeld, Sebastian M.; Chu, Ka Hou; Hui, Jerome H. L.
2014-01-01
The speciose Crustacea is the largest subphylum of arthropods on the planet after the Insecta. To date, however, the only publically available sequenced crustacean genome is that of the water flea, Daphnia pulex, a member of the Branchiopoda. While Daphnia is a well-established ecotoxicological model, previous study showed that one-third of genes contained in its genome are lineage-specific and could not be identified in any other metazoan genomes. To better understand the genomic evolution of crustaceans and arthropods, we have sequenced the genome of a novel shrimp model, Neocaridina denticulata, and tested its experimental malleability. A library of 170-bp nominal fragment size was constructed from DNA of a starved single adult and sequenced using the Illumina HiSeq2000 platform. Core eukaryotic genes, the mitochondrial genome, developmental patterning genes (such as Hox) and microRNA processing pathway genes are all present in this animal, suggesting it has not undergone massive genomic loss. Comparison with the published genome of Daphnia pulex has allowed us to reveal 3750 genes that are indeed specific to the lineage containing malacostracans and branchiopods, rather than Daphnia-specific (E-value: 10−6). We also show the experimental tractability of N. denticulata, which, together with the genomic resources presented here, make it an ideal model for a wide range of further aquacultural, developmental, ecotoxicological, food safety, genetic, hormonal, physiological and reproductive research, allowing better understanding of the evolution of crustaceans and other arthropods. PMID:24619275
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Azmi, Asfar S.; Banerjee, Sanjeev; Ali, Shadan; Wang, Zhiwei; Bao, Bin; Beck, Frances W.J.; Maitah, Main; Choi, Minsig; Shields, Tony F.; Philip, Philip A.; Sarkar, Fazlul H.; Mohammad, Ramzi M.
2011-01-01
Earlier we had shown that the MDM2 inhibitor (MI-219) belonging to the spiro-oxindole family can synergistically enhance the efficacy of platinum chemotherapeutics leading to 50% tumor free survival in a genetically complex pancreatic ductal adenocarcinoma (PDAC) xenograft model. In this report, we have taken a systems and network modeling approach in order to understand central mechanisms behind MI219-oxaliplatin synergy with validation in PDAC, colon and breast cancer cell lines. Microarray profiling of drug treatments (MI-219, oxaliplatin or their combination) in capan-2 cells reveal a similar unique set of gene alterations that is duplicated in other solid tumor cells. As single agent, MI-219 or oxaliplatin induced alterations in 48 and 761 genes respectively. The combination treatment resulted in 767 gene alterations with emergence of 286 synergy unique genes. Ingenuity network modeling of combination and synergy unique genes showed the crucial role of five key local networks CREB, CARF, EGR1, NF-kB and E Cadherin. The network signatures were validated at the protein level in all three cell lines. Individually silencing central nodes in these five hubs resulted in abrogation of MI-219-oxaliplatin activity confirming their critical role in aiding p53 mediated apoptotic response. We anticipate that our MI219-oxaliplatin network blueprints can be clinically translated in the rationale design and application of this unique therapeutic combination in a genetically pre-defined subset of patients. PMID:21623005
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A statistical framework for biomedical literature mining.
Chung, Dongjun; Lawson, Andrew; Zheng, W Jim
2017-09-30
In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not only promote understanding of pathway regulation mechanisms but also allow identification of novel targets for therapeutics. Recently, biomedical literature has been considered as a valuable resource to investigate pathway-modulating genes. While the majority of currently available approaches are based on the co-occurrence of genes within an abstract, it has been reported that these approaches show only sub-optimal performances because 70% of abstracts contain information only for a single gene. To overcome such limitation, we propose a novel statistical framework based on the concept of ontology fingerprint that uses gene ontology to extract information from large biomedical literature data. The proposed framework simultaneously identifies pathway-modulating genes and facilitates interpreting functions of these new genes. We also propose a computationally efficient posterior inference procedure based on Metropolis-Hastings within Gibbs sampler for parameter updates and the poor man's reversible jump Markov chain Monte Carlo approach for model selection. We evaluate the proposed statistical framework with simulation studies, experimental validation, and an application to studies of pathway-modulating genes in yeast. The R implementation of the proposed model is currently available at https://dongjunchung.github.io/bayesGO/. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
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Impact of Cigarette Smoke on the Human and Mouse Lungs: A Gene-Expression Comparison Study
Morissette, Mathieu C.; Lamontagne, Maxime; Bérubé, Jean-Christophe; Gaschler, Gordon; Williams, Andrew; Yauk, Carole; Couture, Christian; Laviolette, Michel; Hogg, James C.; Timens, Wim; Halappanavar, Sabina; Stampfli, Martin R.; Bossé, Yohan
2014-01-01
Cigarette smoke is well known for its adverse effects on human health, especially on the lungs. Basic research is essential to identify the mechanisms involved in the development of cigarette smoke-related diseases, but translation of new findings from pre-clinical models to the clinic remains difficult. In the present study, we aimed at comparing the gene expression signature between the lungs of human smokers and mice exposed to cigarette smoke to identify the similarities and differences. Using human and mouse whole-genome gene expression arrays, changes in gene expression, signaling pathways and biological functions were assessed. We found that genes significantly modulated by cigarette smoke in humans were enriched for genes modulated by cigarette smoke in mice, suggesting a similar response of both species. Sixteen smoking-induced genes were in common between humans and mice including six newly reported to be modulated by cigarette smoke. In addition, we identified a new conserved pulmonary response to cigarette smoke in the induction of phospholipid metabolism/degradation pathways. Finally, the majority of biological functions modulated by cigarette smoke in humans were also affected in mice. Altogether, the present study provides information on similarities and differences in lung gene expression response to cigarette smoke that exist between human and mouse. Our results foster the idea that animal models should be used to study the involvement of pathways rather than single genes in human diseases. PMID:24663285
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Huntley, Stuart; Baggott, Daniel M.; Hamilton, Aaron T.; Tran-Gyamfi, Mary; Yang, Shan; Kim, Joomyeong; Gordon, Laurie; Branscomb, Elbert; Stubbs, Lisa
2006-01-01
Krüppel-type zinc finger (ZNF) motifs are prevalent components of transcription factor proteins in all eukaryotes. KRAB-ZNF proteins, in which a potent repressor domain is attached to a tandem array of DNA-binding zinc-finger motifs, are specific to tetrapod vertebrates and represent the largest class of ZNF proteins in mammals. To define the full repertoire of human KRAB-ZNF proteins, we searched the genome sequence for key motifs and then constructed and manually curated gene models incorporating those sequences. The resulting gene catalog contains 423 KRAB-ZNF protein-coding loci, yielding alternative transcripts that altogether predict at least 742 structurally distinct proteins. Active rounds of segmental duplication, involving single genes or larger regions and including both tandem and distributed duplication events, have driven the expansion of this mammalian gene family. Comparisons between the human genes and ZNF loci mined from the draft mouse, dog, and chimpanzee genomes not only identified 103 KRAB-ZNF genes that are conserved in mammals but also highlighted a substantial level of lineage-specific change; at least 136 KRAB-ZNF coding genes are primate specific, including many recent duplicates. KRAB-ZNF genes are widely expressed and clustered genes are typically not coregulated, indicating that paralogs have evolved to fill roles in many different biological processes. To facilitate further study, we have developed a Web-based public resource with access to gene models, sequences, and other data, including visualization tools to provide genomic context and interaction with other public data sets. PMID:16606702
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Singh, Vineet K; Ring, Robert P; Aswani, Vijay; Stemper, Mary E; Kislow, Jennifer; Ye, Zhan; Shukla, Sanjay K
2017-12-01
Staphylococcus aureus is an opportunistic human pathogen that can cause serious infections in humans. A plethora of known and putative virulence factors are produced by staphylococci that collectively orchestrate pathogenesis. Ear protein (Escherichia coli ampicillin resistance) in S. aureus is an exoprotein in COL strain, predicted to be a superantigen, and speculated to play roles in antibiotic resistance and virulence. The goal of this study was to determine if expression of ear is modulated by single nucleotide polymorphisms in its promoter and coding sequences and whether this gene plays roles in antibiotic resistance and virulence. Promoter, coding sequences and expression of the ear gene in clinical and carriage S. aureus strains with distinct genetic backgrounds were analysed. The JE2 strain and its isogenic ear mutant were used in a systemic infection mouse model to determine the competiveness of the ear mutant.Results/Key findings. The ear gene showed a variable expression, with USA300FPR3757 showing a high-level expression compared to many of the other strains tested including some showing negligible expression. Higher expression was associated with agr type 1 but not correlated with phylogenetic relatedness of the ear gene based upon single nucleotide polymorphisms in the promoter or coding regions suggesting a complex regulation. An isogenic JE2 (USA300 background) ear mutant showed no significant difference in its growth, antibiotic susceptibility or virulence in a mouse model. Our data suggests that despite being highly expressed in a USA300 genetic background, Ear is not a significant contributor to virulence in that strain.
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Nagaie, Satoshi; Ogishima, Soichi; Nakaya, Jun; Tanaka, Hiroshi
2015-01-01
Genome-wide association studies (GWAS) and linkage analysis has identified many single nucleotide polymorphisms (SNPs) related to disease. There are many unknown SNPs whose minor allele frequencies (MAFs) as low as 0.005 having intermediate effects with odds ratio between 1.5~3.0. Low frequency variants having intermediate effects on disease pathogenesis are believed to have complex interactions with environmental factors called gene-environment interactions (GxE). Hence, we describe a model using 3D Manhattan plot called GxE landscape plot to visualize the association of p-values for gene-environment interactions (GxE). We used the Gene-Environment iNteraction Simulator 2 (GENS2) program to simulate interactions between two genetic loci and one environmental factor in this exercise. The dataset used for training contains disease status, gender, 20 environmental exposures and 100 genotypes for 170 subjects, and p-values were calculated by Cochran-Mantel-Haenszel chi-squared test on known data. Subsequently, we created a 3D GxE landscape plot of negative logarithm of the association of p-values for all the possible combinations of genetic and environmental factors with their hierarchical clustering. Thus, the GxE landscape plot is a valuable model to predict association of p-values for GxE and similarity among genotypes and environments in the context of disease pathogenesis. GxE - Gene-environment interactions, GWAS - Genome-wide association study, MAFs - Minor allele frequencies, SNPs - Single nucleotide polymorphisms, EWAS - Environment-wide association study, FDR - False discovery rate, JPT+CHB - HapMap population of Japanese in Tokyo, Japan - Han Chinese in Beijing.
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Raihan, Mohammad Sharif; Liu, Jie; Huang, Juan; Guo, Huan; Pan, Qingchun; Yan, Jianbing
2016-08-01
Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630 Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9 cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7 % of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10 % of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68 Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630 Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.
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RNA degradation and models for post-transcriptional gene-silencing.
Meins, F
2000-06-01
Post-transcriptional gene silencing (PTGS) is a form of stable but potentially reversible epigenetic modification, which frequently occurs in transgenic plants. The interaction in trans of genes with similar transcribed sequences results in sequence-specific degradation of RNAs derived from the genes involved. Highly expressed single-copy loci, transcribed inverted repeats, and poorly transcribed complex loci can act as sources of signals that trigger PTGS. In some cases, mobile, sequence-specific silencing signals can move from cell to cell or even over long distances in the plant. Several current models hold that silencing signals are 'aberrant' RNAs (aRNA), which differ in some way from normal mRNAs. The most likely candidates are small antisense RNAs (asRNA) and double-stranded RNAs (dsRNA). Direct evidence that these or other aRNAs found in silent tissues can induce PTGS is still lacking. Most current models assume that silencing signals interact with target RNAs in a sequence-specific fashion. This results in degradation, usually in the cytoplasm, by exonucleolytic as well as endonucleolytic pathways, which are not necessarily PTGS-specific. Biochemical-switch models hold that the silent state is maintained by a positive auto-regulatory loop. One possibility is that concentrations of hypothetical silencing signals above a critical threshold trigger their own production by self-replication, by degradation of target RNAs, or by a combination of both mechanisms. These models can account for the stability, reversibility and multiplicity of silent states; the strong influence of transcription rate of target genes on the incidence and stability of silencing, and the amplification and systemic propagation of motile silencing signals.
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Pan- and core- network analysis of co-expression genes in a model plant
He, Fei; Maslov, Sergei
2016-12-16
Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ andmore » ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. Here, we showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.« less
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Hughes, Michael P; Smith, Dave A; Morris, Lauren; Fletcher, Claire; Colaco, Alexandria; Huebecker, Mylene; Tordo, Julie; Palomar, Nuria; Massaro, Giulia; Henckaerts, Els; Waddington, Simon N; Platt, Frances M; Rahim, Ahad A
2018-06-05
Niemann-Pick type C disease (NP-C) is a fatal neurodegenerative lysosomal storage disorder. It is caused in 95% of cases by a mutation in the NPC1 gene that encodes NPC1, an integral transmembrane protein localised to the limiting membrane of the lysosome. There is no cure for NP-C but there is a disease-modifying drug (miglustat) that slows disease progression but with associated side effects. Here, we demonstrate in a well-characterised mouse model of NP-C that a single administration of AAV-mediated gene therapy to the brain can significantly extend lifespan, improve quality of life, prevent or ameliorate neurodegeneration, reduce biochemical pathology and normalize or improve various indices of motor function. Over-expression of human NPC1 does not cause adverse effects in the brain and correctly localises to late endosomal/lysosomal compartments. Furthermore, we directly compare gene therapy to licensed miglustat. Even at a low dose, gene therapy has all the benefits of miglustat but without adverse effects. On the basis of these findings and on-going ascendency of the field, we propose intracerebroventricular gene therapy as a potential therapeutic option for clinical use in NP-C.
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Pan- and core- network analysis of co-expression genes in a model plant
DOE Office of Scientific and Technical Information (OSTI.GOV)
He, Fei; Maslov, Sergei
Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ andmore » ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. Here, we showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.« less
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Zhang, Ya-Jie; Li, Lei; Wang, Zhen-Jing; Zhang, Xiao-Jing; Zhao, Han; Zhao, Yan; Wang, Xie-Tong; Li, Chang-Zhong; Wan, Ji-Peng
2018-05-17
To evaluate the association between preeclampsia and three single nucleotide polymorphisms (rs13405728 in LHCGR gene; rs13429458 in THADA gene, and rs2479106 in DENND1A gene) which were identified to be genetic variants of polycystic ovary syndrome (PCOS) by genome-wide association study in Han Chinese populations. A total of 784 northern Han Chinese women (378 controls and 406 cases) were genotyped for the three genetic variants by polymerase chain reaction and direct sequencing. Unconditional logistic regression analysis was used to adjust the impact of prepregnancy body mass index, primiparas, and maternal age. No significant difference was found in the allele frequencies of the three genetic variants between cases and controls (p > .05), but genotype frequency of the SNP rs2479106 was significantly differ between cases and controls when analyzed under recessive models (p = .02). There was also a substantial difference in the genotype frequencies of the SNP rs13429458 between cases and controls under additive models (p = .01). Genetic variants of PCOS (rs13405728 in LHCGR gene; rs13429458 in THADA gene and rs2479106 in DENND1A gene) may not be involved in the development of preeclampsia in Han Chinese women.
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Expression of the Bovine NK-Lysin Gene Family and Activity against Respiratory Pathogens.
Chen, Junfeng; Yang, Chingyuan; Tizioto, Polyana C; Huang, Huan; Lee, Mi O K; Payne, Harold R; Lawhon, Sara D; Schroeder, Friedhelm; Taylor, Jeremy F; Womack, James E
2016-01-01
Unlike the genomes of many mammals that have a single NK-lysin gene, the cattle genome contains a family of four genes, one of which is expressed preferentially in the lung. In this study, we compared the expression of the four bovine NK-lysin genes in healthy animals to animals challenged with pathogens known to be associated with bovine respiratory disease (BRD) using transcriptome sequencing (RNA-seq). The expression of several NK-lysins, especially NK2C, was elevated in challenged relative to control animals. The effects of synthetic peptides corresponding to functional region helices 2 and 3 of each gene product were tested on both model membranes and bio-membranes. Circular dichroism spectroscopy indicated that these peptides adopted a more helical secondary structure upon binding to an anionic model membrane and liposome leakage assays suggested that these peptides disrupt membranes. Bacterial killing assays further confirmed the antimicrobial effects of these peptides on BRD-associated bacteria, including both Pasteurella multocida and Mannhemia haemolytica and an ultrastructural examination of NK-lysin-treated P. multocida cells by transmission electron microscopy revealed the lysis of target membranes. These studies demonstrate that the expanded bovine NK-lysin gene family is potentially important in host defense against pathogens involved in bovine respiratory disease.
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Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon
2014-01-01
Background Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent ‘natural’ phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. Results Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. Conclusions The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. The ontology release files associated with the ontology merge described in this manuscript are available at: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ Current ontology release files are available always available at: http://purl.obolibrary.org/obo/uberon/releases/ PMID:25009735
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Cooperative Targets of Combined mTOR/HDAC Inhibition Promote MYC Degradation.
Simmons, John K; Michalowski, Aleksandra M; Gamache, Benjamin J; DuBois, Wendy; Patel, Jyoti; Zhang, Ke; Gary, Joy; Zhang, Shuling; Gaikwad, Snehal; Connors, Daniel; Watson, Nicholas; Leon, Elena; Chen, Jin-Qiu; Kuehl, W Michael; Lee, Maxwell P; Zingone, Adriana; Landgren, Ola; Ordentlich, Peter; Huang, Jing; Mock, Beverly A
2017-09-01
Cancer treatments often require combinations of molecularly targeted agents to be effective. mTORi (rapamycin) and HDACi (MS-275/entinostat) inhibitors have been shown to be effective in limiting tumor growth, and here we define part of the cooperative action of this drug combination. More than 60 human cancer cell lines responded synergistically (CI<1) when treated with this drug combination compared with single agents. In addition, a breast cancer patient-derived xenograft, and a BCL-XL plasmacytoma mouse model both showed enhanced responses to the combination compared with single agents. Mice bearing plasma cell tumors lived an average of 70 days longer on combination treatment compared with single agents. A set of 37 genes cooperatively affected (34 downregulated; 3 upregulated) by the combination responded pharmacodynamically in human myeloma cell lines, xenografts, and a P493 model, and were both enriched in tumors, and correlated with prognostic markers in myeloma patient datasets. Genes downregulated by the combination were overexpressed in several untreated cancers (breast, lung, colon, sarcoma, head and neck, myeloma) compared with normal tissues. The MYC/E2F axis, identified by upstream regulator analyses and validated by immunoblots, was significantly inhibited by the drug combination in several myeloma cell lines. Furthermore, 88% of the 34 genes downregulated have MYC-binding sites in their promoters, and the drug combination cooperatively reduced MYC half-life by 55% and increased degradation. Cells with MYC mutations were refractory to the combination. Thus, integrative approaches to understand drug synergy identified a clinically actionable strategy to inhibit MYC/E2F activity and tumor cell growth in vivo Mol Cancer Ther; 16(9); 2008-21. ©2017 AACR . ©2017 American Association for Cancer Research.
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An autonomous molecular computer for logical control of gene expression
Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud
2013-01-01
Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1–7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8–15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12–17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18–22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug. PMID:15116117
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Mata López, Sara; Hammond, James J; Rigsby, Madison B; Balog-Alvarez, Cynthia J; Kornegay, Joe N; Nghiem, Peter P
2018-05-29
Boys with Duchenne muscular dystrophy (DMD) have DMD gene mutations, with associated loss of the dystrophin protein and progressive muscle degeneration and weakness. Corticosteroids and palliative support are currently the best treatment options. The long-term benefits of recently approved compounds such as eteplirsen and ataluren remain to be seen. Dogs with naturally occurring dystrophinopathies show progressive disease akin to that of DMD. Accordingly, canine DMD models are useful for studies of pathogenesis and preclinical therapy development. A dystrophin-deficient, male border collie dog was evaluated at the age of 5 months for progressive muscle weakness and dysphagia. Dramatically increased serum creatine kinase levels (41,520 U/L; normal range 59-895 U/L) were seen on a biochemistry panel. Histopathologic changes characteristic of dystrophinopathy were seen. Dystrophin was absent in the skeletal muscle on immunofluorescence microscopy and western blot. Whole genome sequencing, polymerase chain reaction, and Sanger sequencing revealed a frameshift, single nucleotide deletion in canine DMD exon 20, position 27,626,466 (c.2841delT mRNA), resulting in a stop codon six nucleotides downstream. Semen was archived for future line perpetuation. This spontaneous canine dystrophinopathy occurred due to a novel mutation in the minor DMD mutation hotspot (between exons 2 through 20). Perpetuating this line could allow for preclinical testing of genetic therapies targeted to this area of the DMD gene.