Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

PubMed Central

2013-01-01

Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize deregulated genes and group them into gene modules by simultaneously considering gene expression level changes and gene-gene co-regulations. When applied to both simulated and empirical data, nDGE outperforms the traditional DGE method. More specifically, when applied to smoker and non-smoker lung cancer sets, nDGE results illustrate the molecular differences between smoker and non-smoker lung cancer. PMID:24341432

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

PubMed Central

Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md. Nurul Haque

2015-01-01

Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression. PMID:26413858

Robust Principal Component Analysis Regularized by Truncated Nuclear Norm for Identifying Differentially Expressed Genes.

PubMed

Wang, Ya-Xuan; Gao, Ying-Lian; Liu, Jin-Xing; Kong, Xiang-Zhen; Li, Hai-Jun

2017-09-01

Identifying differentially expressed genes from the thousands of genes is a challenging task. Robust principal component analysis (RPCA) is an efficient method in the identification of differentially expressed genes. RPCA method uses nuclear norm to approximate the rank function. However, theoretical studies showed that the nuclear norm minimizes all singular values, so it may not be the best solution to approximate the rank function. The truncated nuclear norm is defined as the sum of some smaller singular values, which may achieve a better approximation of the rank function than nuclear norm. In this paper, a novel method is proposed by replacing nuclear norm of RPCA with the truncated nuclear norm, which is named robust principal component analysis regularized by truncated nuclear norm (TRPCA). The method decomposes the observation matrix of genomic data into a low-rank matrix and a sparse matrix. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Thus, the differentially expressed genes can be identified according to the sparse matrix. The experimental results on The Cancer Genome Atlas data illustrate that the TRPCA method outperforms other state-of-the-art methods in the identification of differentially expressed genes.

Id-1 gene and gene products as therapeutic targets for treatment of breast cancer and other types of carcinoma

DOEpatents

Desprez, Pierre-Yves; Campisi, Judith

2014-08-19

A method for treatment of breast cancer and other types of cancer. The method comprises targeting and modulating Id-1 gene expression, if any, for the Id-1 gene, or gene products in breast or other epithelial cancers in a patient by delivering products that modulate Id-1 gene expression. When expressed, Id-1 gene is a prognostic indicator that cancer cells are invasive and metastatic.

A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

PubMed Central

Zuckerman, Neta S.; Noam, Yair; Goldsmith, Andrea J.; Lee, Peter P.

2013-01-01

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures - these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets. PMID:23990767

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

PubMed

Reynier, Frédéric; Petit, Fabien; Paye, Malick; Turrel-Davin, Fanny; Imbert, Pierre-Emmanuel; Hot, Arnaud; Mougin, Bruno; Miossec, Pierre

2011-01-01

The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals. Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients. In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

An efficient method to identify differentially expressed genes in microarray experiments

PubMed Central

Qin, Huaizhen; Feng, Tao; Harding, Scott A.; Tsai, Chung-Jui; Zhang, Shuanglin

2013-01-01

Motivation Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. Results We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. Availability The C++ code to implement the proposed method is available upon request for academic use. PMID:18453554

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Fully moderated T-statistic for small sample size gene expression arrays.

PubMed

Yu, Lianbo; Gulati, Parul; Fernandez, Soledad; Pennell, Michael; Kirschner, Lawrence; Jarjoura, David

2011-09-15

Gene expression microarray experiments with few replications lead to great variability in estimates of gene variances. Several Bayesian methods have been developed to reduce this variability and to increase power. Thus far, moderated t methods assumed a constant coefficient of variation (CV) for the gene variances. We provide evidence against this assumption, and extend the method by allowing the CV to vary with gene expression. Our CV varying method, which we refer to as the fully moderated t-statistic, was compared to three other methods (ordinary t, and two moderated t predecessors). A simulation study and a familiar spike-in data set were used to assess the performance of the testing methods. The results showed that our CV varying method had higher power than the other three methods, identified a greater number of true positives in spike-in data, fit simulated data under varying assumptions very well, and in a real data set better identified higher expressing genes that were consistent with functional pathways associated with the experiments.

Direct Introduction of Genes into Rats and Expression of the Genes

NASA Astrophysics Data System (ADS)

Benvenisty, Nissim; Reshef, Lea

1986-12-01

A method of introducing actively expressed genes into intact mammals is described. DNA precipitated with calcium phosphate has been injected intraperitoneally into newborn rats. The injected genes have been taken up and expressed by the animal tissues. To examine the generality of the method we have injected newborn rats with the chloramphenicol acetyltransferase prokaryotic gene fused with various viral and cellular gene promoters and the gene for hepatitis B surface antigen, and we observed appearance of chloramphenicol acetyltransferase activity and hepatitis B surface antigen in liver and spleen. In addition, administration of genes coding for hormones (insulin or growth hormone) resulted in their expression.

[Selection of reference genes of Siraitia grosvenorii by real-time PCR].

PubMed

Tu, Dong-ping; Mo, Chang-ming; Ma, Xiao-jun; Zhao, Huan; Tang, Qi; Huang, Jie; Pan, Li-mei; Wei, Rong-chang

2015-01-01

Siraitia grosvenorii is a traditional Chinese medicine also as edible food. This study selected six candidate reference genes by real-time quantitative PCR, the expression stability of the candidate reference genes in the different samples was analyzed by using the software and methods of geNorm, NormFinder, BestKeeper, Delta CT method and RefFinder, reference genes for S. grosvenorii were selected for the first time. The results showed that 18SrRNA expressed most stable in all samples, was the best reference gene in the genetic analysis. The study has a guiding role for the analysis of gene expression using qRT-PCR methods, providing a suitable reference genes to ensure the results in the study on differential expressed gene in synthesis and biological pathways, also other genes of S. grosvenorii.

Gene expression inference with deep learning.

PubMed

Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-06-15

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.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.

Gene expression inference with deep learning

PubMed Central

Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-01-01

Motivation: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. Results: We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. Availability and implementation: D-GEX is available at https://github.com/uci-cbcl/D-GEX. Contact: xhx@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26873929

Methods for monitoring multiple gene expression

DOEpatents

Berka, Randy; Bachkirova, Elena; Rey, Michael

2013-10-01

The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

Reconstructing Cell Lineages from Single-Cell Gene Expression Data: A Pilot Study

DTIC Science & Technology

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

Methods for monitoring multiple gene expression

DOEpatents

Berka, Randy [Davis, CA; Bachkirova, Elena [Davis, CA; Rey, Michael [Davis, CA

2012-05-01

The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

Methods for monitoring multiple gene expression

DOEpatents

Berka, Randy [Davis, CA; Bachkirova, Elena [Davis, CA; Rey, Michael [Davis, CA

2008-06-01

The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

PubMed Central

Ren, Xianwen; Wang, Yong; Zhang, Xiang-Sun; Jin, Qi

2013-01-01

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles. PMID:23761440

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

A method to identify differential expression profiles of time-course gene data with Fourier transformation

PubMed Central

2013-01-01

Background Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics. PMID:24134721

Normal uniform mixture differential gene expression detection for cDNA microarrays

PubMed Central

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

Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

PubMed Central

2012-01-01

Background We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these “differentially expressed GO terms” and have named the algorithm “matrix-assisted identification method of differentially expressed GO terms” (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. Findings We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson’s correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. Conclusions MIMGO is a reliable method to identify differentially expressed GO terms comprehensively. PMID:23232071

A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis

PubMed Central

2013-01-01

Background Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods. PMID:24373308

GeneNetFinder2: Improved Inference of Dynamic Gene Regulatory Relations with Multiple Regulators.

PubMed

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.

TA-GC cloning: A new simple and versatile technique for the directional cloning of PCR products for recombinant protein expression.

PubMed

Niarchos, Athanasios; Siora, Anastasia; Konstantinou, Evangelia; Kalampoki, Vasiliki; Lagoumintzis, George; Poulas, Konstantinos

2017-01-01

During the last few decades, the recombinant protein expression finds more and more applications. The cloning of protein-coding genes into expression vectors is required to be directional for proper expression, and versatile in order to facilitate gene insertion in multiple different vectors for expression tests. In this study, the TA-GC cloning method is proposed, as a new, simple and efficient method for the directional cloning of protein-coding genes in expression vectors. The presented method features several advantages over existing methods, which tend to be relatively more labour intensive, inflexible or expensive. The proposed method relies on the complementarity between single A- and G-overhangs of the protein-coding gene, obtained after a short incubation with T4 DNA polymerase, and T and C overhangs of the novel vector pET-BccI, created after digestion with the restriction endonuclease BccI. The novel protein-expression vector pET-BccI also facilitates the screening of transformed colonies for recombinant transformants. Evaluation experiments of the proposed TA-GC cloning method showed that 81% of the transformed colonies contained recombinant pET-BccI plasmids, and 98% of the recombinant colonies expressed the desired protein. This demonstrates that TA-GC cloning could be a valuable method for cloning protein-coding genes in expression vectors.

TA-GC cloning: A new simple and versatile technique for the directional cloning of PCR products for recombinant protein expression

PubMed Central

Niarchos, Athanasios; Siora, Anastasia; Konstantinou, Evangelia; Kalampoki, Vasiliki; Poulas, Konstantinos

2017-01-01

During the last few decades, the recombinant protein expression finds more and more applications. The cloning of protein-coding genes into expression vectors is required to be directional for proper expression, and versatile in order to facilitate gene insertion in multiple different vectors for expression tests. In this study, the TA-GC cloning method is proposed, as a new, simple and efficient method for the directional cloning of protein-coding genes in expression vectors. The presented method features several advantages over existing methods, which tend to be relatively more labour intensive, inflexible or expensive. The proposed method relies on the complementarity between single A- and G-overhangs of the protein-coding gene, obtained after a short incubation with T4 DNA polymerase, and T and C overhangs of the novel vector pET-BccI, created after digestion with the restriction endonuclease BccI. The novel protein-expression vector pET-BccI also facilitates the screening of transformed colonies for recombinant transformants. Evaluation experiments of the proposed TA-GC cloning method showed that 81% of the transformed colonies contained recombinant pET-BccI plasmids, and 98% of the recombinant colonies expressed the desired protein. This demonstrates that TA-GC cloning could be a valuable method for cloning protein-coding genes in expression vectors. PMID:29091919

Identification and validation of suitable reference genes for RT-qPCR analysis in mouse testis development.

PubMed

Gong, Zu-Kang; Wang, Shuang-Jie; Huang, Yong-Qi; Zhao, Rui-Qiang; Zhu, Qi-Fang; Lin, Wen-Zhen

2014-12-01

RT-qPCR is a commonly used method for evaluating gene expression; however, its accuracy and reliability are dependent upon the choice of appropriate reference gene(s), and there is limited information available on suitable reference gene(s) that can be used in mouse testis at different stages. In this study, using the RT-qPCR method, we investigated the expression variations of six reference genes representing different functional classes (Actb, Gapdh, Ppia, Tbp, Rps29, Hprt1) in mice testis during embryonic and postnatal development. The expression stabilities of putative reference genes were evaluated using five algorithms: geNorm, NormFinder, Bestkeeper, the comparative delta C(t) method and integrated tool RefFinder. Analysis of the results showed that Ppia, Gapdh and Actb were identified as the most stable genes and the geometric mean of Ppia, Gapdh and Actb constitutes an appropriate normalization factor for gene expression studies. The mRNA expression of AT1 as a test gene of interest varied depending upon which of the reference gene(s) was used as an internal control(s). This study suggested that Ppia, Gapdh and Actb are suitable reference genes among the six genes used for RT-qPCR normalization and provide crucial information for transcriptional analyses in future studies of gene expression in the developing mouse testis.

Material and methods to increase plant growth and yield

DOEpatents

Kirst, Matias

2015-09-15

The present invention relates to materials and methods for modulating growth rates, yield, and/or resistance to drought conditions in plants. In one embodiment, a method of the invention comprises increasing expression of an hc1 gene (or a homolog thereof that provides for substantially the same activity), or increasing expression or activity of the protein encoded by an hc1 gene thereof, in a plant, wherein expression of the hc1 gene or expression or activity of the protein encoded by an hc1 gene results in increased growth rate, yield, and/or drought resistance in the plant.

Materials and methods to increase plant growth and yield

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirst, Matias

The present invention relates to materials and methods for modulating growth rates, yield, and/or resistance to drought conditions in plants. In one embodiment, a method of the invention comprises increasing expression of an hc1 gene (or a homolog thereof that provides for substantially the same activity), or increasing expression or activity of the protein encoded by an hc1 gene thereof, in a plant, wherein expression of the hc1 gene or expression or activity of the protein encoded by an hc1 gene results in increased growth rate, yield, and/or drought resistance in the plant.

Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.

PubMed

Sun, Xiaoxiao; Dalpiaz, David; Wu, Di; S Liu, Jun; Zhong, Wenxuan; Ma, Ping

2016-08-26

Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available methods treat gene expressions at different time points as replicates and test the significance of the mean expression difference between treatments or conditions irrespective of time. They thus fail to identify many DE genes with different profiles across time. In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes in time course RNA-Seq data. In the NBMM, mean gene expression is characterized by a fixed effect, and time dependency is described by random effects. The NBMM is very flexible and can be fitted to both unreplicated and replicated time course RNA-Seq data via a penalized likelihood method. By comparing gene expression profiles over time, we further classify the DE genes into two subtypes to enhance the understanding of expression dynamics. A significance test for detecting DE genes is derived using a Kullback-Leibler distance ratio. Additionally, a significance test for gene sets is developed using a gene set score. Simulation analysis shows that the NBMM outperforms currently available methods for detecting DE genes and gene sets. Moreover, our real data analysis of fruit fly developmental time course RNA-Seq data demonstrates the NBMM identifies biologically relevant genes which are well justified by gene ontology analysis. The proposed method is powerful and efficient to detect biologically relevant DE genes and gene sets in time course RNA-Seq data.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

PubMed Central

Hejblum, Boris P.; Skinner, Jason; Thiébaut, Rodolphe

2015-01-01

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. PMID:26111374

Gene selection for tumor classification using neighborhood rough sets and entropy measures.

PubMed

Chen, Yumin; Zhang, Zunjun; Zheng, Jianzhong; Ma, Ying; Xue, Yu

2017-03-01

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification. Copyright © 2017 Elsevier Inc. All rights reserved.

Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex.

PubMed

Pavlidis, Paul; Qin, Jie; Arango, Victoria; Mann, John J; Sibille, Etienne

2004-06-01

One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, "overrepresentation analysis" (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, "functional class scoring" (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined "threshold of significance."

Method of controlling gene expression

DOEpatents

Peters, Norman K.; Frost, John W.; Long, Sharon R.

1991-12-03

A method of controlling expression of a DNA segment under the control of a nod gene promoter which comprises administering to a host containing a nod gene promoter an amount sufficient to control expression of the DNA segment of a compound of the formula: ##STR1## in which each R is independently H or OH, is described.

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors. By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.

Implementation of plaid model biclustering method on microarray of carcinoma and adenoma tumor gene expression data

NASA Astrophysics Data System (ADS)

Ardaneswari, Gianinna; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

A Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane and the organ adenoma is a benign tumor of the gland-like cells or epithelial tissue. In the field of molecular biology, the development of microarray technology is used in the data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, thats clustering method which not only the objects to be clustered, but also the properties or condition of the object. This research proposed Plaid Model Biclustering as one of biclustering method. In this study, we discuss the implementation of Plaid Model Biclustering Method on microarray of Carcinoma and Adenoma tumor gene expression data. From the experimental results, we found three biclusters are formed by Carcinoma gene expression data and four biclusters are formed by Adenoma gene expression data.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

Applicability of a gene expression based prediction method to SD and Wistar rats: an example of CARCINOscreen®.

PubMed

Matsumoto, Hiroshi; Saito, Fumiyo; Takeyoshi, Masahiro

2015-12-01

Recently, the development of several gene expression-based prediction methods has been attempted in the fields of toxicology. CARCINOscreen® is a gene expression-based screening method to predict carcinogenicity of chemicals which target the liver with high accuracy. In this study, we investigated the applicability of the gene expression-based screening method to SD and Wistar rats by using CARCINOscreen®, originally developed with F344 rats, with two carcinogens, 2,4-diaminotoluen and thioacetamide, and two non-carcinogens, 2,6-diaminotoluen and sodium benzoate. After the 28-day repeated dose test was conducted with each chemical in SD and Wistar rats, microarray analysis was performed using total RNA extracted from each liver. Obtained gene expression data were applied to CARCINOscreen®. Predictive scores obtained by the CARCINOscreen® for known carcinogens were > 2 in all strains of rats, while non-carcinogens gave prediction scores below 0.5. These results suggested that the gene expression based screening method, CARCINOscreen®, can be applied to SD and Wistar rats, widely used strains in toxicological studies, by setting of an appropriate boundary line of prediction score to classify the chemicals into carcinogens and non-carcinogens.

Gene expression studies of reference genes for quantitative real-time PCR: an overview in insects.

PubMed

Shakeel, Muhammad; Rodriguez, Alicia; Tahir, Urfa Bin; Jin, Fengliang

2018-02-01

Whenever gene expression is being examined, it is essential that a normalization process is carried out to eliminate non-biological variations. The use of reference genes, such as glyceraldehyde-3-phosphate dehydrogenase, actin, and ribosomal protein genes, is the usual method of choice for normalizing gene expression. Although reference genes are used to normalize target gene expression, a major problem is that the stability of these genes differs among tissues, developmental stages, species, and responses to abiotic factors. Therefore, the use and validation of multiple reference genes are required. This review discusses the reasons that why RT-qPCR has become the preferred method for validating results of gene expression profiles, the use of specific and non-specific dyes and the importance of use of primers and probes for qPCR as well as to discuss several statistical algorithms developed to help the validation of potential reference genes. The conflicts arising in the use of classical reference genes in gene normalization and their replacement with novel references are also discussed by citing the high stability and low stability of classical and novel reference genes under various biotic and abiotic experimental conditions by employing various methods applied for the reference genes amplification.

Distributional fold change test – a statistical approach for detecting differential expression in microarray experiments

PubMed Central

2012-01-01

Background Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. Results A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best – on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. Conclusions The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set. PMID:23122055

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

Modelling gene expression profiles related to prostate tumor progression using binary states

PubMed Central

2013-01-01

Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. PMID:23721350

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed Central

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-01-01

Background Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Results Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. Conclusion In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects. PMID:12962547

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-09-08

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.

PubMed

Crombach, Anton; Cicin-Sain, Damjan; Wotton, Karl R; Jaeger, Johannes

2012-01-01

Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, "medium-throughput" pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

Evaluation of biolistic gene transfer methods in vivo using non-invasive bioluminescent imaging techniques.

PubMed

Xia, Jixiang; Martinez, Angela; Daniell, Henry; Ebert, Steven N

2011-06-02

Gene therapy continues to hold great potential for treating many different types of disease and dysfunction. Safe and efficient techniques for gene transfer and expression in vivo are needed to enable gene therapeutic strategies to be effective in patients. Currently, the most commonly used methods employ replication-defective viral vectors for gene transfer, while physical gene transfer methods such as biolistic-mediated ("gene-gun") delivery to target tissues have not been as extensively explored. In the present study, we evaluated the efficacy of biolistic gene transfer techniques in vivo using non-invasive bioluminescent imaging (BLI) methods. Plasmid DNA carrying the firefly luciferase (LUC) reporter gene under the control of the human Cytomegalovirus (CMV) promoter/enhancer was transfected into mouse skin and liver using biolistic methods. The plasmids were coupled to gold microspheres (1 μm diameter) using different DNA Loading Ratios (DLRs), and "shot" into target tissues using a helium-driven gene gun. The optimal DLR was found to be in the range of 4-10. Bioluminescence was measured using an In Vivo Imaging System (IVIS-50) at various time-points following transfer. Biolistic gene transfer to mouse skin produced peak reporter gene expression one day after transfer. Expression remained detectable through four days, but declined to undetectable levels by six days following gene transfer. Maximum depth of tissue penetration following biolistic transfer to abdominal skin was 200-300 μm. Similarly, biolistic gene transfer to mouse liver in vivo also produced peak early expression followed by a decline over time. In contrast to skin, however, liver expression of the reporter gene was relatively stable 4-8 days post-biolistic gene transfer, and remained detectable for nearly two weeks. The use of bioluminescence imaging techniques enabled efficient evaluation of reporter gene expression in vivo. Our results demonstrate that different tissues show different expression kinetics following gene transfer of the same reporter plasmid to different mouse tissues in vivo. We evaluated superficial (skin) and abdominal organ (liver) targets, and found that reporter gene expression peaked within the first two days post-transfer in each case, but declined most rapidly in the skin (3-4 days) compared to liver (10-14 days). This information is essential for designing effective gene therapy strategies in different target tissues.

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

PubMed

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.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

PubMed Central

2014-01-01

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods. PMID:24708878

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

PubMed Central

2011-01-01

Background We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm. Results We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Conclusions The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. PMID:21699737

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

PubMed

Liu, Chenglin; Cui, Peng; Huang, Tao

2017-01-01

The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Validation of miRNA genes suitable as reference genes in qPCR analyses of miRNA gene expression in Atlantic salmon (Salmo salar).

PubMed

Johansen, Ilona; Andreassen, Rune

2014-12-23

MicroRNAs (miRNAs) are an abundant class of endogenous small RNA molecules that downregulate gene expression at the post-transcriptional level. They play important roles by regulating genes that control multiple biological processes, and recent years there has been an increased interest in studying miRNA genes and miRNA gene expression. The most common method applied to study gene expression of single genes is quantitative PCR (qPCR). However, before expression of mature miRNAs can be studied robust qPCR methods (miRNA-qPCR) must be developed. This includes identification and validation of suitable reference genes. We are particularly interested in Atlantic salmon (Salmo salar). This is an economically important aquaculture species, but no reference genes dedicated for use in miRNA-qPCR methods has been validated for this species. Our aim was, therefore, to identify suitable reference genes for miRNA-qPCR methods in Salmo salar. We used a systematic approach where we utilized similar studies in other species, some biological criteria, results from deep sequencing of small RNAs and, finally, experimental validation of candidate reference genes by qPCR to identify the most suitable reference genes. Ssa-miR-25-3p was identified as most suitable single reference gene. The best combinations of two reference genes were ssa-miR-25-3p and ssa-miR-455-5p. These two genes were constitutively and stably expressed across many different tissues. Furthermore, infectious salmon anaemia did not seem to affect their expression levels. These genes were amplified with high specificity, good efficiency and the qPCR assays showed a good linearity when applying a simple cybergreen miRNA-PCR method using miRNA gene specific forward primers. We have identified suitable reference genes for miRNA-qPCR in Atlantic salmon. These results will greatly facilitate further studies on miRNA genes in this species. The reference genes identified are conserved genes that are identical in their mature sequence in many aquaculture species. Therefore, they may also be suitable as reference genes in other teleosts. Finally, the systematic approach used in our study successfully identified suitable reference genes, suggesting that this may be a useful strategy to apply in similar validation studies in other aquaculture species.

Identifying potential maternal genes of Bombyx mori using digital gene expression profiling

PubMed Central

Xu, Pingzhen

2018-01-01

Maternal genes present in mature oocytes play a crucial role in the early development of silkworm. Although maternal genes have been widely studied in many other species, there has been limited research in Bombyx mori. High-throughput next generation sequencing provides a practical method for gene discovery on a genome-wide level. Herein, a transcriptome study was used to identify maternal-related genes from silkworm eggs. Unfertilized eggs from five different stages of early development were used to detect the changing situation of gene expression. The expressed genes showed different patterns over time. Seventy-six maternal genes were annotated according to homology analysis with Drosophila melanogaster. More than half of the differentially expressed maternal genes fell into four expression patterns, while the expression patterns showed a downward trend over time. The functional annotation of these material genes was mainly related to transcription factor activity, growth factor activity, nucleic acid binding, RNA binding, ATP binding, and ion binding. Additionally, twenty-two gene clusters including maternal genes were identified from 18 scaffolds. Altogether, we plotted a profile for the maternal genes of Bombyx mori using a digital gene expression profiling method. This will provide the basis for maternal-specific signature research and improve the understanding of the early development of silkworm. PMID:29462160

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients.

PubMed

Rapin, Nicolas; Bagger, Frederik Otzen; Jendholm, Johan; Mora-Jensen, Helena; Krogh, Anders; Kohlmann, Alexander; Thiede, Christian; Borregaard, Niels; Bullinger, Lars; Winther, Ole; Theilgaard-Mönch, Kim; Porse, Bo T

2014-02-06

Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.

oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

PubMed Central

Ho Sui, Shannan J.; Mortimer, James R.; Arenillas, David J.; Brumm, Jochen; Walsh, Christopher J.; Kennedy, Brian P.; Wasserman, Wyeth W.

2005-01-01

Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes. PMID:15933209

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma.

PubMed

Hopp, Lydia; Löffler-Wirth, Henry; Galle, Jörg; Binder, Hans

2018-06-11

We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer. The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'. We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states. The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

PubMed

Tran, Tuan-Anh; Vo, Nam Tri; Nguyen, Hoang Duc; Pham, Bao The

2015-12-01

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Enhancing biological relevance of a weighted gene co-expression network for functional module identification.

PubMed

Prom-On, Santitham; Chanthaphan, Atthawut; Chan, Jonathan Hoyin; Meechai, Asawin

2011-02-01

Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

PubMed Central

2013-01-01

Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO. PMID:23617960

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.

PubMed

Huynh-Thu, Vân Anh; Geurts, Pierre

2018-02-21

The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated extensively on the artificial DREAM4 benchmarks and on three real time series expression datasets. Although dynGENIE3 does not systematically yield the best performance on each and every network, it is competitive with diverse methods from the literature, while preserving the main advantages of GENIE3 in terms of scalability.

Gene expression variability in human hepatic drug metabolizing enzymes and transporters.

PubMed

Yang, Lun; Price, Elvin T; Chang, Ching-Wei; Li, Yan; Huang, Ying; Guo, Li-Wu; Guo, Yongli; Kaput, Jim; Shi, Leming; Ning, Baitang

2013-01-01

Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs) in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers.

PubMed

Moon, Myungjin; Nakai, Kenta

2018-04-01

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box-Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease.

PubMed

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K

2016-11-30

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

PubMed Central

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K.

2016-01-01

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature. PMID:27901073

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

PubMed

Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

2013-09-22

High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

Identification of the transcriptional regulators by expression profiling infected with hepatitis B virus.

PubMed

Chai, Xiaoqiang; Han, Yanan; Yang, Jian; Zhao, Xianxian; Liu, Yewang; Hou, Xugang; Tang, Yiheng; Zhao, Shirong; Li, Xiao

2016-02-01

The molecular pathogenesis of infection by hepatitis B virus with human is extremely complex and heterogeneous. To date the molecular information is not clearly defined despite intensive research efforts. Thus, studies aimed at transcription and regulation during virus infection or combined researches of those already known to be beneficial are needed. With the purpose of identifying the transcriptional regulators related to infection of hepatitis B virus in gene level, the gene expression profiles from some normal individuals and hepatitis B patients were analyzed in our study. In this work, the differential expressed genes were selected primarily. The several genes among those were validated in an independent set by qRT-PCR. Then the differentially co-expression analysis was conducted to identify differentially co-expressed links and differential co-expressed genes. Next, the analysis of the regulatory impact factors was performed through mapping the links and regulatory data. In order to give a further insight to these regulators, the co-expression gene modules were identified using a threshold-based hierarchical clustering method. Incidentally, the construction of the regulatory network was generated using the computer software. A total of 137,284 differentially co-expressed links and 780 differential co-expressed genes were identified. These co-expressed genes were significantly enriched inflammatory response. The results of regulatory impact factors revealed several crucial regulators related to hepatocellular carcinoma and other high-rank regulators. Meanwhile, more than one hundred co-expression gene modules were identified using clustering method. In our study, some important transcriptional regulators were identified using a computational method, which may enhance the understanding of disease mechanisms and lead to an improved treatment of hepatitis B. However, further experimental studies are required to confirm these findings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang

2015-01-01

RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.

A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets.

PubMed

Liu, Li-Zhi; Wu, Fang-Xiang; Zhang, Wen-Jun

2014-01-01

As an abstract mapping of the gene regulations in the cell, gene regulatory network is important to both biological research study and practical applications. The reverse engineering of gene regulatory networks from microarray gene expression data is a challenging research problem in systems biology. With the development of biological technologies, multiple time-course gene expression datasets might be collected for a specific gene network under different circumstances. The inference of a gene regulatory network can be improved by integrating these multiple datasets. It is also known that gene expression data may be contaminated with large errors or outliers, which may affect the inference results. A novel method, Huber group LASSO, is proposed to infer the same underlying network topology from multiple time-course gene expression datasets as well as to take the robustness to large error or outliers into account. To solve the optimization problem involved in the proposed method, an efficient algorithm which combines the ideas of auxiliary function minimization and block descent is developed. A stability selection method is adapted to our method to find a network topology consisting of edges with scores. The proposed method is applied to both simulation datasets and real experimental datasets. It shows that Huber group LASSO outperforms the group LASSO in terms of both areas under receiver operating characteristic curves and areas under the precision-recall curves. The convergence analysis of the algorithm theoretically shows that the sequence generated from the algorithm converges to the optimal solution of the problem. The simulation and real data examples demonstrate the effectiveness of the Huber group LASSO in integrating multiple time-course gene expression datasets and improving the resistance to large errors or outliers.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

A Novel mRNA Level Subtraction Method for Quick Identification of Target-Orientated Uniquely Expressed Genes Between Peanut Immature Pod and Leaf

PubMed Central

2010-01-01

Subtraction technique has been broadly applied for target gene discovery. However, most current protocols apply relative differential subtraction and result in great amount clone mixtures of unique and differentially expressed genes. This makes it more difficult to identify unique or target-orientated expressed genes. In this study, we developed a novel method for subtraction at mRNA level by integrating magnetic particle technology into driver preparation and tester–driver hybridization to facilitate uniquely expressed gene discovery between peanut immature pod and leaf through a single round subtraction. The resulting target clones were further validated through polymerase chain reaction screening using peanut immature pod and leaf cDNA libraries as templates. This study has resulted in identifying several genes expressed uniquely in immature peanut pod. These target genes can be used for future peanut functional genome and genetic engineering research. PMID:21406066

Transient, Inducible, Placenta-Specific Gene Expression in Mice

PubMed Central

Fan, Xiujun; Petitt, Matthew; Gamboa, Matthew; Huang, Mei; Dhal, Sabita; Druzin, Maurice L.; Wu, Joseph C.

2012-01-01

Molecular understanding of placental functions and pregnancy disorders is limited by the absence of methods for placenta-specific gene manipulation. Although persistent placenta-specific gene expression has been achieved by lentivirus-based gene delivery methods, developmentally and physiologically important placental genes have highly stage-specific functions, requiring controllable, transient expression systems for functional analysis. Here, we describe an inducible, placenta-specific gene expression system that enables high-level, transient transgene expression and monitoring of gene expression by live bioluminescence imaging in mouse placenta at different stages of pregnancy. We used the third generation tetracycline-responsive tranactivator protein Tet-On 3G, with 10- to 100-fold increased sensitivity to doxycycline (Dox) compared with previous versions, enabling unusually sensitive on-off control of gene expression in vivo. Transgenic mice expressing Tet-On 3G were created using a new integrase-based, site-specific approach, yielding high-level transgene expression driven by a ubiquitous promoter. Blastocysts from these mice were transduced with the Tet-On 3G-response element promoter-driving firefly luciferase using lentivirus-mediated placenta-specific gene delivery and transferred into wild-type pseudopregnant recipients for placenta-specific, Dox-inducible gene expression. Systemic Dox administration at various time points during pregnancy led to transient, placenta-specific firefly luciferase expression as early as d 5 of pregnancy in a Dox dose-dependent manner. This system enables, for the first time, reliable pregnancy stage-specific induction of gene expression in the placenta and live monitoring of gene expression during pregnancy. It will be widely applicable to studies of both placental development and pregnancy, and the site-specific Tet-On G3 mouse will be valuable for studies in a broad range of tissues. PMID:23011919

[Effect of methyl tertiary butyl ether on the expression of proto-oncogenes and function genes].

PubMed

Zhou, W; Huang, G; Zhang, H

1999-05-30

Methyl tertiary butyl ether (MTBE) is a new gasoline additive, which is used to increase the combustion of gasoline and to reduce the emission of harmful exhaust from automobile. The mechanism for the carcinogenesis of MTBE in animals is not clear. Immunohistochemistry method was used to detect the effect of MTBE on the expression of c-myc and p21 proteins in NIH3T3 cells. Dot hybridization method was used to explore the expression of c-myc gene and GST-P(glutathione S-transferase-P) gene in the of MTBE treated rats. The results showed that MTBE could enhance the expression of c-myc protein, but had no effect on p21 protein. MTBE could induce high expression of c-myc gene, and had no effect on the expression of GST-P gene. These results suggest that the high expression of c-myc gene induced by MTBE might be one of the mechanisms of its carcinogenicity in animal.

A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

PubMed

Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

2014-01-01

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

The Choice of the Filtering Method in Microarrays Affects the Inference Regarding Dosage Compensation of the Active X-Chromosome

PubMed Central

Zeller, Tanja; Wild, Philipp S.; Truong, Vinh; Trégouët, David-Alexandre; Munzel, Thomas; Ziegler, Andreas; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2011-01-01

Background The hypothesis of dosage compensation of genes of the X chromosome, supported by previous microarray studies, was recently challenged by RNA-sequencing data. It was suggested that microarray studies were biased toward an over-estimation of X-linked expression levels as a consequence of the filtering of genes below the detection threshold of microarrays. Methodology/Principal Findings To investigate this hypothesis, we used microarray expression data from circulating monocytes in 1,467 individuals. In total, 25,349 and 1,156 probes were unambiguously assigned to autosomes and the X chromosome, respectively. Globally, there was a clear shift of X-linked expressions toward lower levels than autosomes. We compared the ratio of expression levels of X-linked to autosomal transcripts (X∶AA) using two different filtering methods: 1. gene expressions were filtered out using a detection threshold irrespective of gene chromosomal location (the standard method in microarrays); 2. equal proportions of genes were filtered out separately on the X and on autosomes. For a wide range of filtering proportions, the X∶AA ratio estimated with the first method was not significantly different from 1, the value expected if dosage compensation was achieved, whereas it was significantly lower than 1 with the second method, leading to the rejection of the hypothesis of dosage compensation. We further showed in simulated data that the choice of the most appropriate method was dependent on biological assumptions regarding the proportion of actively expressed genes on the X chromosome comparative to the autosomes and the extent of dosage compensation. Conclusion/Significance This study shows that the method used for filtering out lowly expressed genes in microarrays may have a major impact according to the hypothesis investigated. The hypothesis of dosage compensation of X-linked genes cannot be firmly accepted or rejected using microarray-based data. PMID:21912656

Spectral biclustering of microarray data: coclustering genes and conditions.

PubMed

Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

2003-04-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study.

PubMed

Berghoff, Bork A; Karlsson, Torgny; Källman, Thomas; Wagner, E Gerhart H; Grabherr, Manfred G

2017-01-01

Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. Here, we present a novel method, moose 2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli , and show how moose 2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/. The proposed RNA-seq normalization method, moose 2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data are handled adequately to minimize variations between replicates.

A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

PubMed

Zhang, Qingyang

2018-05-16

Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.

A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression

PubMed Central

Salinas, Yasmmyn D.; Shi, YiJun; Greenwood, Michael; Hoe, See Ziau; Murphy, David; Gainer, Harold

2015-01-01

Magnocellular neurons (MCNs) in the hypothalamo-neurohypophysial system (HNS) are highly specialized to release large amounts of arginine vasopressin (Avp) or oxytocin (Oxt) into the blood stream and play critical roles in the regulation of body fluid homeostasis. The MCNs are osmosensory neurons and are excited by exposure to hypertonic solutions and inhibited by hypotonic solutions. The MCNs respond to systemic hypertonic and hypotonic stimulation with large changes in the expression of their Avp and Oxt genes, and microarray studies have shown that these osmotic perturbations also cause large changes in global gene expression in the HNS. In this paper, we examine gene expression in the rat supraoptic nucleus (SON) under normosmotic and chronic salt-loading SL) conditions by the first time using “new-generation”, RNA sequencing (RNA-Seq) methods. We reliably detect 9,709 genes as present in the SON by RNA-Seq, and 552 of these genes were changed in expression as a result of chronic SL. These genes reflect diverse functions, and 42 of these are involved in either transcriptional or translational processes. In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. These data provide the research community a comprehensive view of the transcriptome in the SON under normosmotic conditions and the changes in specific gene expression evoked by salt loading. PMID:25897513

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.

PubMed

Low, Joel Z B; Khang, Tsung Fei; Tammi, Martti T

2017-12-28

In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis. We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data. Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

Non-Viral Transfection Methods Optimized for Gene Delivery to a Lung Cancer Cell Line

PubMed Central

Salimzadeh, Loghman; Jaberipour, Mansooreh; Hosseini, Ahmad; Ghaderi, Abbas

2013-01-01

Background Mehr-80 is a newly established adherent human large cell lung cancer cell line that has not been transfected until now. This study aims to define the optimal transfection conditions and effects of some critical elements for enhancing gene delivery to this cell line by utilizing different non-viral transfection Procedures. Methods In the current study, calcium phosphate (CaP), DEAE-dextran, superfect, electroporation and lipofection transfection methods were used to optimize delivery of a plasmid construct that expressed Green Fluorescent Protein (GFP). Transgene expression was detected by fluorescent microscopy and flowcytometry. Toxicities of the methods were estimated by trypan blue staining. In order to evaluate the density of the transfected gene, we used a plasmid construct that expressed the Stromal cell-Derived Factor-1 (SDF-1) gene and measured its expression by real-time PCR. Results Mean levels of GFP-expressing cells 48 hr after transfection were 8.4% (CaP), 8.2% (DEAE-dextran), 4.9% (superfect), 34.1% (electroporation), and 40.1% (lipofection). Lipofection had the highest intense SDF-1 expression of the analyzed methods. Conclusion This study has shown that the lipofection and electroporation methods were more efficient at gene delivery to Mehr-80 cells. The quantity of DNA per transfection, reagent concentration, and incubation time were identified as essential factors for successful transfection in all of the studied methods. PMID:23799175

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

PubMed

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.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Aberrant Gene Expression in Humans

PubMed Central

Yang, Ence; Ji, Guoli; Brinkmeyer-Langford, Candice L.; Cai, James J.

2015-01-01

Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions. PMID:25617623

Multiplex titration RT-PCR: rapid determination of gene expression patterns for a large number of genes

NASA Technical Reports Server (NTRS)

Nebenfuhr, A.; Lomax, T. L.

1998-01-01

We have developed an improved method for determination of gene expression levels with RT-PCR. The procedure is rapid and does not require extensive optimization or densitometric analysis. Since the detection of individual transcripts is PCR-based, small amounts of tissue samples are sufficient for the analysis of expression patterns in large gene families. Using this method, we were able to rapidly screen nine members of the Aux/IAA family of auxin-responsive genes and identify those genes which vary in message abundance in a tissue- and light-specific manner. While not offering the accuracy of conventional semi-quantitative or competitive RT-PCR, our method allows quick screening of large numbers of genes in a wide range of RNA samples with just a thermal cycler and standard gel analysis equipment.

Getting the most out of RNA-seq data analysis.

PubMed

Khang, Tsung Fei; Lau, Ching Yee

2015-01-01

Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

PubMed

Wang, Shuaiqun; Aorigele; Kong, Wei; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

PubMed Central

Aorigele; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

Transcriptomic analysis reveals differential gene expression in response to aluminium in common bean (Phaseolus vulgaris) genotypes

PubMed Central

Eticha, Dejene; Zahn, Marc; Bremer, Melanie; Yang, Zhongbao; Rangel, Andrés F.; Rao, Idupulapati M.; Horst, Walter J.

2010-01-01

Background and Aims Aluminium (Al) resistance in common bean is known to be due to exudation of citrate from the root after a lag phase, indicating the induction of gene transcription and protein synthesis. The aims of this study were to identify Al-induced differentially expressed genes and to analyse the expression of candidate genes conferring Al resistance in bean. Methods The suppression subtractive hybridization (SSH) method was used to identify differentially expressed genes in an Al-resistant bean genotype (‘Quimbaya’) during the induction period. Using quantitative real-time PCR the expression patterns of selected genes were compared between an Al-resistant and an Al-sensitive genotype (‘VAX 1’) treated with Al for up to 24 h. Key Results Short-term Al treatment resulted in up-regulation of stress-induced genes and down-regulation of genes involved in metabolism. However, the expressions of genes encoding enzymes involved in citrate metabolism were not significantly affected by Al. Al treatment dramatically increased the expression of common bean expressed sequence tags belonging to the citrate transporter gene family MATE (multidrug and toxin extrusion family protein) in both the Al-resistant and -sensitive genotype in close agreement with Al-induced citrate exudation. Conclusions The expression of a citrate transporter MATE gene is crucial for citrate exudation in common bean. However, although the expression of the citrate transporter is a prerequisite for citrate exudation, genotypic Al resistance in common bean particularly depends on the capacity to sustain the synthesis of citrate for maintaining the cytosolic citrate pool that enables exudation. PMID:20237115

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Selection of suitable reference genes for gene expression studies in Staphylococcus capitis during growth under erythromycin stress.

PubMed

Cui, Bintao; Smooker, Peter M; Rouch, Duncan A; Deighton, Margaret A

2016-08-01

Accurate and reproducible measurement of gene transcription requires appropriate reference genes, which are stably expressed under different experimental conditions to provide normalization. Staphylococcus capitis is a human pathogen that produces biofilm under stress, such as imposed by antimicrobial agents. In this study, a set of five commonly used staphylococcal reference genes (gyrB, sodA, recA, tuf and rpoB) were systematically evaluated in two clinical isolates of Staphylococcus capitis (S. capitis subspecies urealyticus and capitis, respectively) under erythromycin stress in mid-log and stationary phases. Two public software programs (geNorm and NormFinder) and two manual calculation methods, reference residue normalization (RRN) and relative quantitative (RQ), were applied. The potential reference genes selected by the four algorithms were further validated by comparing the expression of a well-studied biofilm gene (icaA) with phenotypic biofilm formation in S. capitis under four different experimental conditions. The four methods differed considerably in their ability to predict the most suitable reference gene or gene combination for comparing icaA expression under different conditions. Under the conditions used here, the RQ method provided better selection of reference genes than the other three algorithms; however, this finding needs to be confirmed with a larger number of isolates. This study reinforces the need to assess the stability of reference genes for analysis of target gene expression under different conditions and the use of more than one algorithm in such studies. Although this work was conducted using a specific human pathogen, it emphasizes the importance of selecting suitable reference genes for accurate normalization of gene expression more generally.

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

PubMed Central

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R.; del Río-Navarro, Blanca E.; Mendoza-Vargas, Alfredo; Sánchez, Filiberto

2017-01-01

Background In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. Methods We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6–10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). Results From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Discussion Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments. PMID:29230367

Automated Discovery of Functional Generality of Human Gene Expression Programs

PubMed Central

Gerber, Georg K; Dowell, Robin D; Jaakkola, Tommi S; Gifford, David K

2007-01-01

An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-κB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal “cross-talk,” and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data. PMID:17696603

Optimatization of transient transformation methods to study gene expression in Musa acuminata (AAA group) cultivar Ambon Lumut

NASA Astrophysics Data System (ADS)

Prayuni, Kinasih; Dwivany, Fenny M.

2015-09-01

Banana is classified as a climateric fruit, whose ripening is regulated by ethylene. Ethylene is synthesized from ACC (1-aminocyclopropane-1-carboxylic acid) by ACC oxidase enzyme which is encoded by ACO gene. Controling an important gene expression in ethylene biosynthesis pathway has became a target to delay the ripening process. Therefore in the previous study we have designed a MaACO-RNAi construct to control MaACO gene expression. In this research, we study the effectiveness of different transient transformation methods to deliver the construct. Direct injection, with or no vaccum infiltration methods were used to deliver MaACO-RNAi construct. All of the methods succesfully deliver the construct into banana fruits based on RT-PCR result.

How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

PubMed

Veturi, Yogasudha; Ritchie, Marylyn D

2018-01-01

Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate.

PubMed

Liu, Xuejun; Shi, Xinxin; Chen, Chunlin; Zhang, Li

2015-10-16

The high-throughput sequencing technology, RNA-Seq, has been widely used to quantify gene and isoform expression in the study of transcriptome in recent years. Accurate expression measurement from the millions or billions of short generated reads is obstructed by difficulties. One is ambiguous mapping of reads to reference transcriptome caused by alternative splicing. This increases the uncertainty in estimating isoform expression. The other is non-uniformity of read distribution along the reference transcriptome due to positional, sequencing, mappability and other undiscovered sources of biases. This violates the uniform assumption of read distribution for many expression calculation approaches, such as the direct RPKM calculation and Poisson-based models. Many methods have been proposed to address these difficulties. Some approaches employ latent variable models to discover the underlying pattern of read sequencing. However, most of these methods make bias correction based on surrounding sequence contents and share the bias models by all genes. They therefore cannot estimate gene- and isoform-specific biases as revealed by recent studies. We propose a latent variable model, NLDMseq, to estimate gene and isoform expression. Our method adopts latent variables to model the unknown isoforms, from which reads originate, and the underlying percentage of multiple spliced variants. The isoform- and exon-specific read sequencing biases are modeled to account for the non-uniformity of read distribution, and are identified by utilizing the replicate information of multiple lanes of a single library run. We employ simulation and real data to verify the performance of our method in terms of accuracy in the calculation of gene and isoform expression. Results show that NLDMseq obtains competitive gene and isoform expression compared to popular alternatives. Finally, the proposed method is applied to the detection of differential expression (DE) to show its usefulness in the downstream analysis. The proposed NLDMseq method provides an approach to accurately estimate gene and isoform expression from RNA-Seq data by modeling the isoform- and exon-specific read sequencing biases. It makes use of a latent variable model to discover the hidden pattern of read sequencing. We have shown that it works well in both simulations and real datasets, and has competitive performance compared to popular methods. The method has been implemented as a freely available software which can be found at https://github.com/PUGEA/NLDMseq.

TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.

PubMed

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.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

A cis-regulatory logic simulator.

PubMed

Zeigler, Robert D; Gertz, Jason; Cohen, Barak A

2007-07-27

A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence. We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence. We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.

Functional Analyses of NSF1 in Wine Yeast Using Interconnected Correlation Clustering and Molecular Analyses

PubMed Central

Bessonov, Kyrylo; Walkey, Christopher J.; Shelp, Barry J.; van Vuuren, Hennie J. J.; Chiu, David; van der Merwe, George

2013-01-01

Analyzing time-course expression data captured in microarray datasets is a complex undertaking as the vast and complex data space is represented by a relatively low number of samples as compared to thousands of available genes. Here, we developed the Interdependent Correlation Clustering (ICC) method to analyze relationships that exist among genes conditioned on the expression of a specific target gene in microarray data. Based on Correlation Clustering, the ICC method analyzes a large set of correlation values related to gene expression profiles extracted from given microarray datasets. ICC can be applied to any microarray dataset and any target gene. We applied this method to microarray data generated from wine fermentations and selected NSF1, which encodes a C2H2 zinc finger-type transcription factor, as the target gene. The validity of the method was verified by accurate identifications of the previously known functional roles of NSF1. In addition, we identified and verified potential new functions for this gene; specifically, NSF1 is a negative regulator for the expression of sulfur metabolism genes, the nuclear localization of Nsf1 protein (Nsf1p) is controlled in a sulfur-dependent manner, and the transcription of NSF1 is regulated by Met4p, an important transcriptional activator of sulfur metabolism genes. The inter-disciplinary approach adopted here highlighted the accuracy and relevancy of the ICC method in mining for novel gene functions using complex microarray datasets with a limited number of samples. PMID:24130853

Supervised group Lasso with applications to microarray data analysis

PubMed Central

Ma, Shuangge; Song, Xiao; Huang, Jian

2007-01-01

Background A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. Results We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. Conclusion We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods. PMID:17316436

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

PubMed

Feltus, F Alex; Ficklin, Stephen P; Gibson, Scott M; Smith, Melissa C

2013-06-05

In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study

PubMed Central

2013-01-01

Background In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. Results A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Conclusions Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired. PMID:23738693

Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

PubMed Central

Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

2013-01-01

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

Digital gene expression for non-model organisms

PubMed Central

Hong, Lewis Z.; Li, Jun; Schmidt-Küntzel, Anne; Warren, Wesley C.; Barsh, Gregory S.

2011-01-01

Next-generation sequencing technologies offer new approaches for global measurements of gene expression but are mostly limited to organisms for which a high-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification of transcript abundance. We show that EDGE is capable of assaying for expression in >99% of genes in the genome and achieves saturation after 6–8 million reads. EDGE exhibits very little technical noise, reveals a large (106) dynamic range of gene expression, and is particularly suited for quantification of transcript abundance in non-model organisms where a high-quality annotated genome is not available. In a direct comparison with RNA-seq, both methods provide similar assessments of relative transcript abundance, but EDGE does better at detecting gene expression differences for poorly expressed genes and does not exhibit transcript length bias. Applying EDGE to laboratory mice, we show that a loss-of-function mutation in the melanocortin 1 receptor (Mc1r), recognized as a Mendelian determinant of yellow hair color in many different mammals, also causes reduced expression of genes involved in the interferon response. To illustrate the application of EDGE to a non-model organism, we examine skin biopsy samples from a cheetah (Acinonyx jubatus) and identify genes likely to control differences in the color of spotted versus non-spotted regions. PMID:21844123

In search of functional association from time-series microarray data based on the change trend and level of gene expression

PubMed Central

He, Feng; Zeng, An-Ping

2006-01-01

Background The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. Results In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. Conclusion The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data. PMID:16478547

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

PubMed Central

Song, Yufen; Yan, Zhaohui

2018-01-01

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR. PMID:29257233

Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

PubMed Central

CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

2014-01-01

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

A gene expression biomarker identifies in vitro and in vivo ERα modulators in a human gene expression compendium

EPA Science Inventory

We propose the use of gene expression profiling to complement the chemical characterization currently based on HTS assay data and present a case study relevant to the Endocrine Disruptor Screening Program. We have developed computational methods to identify estrogen receptor &alp...

Similarity of markers identified from cancer gene expression studies: observations from GEO.

PubMed

Shi, Xingjie; Shen, Shihao; Liu, Jin; Huang, Jian; Zhou, Yong; Ma, Shuangge

2014-09-01

Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

PubMed

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.

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

PubMed Central

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

Selection of Reference Genes for Expression Studies of Xenobiotic Adaptation in Tetranychus urticae.

PubMed

Morales, Mariany Ashanty; Mendoza, Bianca Marie; Lavine, Laura Corley; Lavine, Mark Daniel; Walsh, Douglas Bruce; Zhu, Fang

2016-01-01

Quantitative real-time PCR (qRT-PCR) is an extensively used, high-throughput method to analyze transcriptional expression of genes of interest. An appropriate normalization strategy with reliable reference genes is required for calculating gene expression across diverse experimental conditions. In this study, we aim to identify the most stable reference genes for expression studies of xenobiotic adaptation in Tetranychus urticae, an extremely polyphagous herbivore causing significant yield reduction of agriculture. We chose eight commonly used housekeeping genes as candidates. The qRT-PCR expression data for these genes were evaluated from seven populations: a susceptible and three acaricide resistant populations feeding on lima beans, and three other susceptible populations which had been shifted host from lima beans to three other plant species. The stability of the candidate reference genes was then assessed using four different algorithms (comparative ΔCt method, geNorm, NormFinder, and BestKeeper). Additionally, we used an online web-based tool (RefFinder) to assign an overall final rank for each candidate gene. Our study found that CycA and Rp49 are best for investigating gene expression in acaricide susceptible and resistant populations. GAPDH, Rp49, and Rpl18 are best for host plant shift studies. And GAPDH and Rp49 were the most stable reference genes when investigating gene expression under changes in both experimental conditions. These results will facilitate research in revealing molecular mechanisms underlying the xenobiotic adaptation of this notorious agricultural pest.

Selection of Reference Genes for Expression Studies of Xenobiotic Adaptation in Tetranychus urticae

PubMed Central

Morales, Mariany Ashanty; Mendoza, Bianca Marie; Lavine, Laura Corley; Lavine, Mark Daniel; Walsh, Douglas Bruce; Zhu, Fang

2016-01-01

Quantitative real-time PCR (qRT-PCR) is an extensively used, high-throughput method to analyze transcriptional expression of genes of interest. An appropriate normalization strategy with reliable reference genes is required for calculating gene expression across diverse experimental conditions. In this study, we aim to identify the most stable reference genes for expression studies of xenobiotic adaptation in Tetranychus urticae, an extremely polyphagous herbivore causing significant yield reduction of agriculture. We chose eight commonly used housekeeping genes as candidates. The qRT-PCR expression data for these genes were evaluated from seven populations: a susceptible and three acaricide resistant populations feeding on lima beans, and three other susceptible populations which had been shifted host from lima beans to three other plant species. The stability of the candidate reference genes was then assessed using four different algorithms (comparative ΔCt method, geNorm, NormFinder, and BestKeeper). Additionally, we used an online web-based tool (RefFinder) to assign an overall final rank for each candidate gene. Our study found that CycA and Rp49 are best for investigating gene expression in acaricide susceptible and resistant populations. GAPDH, Rp49, and Rpl18 are best for host plant shift studies. And GAPDH and Rp49 were the most stable reference genes when investigating gene expression under changes in both experimental conditions. These results will facilitate research in revealing molecular mechanisms underlying the xenobiotic adaptation of this notorious agricultural pest. PMID:27570487

Visualization of Notch signaling oscillation in cells and tissues.

PubMed

Shimojo, Hiromi; Harima, Yukiko; Kageyama, Ryoichiro

2014-01-01

The Notch signaling effectors Hes1 and Hes7 exhibit oscillatory expression with a period of about 2-3 h during embryogenesis. Hes1 oscillation is important for proliferation and differentiation of neural stem cells, whereas Hes7 oscillation regulates periodic formation of somites. Continuous expression of Hes1 and Hes7 inhibits these developmental processes. Thus, expression dynamics are very important for gene functions, but it is difficult to distinguish between oscillatory and persistent expression by conventional methods such as in situ hybridization and immunostaining. Here, we describe time-lapse imaging methods using destabilized luciferase reporters and a highly sensitive cooled charge-coupled device camera, which can monitor dynamic gene expression. Furthermore, the expression of two genes can be examined simultaneously by a dual reporter system using two-color luciferase reporters. Time-lapse imaging analyses reveal how dynamically gene expression changes in many biological events.

Gene silencing in Escherichia coli using antisense RNAs expressed from doxycycline-inducible vectors.

PubMed

Nakashima, N; Tamura, T

2013-06-01

Here, we report on the construction of doxycycline (tetracycline analogue)-inducible vectors that express antisense RNAs in Escherichia coli. Using these vectors, the expression of genes of interest can be silenced conditionally. The expression of antisense RNAs from the vectors was more tightly regulated than the previously constructed isopropyl-β-D-galactopyranoside-inducible vectors. Furthermore, expression levels of antisense RNAs were enhanced by combining the doxycycline-inducible promoter with the T7 promoter-T7 RNA polymerase system; the T7 RNA polymerase gene, under control of the doxycycline-inducible promoter, was integrated into the lacZ locus of the genome without leaving any antibiotic marker. These vectors are useful for investigating gene functions or altering cell phenotypes for biotechnological and industrial applications. A gene silencing method using antisense RNAs in Escherichia coli is described, which facilitates the investigation of bacterial gene function. In particular, the method is suitable for comprehensive analyses or phenotypic analyses of genes essential for growth. Here, we describe expansion of vector variations for expressing antisense RNAs, allowing choice of a vector appropriate for the target genes or experimental purpose. © 2013 The Society for Applied Microbiology.

Scoring clustering solutions by their biological relevance.

PubMed

Gat-Viks, I; Sharan, R; Shamir, R

2003-12-12

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. The software is available from the authors upon request.

Quantitation of apolipoprotein epsilon gene expression by competitive polymerase chain reaction in a patient with familial apolipoprotein E deficiency.

PubMed

Dobmeyer, J M; Rexin, M; Dobmeyer, T S; Klein, S A; Rossol, R; Feussner, G

1998-06-22

A simple method of obtaining semiquantitative and reliable data on apolipoprotein (apo) sigma gene expression is described. We detected apo sigma specific sequences by reverse transcription (rT)-PCR. For quantitative measurement, an apo sigma DNA standard was produced allowing the development of a competitive PCR-method. The efficiency of RNA extraction and cDNA synthesis was controlled by quantitation of a housekeeping gene (glyceraldehyde-3-phosphatedehydrogenase, G3PDH) in separate reactions. To imitate a defined induction of apo sigma gene expression, serial twofold dilutions of total RNA were reversely transcribed and the respective cDNAs used to perform a competitive apo sigma and G3PDH PCR. The change in apo sigma cDNA and G3PDH cDNA was 1.7-2.3-fold with an expected value of 2.0-fold. Standard deviations in three independently performed experiments were within a range of < 15% of the mean, indicating low intra-assay variation and high reproducibility. To illustrate this method, apo sigma gene expression was measured in a patient with complete lack of functional active apo E in comparison to healthy controls. The method presented here might be valuable in assessment of apo sigma gene expression in human disease.

Three-Dimensional Gene Map of Cancer Cell Types: Structural Entropy Minimisation Principle for Defining Tumour Subtypes

PubMed Central

Li, Angsheng; Yin, Xianchen; Pan, Yicheng

2016-01-01

In this study, we propose a method for constructing cell sample networks from gene expression profiles, and a structural entropy minimisation principle for detecting natural structure of networks and for identifying cancer cell subtypes. Our method establishes a three-dimensional gene map of cancer cell types and subtypes. The identified subtypes are defined by a unique gene expression pattern, and a three-dimensional gene map is established by defining the unique gene expression pattern for each identified subtype for cancers, including acute leukaemia, lymphoma, multi-tissue, lung cancer and healthy tissue. Our three-dimensional gene map demonstrates that a true tumour type may be divided into subtypes, each defined by a unique gene expression pattern. Clinical data analyses demonstrate that most cell samples of an identified subtype share similar survival times, survival indicators and International Prognostic Index (IPI) scores and indicate that distinct subtypes identified by our algorithms exhibit different overall survival times, survival ratios and IPI scores. Our three-dimensional gene map establishes a high-definition, one-to-one map between the biologically and medically meaningful tumour subtypes and the gene expression patterns, and identifies remarkable cells that form singleton submodules. PMID:26842724

In Situ Detection of MicroRNA Expression with RNAscope Probes.

PubMed

Yin, Viravuth P

2018-01-01

Elucidating the spatial resolution of gene transcripts provides important insight into potential gene function. MicroRNAs are short, singled-stranded noncoding RNAs that control gene expression through base-pair complementarity with target mRNAs in the 3' untranslated region (UTR) and inhibiting protein expression. However, given their small size of ~22- to 24-nt and low expression levels, standard in situ hybridization detection methods are not amendable for microRNA spatial resolution. Here, I describe a technique that employs RNAscope probe design and propriety amplification technology that provides simultaneous single molecule detection of individual microRNA and its target gene. This method allows for rapid and sensitive detection of noncoding RNA transcripts in frozen tissue sections.

A cross-species bi-clustering approach to identifying conserved co-regulated genes.

PubMed

Sun, Jiangwen; Jiang, Zongliang; Tian, Xiuchun; Bi, Jinbo

2016-06-15

A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes. We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos. The R package containing the implementation of the proposed method in C ++ is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/ jinbo@engr.uconn.edu. © The Author 2016. Published by Oxford University Press.

Rethinking cell-cycle-dependent gene expression in Schizosaccharomyces pombe.

PubMed

Cooper, Stephen

2017-11-01

Three studies of gene expression during the division cycle of Schizosaccharomyces pombe led to the proposal that a large number of genes are expressed at particular times during the S. pombe cell cycle. Yet only a small fraction of genes proposed to be expressed in a cell-cycle-dependent manner are reproducible in all three published studies. In addition to reproducibility problems, questions about expression amplitudes, cell-cycle timing of expression, synchronization artifacts, and the problem with methods for synchronizing cells must be considered. These problems and complications prompt the idea that caution should be used before accepting the conclusion that there are a large number of genes expressed in a cell-cycle-dependent manner in S. pombe.

Gene expression complex networks: synthesis, identification, and analysis.

PubMed

Lopes, Fabrício M; Cesar, Roberto M; Costa, Luciano Da F

2011-10-01

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdös-Rényi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabási-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree variation, decreasing its network recovery rate with the increase of . The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

ADGO: analysis of differentially expressed gene sets using composite GO annotation.

PubMed

Nam, Dougu; Kim, Sang-Bae; Kim, Seon-Kyu; Yang, Sungjin; Kim, Seon-Young; Chu, In-Sun

2006-09-15

Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. chu@kribb.re.kr http://array.kobic.re.kr/ADGO.

Establishment of a novel collagenase perfusion method to isolate rat pancreatic stellate cells and investigation of their gene expression of TGF-beta1, type I collagen, and CTGF in primary culture or freshly isolated cells.

PubMed

Shinji, Toshiyuki; Ujike, Kozo; Ochi, Koji; Kusano, Nobuchika; Kikui, Tetsuya; Matsumura, Naoki; Emori, Yasuyuki; Seno, Toshinobu; Koide, Norio

2002-08-01

In studies of the pathogenesis of pancreatic fibrosis, pancreatic stellate cells (PSCs) have recently gained attention. In the present study, we established a new collagenase perfusion method through thoracic aorta cannulation to isolate PSCs, and we studied gene expression of TGF-beta1, type I collagen, and connective tissue growth factor using primary cultured PSCs. Our method facilitated PSC isolation, and by our new method, 4.3 +/- 1.2 x 10(6) PSCs were obtained from a rat. In comparing the expression of these genes with that of hepatic stellate cells (HSCs), we observed a similar pattern, although PSCs expressed type I collagen gene earlier than did HSCs. These results suggest that PSCs may play an important role in fibrosis of the pancreas, as HSCs do in liver fibrosis; in addition, PSCs may exist in a preactivated state or may be more easily activated than are HSCs. We also isolated the PSCs from a WBN/Kob rat, the spontaneous pancreatitis rat, and compared the gene expression with that from a normal rat.

Gene regulatory network inference using fused LASSO on multiple data sets

PubMed Central

Omranian, Nooshin; Eloundou-Mbebi, Jeanne M. O.; Mueller-Roeber, Bernd; Nikoloski, Zoran

2016-01-01

Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions. PMID:26864687

Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles

PubMed Central

Huang, Shi-Ming; Zhao, Xia; Zhao, Xue-Mei; Wang, Xiao-Ying; Li, Shan-Shan; Zhu, Yu-Hui

2014-01-01

Objectives: Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent. Methods: MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone. Results: A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection. Conclusions: We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection. PMID:25664019

In Vivo Functional Genomic Studies of Sterol Carrier Protein-2 Gene in the Yellow Fever Mosquito

PubMed Central

Peng, Rong; Maklokova, Vilena I.; Chandrashekhar, Jayadevi H.; Lan, Que

2011-01-01

A simple and efficient DNA delivery method to introduce extrachromosomal DNA into mosquito embryos would significantly aid functional genomic studies. The conventional method for delivery of DNA into insects is to inject the DNA directly into the embryos. Taking advantage of the unique aspects of mosquito reproductive physiology during vitellogenesis and an in vivo transfection reagent that mediates DNA uptake in cells via endocytosis, we have developed a new method to introduce DNA into mosquito embryos vertically via microinjection of DNA vectors in vitellogenic females without directly manipulating the embryos. Our method was able to introduce inducible gene expression vectors transiently into F0 mosquitoes to perform functional studies in vivo without transgenic lines. The high efficiency of expression knockdown was reproducible with more than 70% of the F0 individuals showed sufficient gene expression suppression (<30% of the controls' levels). At the cohort level, AeSCP-2 expression knockdown in early instar larvae resulted in detectable phenotypes of the expression deficiency such as high mortality, lowered fertility, and distorted sex ratio after induction of AeSCP-2 siRNA expression in vivo. The results further confirmed the important role of AeSCP-2 in the development and reproduction of A. aegypti. In this study, we proved that extrachromosaomal transient expression of an inducible gene from a DNA vector vertically delivered via vitellogenic females can be used to manipulate gene expression in F0 generation. This new method will be a simple and efficient tool for in vivo functional genomic studies in mosquitoes. PMID:21437205

Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain

PubMed Central

Montoya, Julio Cesar; Fajardo, Dianora; Peña, Angela; Sánchez, Adalberto; Domínguez, Martha C; Satizábal, José María

2014-01-01

Background: The information of gene expression obtained from databases, have made possible the extraction and analysis of data related with several molecular processes involving not only in brain homeostasis but its disruption in some neuropathologies; principally in Down syndrome and the Alzheimer disease. Objective: To correlate the levels of transcription of 19 genes located in the Down Syndrome Critical Region (DSCR) with their expression in several substructures of normal human brain. Methods: There were obtained expression profiles of 19 DSCR genes in 42 brain substructures, from gene expression values available at the database of the human brain of the Brain Atlas of the Allen Institute for Brain Sciences", (http://human.brain-map.org/). The co-expression patterns of DSCR genes in brain were calculated by using multivariate statistical methods. Results: Highest levels of gene expression were registered at caudate nucleus, nucleus accumbens and putamen among central areas of cerebral cortex. Increased expression levels of RCAN1 that encode by a protein involved in signal transduction process of the CNS were recorded for PCP4 that participates in the binding to calmodulin and TTC3; a protein that is associated with differentiation of neurons. That previously identified brain structures play a crucial role in the learning process, in different class of memory and in motor skills. Conclusion: The precise regulation of DSCR gene expression is crucial to maintain the brain homeostasis, especially in those areas with high levels of gene expression associated with a remarkable process of learning and cognition. PMID:25767303

ASSESSMENT OF DIFFERENTIAL GENE EXPRESSION IN SHEEPSHEAD MINNOWS EXPOSED TO ENVIRONMENTAL ESTROGENS

EPA Science Inventory

Gene arrays and quantitative real-time PCR (Q-PRC) are sensitive methods for assessing exposure of fish and other wildlife to environmental contaminants by measuring changes in gene expression. Several genes normally induced by estradiol in female fish, those for vitellogenins (...

Evaluation of RNA from human trabecular bone and identification of stable reference genes.

PubMed

Cepollaro, Simona; Della Bella, Elena; de Biase, Dario; Visani, Michela; Fini, Milena

2018-06-01

The isolation of good quality RNA from tissues is an essential prerequisite for gene expression analysis to study pathophysiological processes. This study evaluated the RNA isolated from human trabecular bone and defined a set of stable reference genes. After pulverization, RNA was extracted with a phenol/chloroform method and then purified using silica columns. The A260/280 ratio, A260/230 ratio, RIN, and ribosomal ratio were measured to evaluate RNA quality and integrity. Moreover, the expression of six candidates was analyzed by qPCR and different algorithms were applied to assess reference gene stability. A good purity and quality of RNA was achieved according to A260/280 and A260/230 ratios, and RIN values. TBP, YWHAZ, and PGK1 were the most stable reference genes that should be used for gene expression analysis. In summary, the method proposed is suitable for gene expression evaluation in human bone and a set of reliable reference genes has been identified. © 2017 Wiley Periodicals, Inc.

Estimation of gene induction enables a relevance-based ranking of gene sets.

PubMed

Bartholomé, Kilian; Kreutz, Clemens; Timmer, Jens

2009-07-01

In order to handle and interpret the vast amounts of data produced by microarray experiments, the analysis of sets of genes with a common biological functionality has been shown to be advantageous compared to single gene analyses. Some statistical methods have been proposed to analyse the differential gene expression of gene sets in microarray experiments. However, most of these methods either require threshhold values to be chosen for the analysis, or they need some reference set for the determination of significance. We present a method that estimates the number of differentially expressed genes in a gene set without requiring a threshold value for significance of genes. The method is self-contained (i.e., it does not require a reference set for comparison). In contrast to other methods which are focused on significance, our approach emphasizes the relevance of the regulation of gene sets. The presented method measures the degree of regulation of a gene set and is a useful tool to compare the induction of different gene sets and place the results of microarray experiments into the biological context. An R-package is available.

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data

PubMed Central

Kadarmideen, Haja N; Watson-haigh, Nathan S

2012-01-01

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended. PMID:23144540

Visualization and analysis for multidimensional gene expressions signature of cigarette smoking

NASA Astrophysics Data System (ADS)

Wang, Changbo; Xiao, Zhao; Zhang, Tianlun; Cui, Jin; Pang, Chenming

2011-11-01

Biologists often use gene chip to get massive experimental data in the field of bioscience and chemical sciences. Facing a large amount of experimental data, researchers often need to find out a few interesting data or simple regulations. This paper presents a set of methods to visualize and analyze the data for gene expression signatures of people who smoke. We use the latest research data from National Center for Biotechnology Information. Totally, there are more than 400 thousand expressions data. Using these data, we can use parallel coordinates method to visualize the different gene expressions between smokers and nonsmokers and we can distinguish non-smokers, former smokers and current smokers by using the different colors. It can be easy to find out which gene is more important during the lung cancer angiogenesis in the smoking people. In another way, we can use a hierarchical model to visualize the inner relation of different genes. The location of the nodes shows different expression moment and the distance to the root shows the sequence of the expression. We can use the ring layout to represent all the nodes, and connect the different nodes which are related with color lines. Combined with the parallel coordinates method, the visualization result show the important genes and some inner relation obviously, which is useful for examination and prevention of lung cancer.

Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions

PubMed Central

Kluger, Yuval; Basri, Ronen; Chang, Joseph T.; Gerstein, Mark

2003-01-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find “marker genes” that are differentially expressed in particular sets of “conditions.” We have developed a method that simultaneously clusters genes and conditions, finding distinctive “checkerboard” patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data). PMID:12671006

Digital detection of multiple minority mutants and expression levels of multiple colorectal cancer-related genes using digital-PCR coupled with bead-array.

PubMed

Huang, Huan; Li, Shuo; Sun, Lizhou; Zhou, Guohua

2015-01-01

To simultaneously analyze mutations and expression levels of multiple genes on one detection platform, we proposed a method termed "multiplex ligation-dependent probe amplification-digital amplification coupled with hydrogel bead-array" (MLPA-DABA) and applied it to diagnose colorectal cancer (CRC). CRC cells and tissues were sampled to extract nucleic acid, perform MLPA with sequence-tagged probes, perform digital emulsion polymerase chain reaction (PCR), and produce a hydrogel bead-array to immobilize beads and form a single bead layer on the array. After hybridization with fluorescent probes, the number of colored beads, which reflects the abundance of expressed genes and the mutation rate, was counted for diagnosis. Only red or green beads occurred on the chips in the mixed samples, indicating the success of single-molecule PCR. When a one-source sample was analyzed using mixed MLPA probes, beads of only one color occurred, suggesting the high specificity of the method in analyzing CRC mutation and gene expression. In gene expression analysis of a CRC tissue from one CRC patient, the mutant percentage was 3.1%, and the expression levels of CRC-related genes were much higher than those of normal tissue. The highly sensitive MLPA-DABA succeeds in the relative quantification of mutations and gene expressions of exfoliated cells in stool samples of CRC patients on the same chip platform. MLPA-DABA coupled with hydrogel bead-array is a promising method in the non-invasive diagnosis of CRC.

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.

PubMed

Doungpan, Narumol; Engchuan, Worrawat; Chan, Jonathan H; Meechai, Asawin

2016-12-05

Gene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results. The two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three subnetwork identification algorithms. The two searching algorithms resulted in better classification and gene/gene-set agreement compared to the original greedy search of the GNFS method. The identified lung cancer subnetwork using the proposed searching algorithm resulted in an improvement of the cross-dataset validation and an increase in the consistency of findings between two independent datasets. The homogeneity measurement of the datasets was conducted to assess dataset compatibility in cross-dataset validation. The lung cancer dataset with higher homogeneity showed a better result when using the GS search while the dataset with low homogeneity showed a better result when using the PN search. The 10-fold cross-dataset validation on the independent lung cancer datasets showed higher classification performance of the proposed algorithms when compared with the greedy search in the original GNFS method. The proposed searching algorithms provide a higher number of genes in the subnetwork expansion step than the greedy algorithm. As a result, the performance of the subnetworks identified from the GSNFS method was improved in terms of classification performance and gene/gene-set level agreement depending on the homogeneity of the datasets used in the analysis. Some common genes obtained from the four datasets using different searching algorithms are genes known to play a role in lung cancer. The improvement of classification performance and the gene/gene-set level agreement, and the biological relevance indicated the effectiveness of the GSNFS method for gene subnetwork identification using expression data.

Selection of reference genes for quantitative real-time PCR normalization in Panax ginseng at different stages of growth and in different organs.

PubMed

Liu, Jing; Wang, Qun; Sun, Minying; Zhu, Linlin; Yang, Michael; Zhao, Yu

2014-01-01

Quantitative real-time reverse transcription PCR (qRT-PCR) has become a widely used method for gene expression analysis; however, its data interpretation largely depends on the stability of reference genes. The transcriptomics of Panax ginseng, one of the most popular and traditional ingredients used in Chinese medicines, is increasingly being studied. Furthermore, it is vital to establish a series of reliable reference genes when qRT-PCR is used to assess the gene expression profile of ginseng. In this study, we screened out candidate reference genes for ginseng using gene expression data generated by a high-throughput sequencing platform. Based on the statistical tests, 20 reference genes (10 traditional housekeeping genes and 10 novel genes) were selected. These genes were tested for the normalization of expression levels in five growth stages and three distinct plant organs of ginseng by qPCR. These genes were subsequently ranked and compared according to the stability of their expressions using geNorm, NormFinder, and BestKeeper computational programs. Although the best reference genes were found to vary across different samples, CYP and EF-1α were the most stable genes amongst all samples. GAPDH/30S RPS20, CYP/60S RPL13 and CYP/QCR were the optimum pair of reference genes in the roots, stems, and leaves. CYP/60S RPL13, CYP/eIF-5A, aTUB/V-ATP, eIF-5A/SAR1, and aTUB/pol IIa were the most stably expressed combinations in each of the five developmental stages. Our study serves as a foundation for developing an accurate method of qRT-PCR and will benefit future studies on gene expression profiles of Panax Ginseng.

Optimal Scaling of Digital Transcriptomes

PubMed Central

Glusman, Gustavo; Caballero, Juan; Robinson, Max; Kutlu, Burak; Hood, Leroy

2013-01-01

Deep sequencing of transcriptomes has become an indispensable tool for biology, enabling expression levels for thousands of genes to be compared across multiple samples. Since transcript counts scale with sequencing depth, counts from different samples must be normalized to a common scale prior to comparison. We analyzed fifteen existing and novel algorithms for normalizing transcript counts, and evaluated the effectiveness of the resulting normalizations. For this purpose we defined two novel and mutually independent metrics: (1) the number of “uniform” genes (genes whose normalized expression levels have a sufficiently low coefficient of variation), and (2) low Spearman correlation between normalized expression profiles of gene pairs. We also define four novel algorithms, one of which explicitly maximizes the number of uniform genes, and compared the performance of all fifteen algorithms. The two most commonly used methods (scaling to a fixed total value, or equalizing the expression of certain ‘housekeeping’ genes) yielded particularly poor results, surpassed even by normalization based on randomly selected gene sets. Conversely, seven of the algorithms approached what appears to be optimal normalization. Three of these algorithms rely on the identification of “ubiquitous” genes: genes expressed in all the samples studied, but never at very high or very low levels. We demonstrate that these include a “core” of genes expressed in many tissues in a mutually consistent pattern, which is suitable for use as an internal normalization guide. The new methods yield robustly normalized expression values, which is a prerequisite for the identification of differentially expressed and tissue-specific genes as potential biomarkers. PMID:24223126

Establishment of a New Quality Control and Vaccine Safety Test for Influenza Vaccines and Adjuvants Using Gene Expression Profiling

PubMed Central

Momose, Haruka; Mizukami, Takuo; Kuramitsu, Madoka; Takizawa, Kazuya; Masumi, Atsuko; Araki, Kumiko; Furuhata, Keiko; Yamaguchi, Kazunari; Hamaguchi, Isao

2015-01-01

We have previously identified 17 biomarker genes which were upregulated by whole virion influenza vaccines, and reported that gene expression profiles of these biomarker genes had a good correlation with conventional animal safety tests checking body weight and leukocyte counts. In this study, we have shown that conventional animal tests showed varied and no dose-dependent results in serially diluted bulk materials of influenza HA vaccines. In contrast, dose dependency was clearly shown in the expression profiles of biomarker genes, demonstrating higher sensitivity of gene expression analysis than the current animal safety tests of influenza vaccines. The introduction of branched DNA based-concurrent expression analysis could simplify the complexity of multiple gene expression approach, and could shorten the test period from 7 days to 3 days. Furthermore, upregulation of 10 genes, Zbp1, Mx2, Irf7, Lgals9, Ifi47, Tapbp, Timp1, Trafd1, Psmb9, and Tap2, was seen upon virosomal-adjuvanted vaccine treatment, indicating that these biomarkers could be useful for the safety control of virosomal-adjuvanted vaccines. In summary, profiling biomarker gene expression could be a useful, rapid, and highly sensitive method of animal safety testing compared with conventional methods, and could be used to evaluate the safety of various types of influenza vaccines, including adjuvanted vaccine. PMID:25909814

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

Gender-Specific Gene Expression in Post-Mortem Human Brain: Localization to Sex Chromosomes

PubMed Central

Vawter, Marquis P; Evans, Simon; Choudary, Prabhakara; Tomita, Hiroaki; Meador-Woodruff, Jim; Molnar, Margherita; Li, Jun; Lopez, Juan F; Myers, Rick; Cox, David; Watson, Stanley J; Akil, Huda; Jones, Edward G; Bunney, William E

2011-01-01

Gender differences in brain development and in the prevalence of neuropsychiatric disorders such as depression have been reported. Gender differences in human brain might be related to patterns of gene expression. Microarray technology is one useful method for investigation of gene expression in brain. We investigated gene expression, cell types, and regional expression patterns of differentially expressed sex chromosome genes in brain. We profiled gene expression in male and female dorsolateral prefrontal cortex, anterior cingulate cortex, and cerebellum using the Affymetrix oligonucleotide microarray platform. Differentially expressed genes between males and females on the Y chromosome (DBY, SMCY, UTY, RPS4Y, and USP9Y) and X chromosome (XIST) were confirmed using real-time PCR measurements. In situ hybridization confirmed the differential expression of gender-specific genes and neuronal expression of XIST, RPS4Y, SMCY, and UTY in three brain regions examined. The XIST gene, which silences gene expression on regions of the X chromosome, is expressed in a subset of neurons. Since a subset of neurons express gender-specific genes, neural subpopulations may exhibit a subtle sexual dimorphism at the level of differences in gene regulation and function. The distinctive pattern of neuronal expression of XIST, RPS4Y, SMCY, and UTY and other sex chromosome genes in neuronal subpopulations may possibly contribute to gender differences in prevalence noted for some neuropsychiatric disorders. Studies of the protein expression of these sex- chromosome-linked genes in brain tissue are required to address the functional consequences of the observed gene expression differences. PMID:14583743

Gene expression profiling of single cells on large-scale oligonucleotide arrays

PubMed Central

Hartmann, Claudia H.; Klein, Christoph A.

2006-01-01

Over the last decade, important insights into the regulation of cellular responses to various stimuli were gained by global gene expression analyses of cell populations. More recently, specific cell functions and underlying regulatory networks of rare cells isolated from their natural environment moved to the center of attention. However, low cell numbers still hinder gene expression profiling of rare ex vivo material in biomedical research. Therefore, we developed a robust method for gene expression profiling of single cells on high-density oligonucleotide arrays with excellent coverage of low abundance transcripts. The protocol was extensively tested with freshly isolated single cells of very low mRNA content including single epithelial, mature and immature dendritic cells and hematopoietic stem cells. Quantitative PCR confirmed that the PCR-based global amplification method did not change the relative ratios of transcript abundance and unsupervised hierarchical cluster analysis revealed that the histogenetic origin of an individual cell is correctly reflected by the gene expression profile. Moreover, the gene expression data from dendritic cells demonstrate that cellular differentiation and pathway activation can be monitored in individual cells. PMID:17071717

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

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.

[Study of testicular cancer gene expression in samples of oral leukoplakia and squamous cell carcinoma of the mouth].

PubMed

Skorodumova, L O; Muraev, A A; Zakharova, E S; Shepelev, M V; Korobko, I V; Zaderenko, I A; Ivanov, S Iu; Gnuchev, N V; Georgiev, G P; Larin, S S

2012-01-01

Cancer-testis (CT) antigens are normally expressed mostly in human germ cells, there is also an aberrant expression in some tumor cells. This expression profile makes them potential tumor growth biomarkers and a promising target for tumor immunotherapy. Specificity of CT genes expression in oral malignant and potentially malignant diseases, e.g. oral leukoplakia, is not yet studied. Data on CT genes expression profile in leukoplakia would allow developing new diagnostic methods with potential value for immunotherapy and prophylaxis of leukoplakia malignization. In our study we compared CT genes expression in normal oral mucosa, oral leukoplakia and oral squamous cell carcinoma. We are the first to describe CT genes expression in oral leukoplakia without dysplasia. This findings make impossible differential diagnosis of oral leukoplakia and squamous cell carcinoma on the basis of CT genes expression. The prognostic value of CT genes expression is still unclear, therefore the longitudinal studies are necessary.

Identification and resolution of artifacts in the interpretation of imprinted gene expression.

PubMed

Proudhon, Charlotte; Bourc'his, Déborah

2010-12-01

Genomic imprinting refers to genes that are epigenetically programmed in the germline to express exclusively or preferentially one allele in a parent-of-origin manner. Expression-based genome-wide screening for the identification of imprinted genes has failed to uncover a significant number of new imprinted genes, probably because of the high tissue- and developmental-stage specificity of imprinted gene expression. A very large number of technical and biological artifacts can also lead to the erroneous evidence of imprinted gene expression. In this article, we focus on three common sources of potential confounding effects: (i) random monoallelic expression in monoclonal cell populations, (ii) genetically determined monoallelic expression and (iii) contamination or infiltration of embryonic tissues with maternal material. This last situation specifically applies to genes that occur as maternally expressed in the placenta. Beside the use of reciprocal crosses that are instrumental to confirm the parental specificity of expression, we provide additional methods for the detection and elimination of these situations that can be misinterpreted as cases of imprinted expression.

Verification and Optimal Control of Context-Sensitive Probabilistic Boolean Networks Using Model Checking and Polynomial Optimization

PubMed Central

Hiraishi, Kunihiko

2014-01-01

One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs. PMID:24587766

Clustering gene expression data based on predicted differential effects of GV interaction.

PubMed

Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

Suppression of polygalacturonase gene expression in the phytopathogenic fungus Ophiostoma novo-ulmi by RNA interference.

PubMed

Carneiro, Joyce S; de la Bastide, Paul Y; Chabot, Meghan; Lerch, Lindsey; Hintz, William E

2010-05-01

The fungal pathogen, Ophiostomo novo-ulmi, has been responsible for the rapid decline of American elm (Ulmus americana) across North America and remains a serious threat to surviving elm populations. The production of pectinolytic polygalacturonase enzymes has been implicated as a virulence factor for many fungal pathogens, including O. novo-ulmi. Previous work has shown that the targeted disruption of the endopolygalacturonase gene locus epg1 of O. novo-ulmi reduced, but did not eliminate pectinase activity. In the present study, we evaluated the use of RNA interference (RNAi) as a method of suppressing expression of the epg1 locus in O. novo-ulmi and compared its efficiency to the gene disruption method. While there was a reduction in epg1-specific mRNA transcripts and in the amount of polygalacturonase enzyme secreted for both methods of gene regulation, neither method completely suppressed the expression of pectinase activity. There was, however, a significantly greater reduction in both transcript levels and secreted enzyme observed for some of the RNAi transformants. As the first demonstration of RNAi in O. novo-ulmi, this method of gene regulation shows promise in future studies of gene expression and pathogenicity. Copyright 2010 Elsevier Inc. All rights reserved.

Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress.

PubMed

Jo, Kyuri; Kwon, Hawk-Bin; Kim, Sun

2014-06-01

Measuring expression levels of genes at the whole genome level can be useful for many purposes, especially for revealing biological pathways underlying specific phenotype conditions. When gene expression is measured over a time period, we have opportunities to understand how organisms react to stress conditions over time. Thus many biologists routinely measure whole genome level gene expressions at multiple time points. However, there are several technical difficulties for analyzing such whole genome expression data. In addition, these days gene expression data is often measured by using RNA-sequencing rather than microarray technologies and then analysis of expression data is much more complicated since the analysis process should start with mapping short reads and produce differentially activated pathways and also possibly interactions among pathways. In addition, many useful tools for analyzing microarray gene expression data are not applicable for the RNA-seq data. Thus a comprehensive package for analyzing time series transcriptome data is much needed. In this article, we present a comprehensive package, Time-series RNA-seq Analysis Package (TRAP), integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment. In addition to implementing useful algorithms that are not available for RNA-seq data, we extended existing pathway analysis methods, ORA and SPIA, for time series analysis and estimates statistical values for combined dataset by an advanced metric. TRAP also produces visual summary of pathway interactions. Gene expression change labeling, a practical clustering method used in TRAP, enables more accurate interpretation of the data when combined with pathway analysis. We applied our methods on a real dataset for the analysis of rice (Oryza sativa L. Japonica nipponbare) upon drought stress. The result showed that TRAP was able to detect pathways more accurately than several existing methods. TRAP is available at http://biohealth.snu.ac.kr/software/TRAP/. Copyright © 2014 Elsevier Inc. All rights reserved.

Final technical report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edward DeLong

2011-10-07

Our overarching goals in this project were to: Develop and improve high-throughput sequencing methods and analytical approaches for quantitative analyses of microbial gene expression at the Hawaii Ocean Time Series Station and the Bermuda Atlantic Time Series Station; Conduct field analyses following gene expression patterns in picoplankton microbial communities in general, and Prochlorococcus flow sorted from that community, as they respond to different environmental variables (light, macronutrients, dissolved organic carbon), that are predicted to influence activity, productivity, and carbon cycling; Use the expression analyses of flow sorted Prochlorococcus to identify horizontally transferred genes and gene products, in particular those thatmore » are located in genomic islands and likely to confer habitat-specific fitness advantages; Use the microbial community gene expression data that we generate to gain insights, and test hypotheses, about the variability, genomic context, activity and function of as yet uncharacterized gene products, that appear highly expressed in the environment. We achieved the above goals, and even more over the course of the project. This includes a number of novel methodological developments, as well as the standardization of microbial community gene expression analyses in both field surveys, and experimental modalities. The availability of these methods, tools and approaches is changing current practice in microbial community analyses.« less

Arctigenin from Arctium lappa inhibits interleukin-2 and interferon gene expression in primary human T lymphocytes

PubMed Central

2011-01-01

Background Arctium lappa (Niubang), a Chinese herbal medicine, is used to treat tissue inflammation. This study investigates the effects of arctigenin (AC), isolated from A. lappa, on anti-CD3/CD28 Ab-stimulated cell proliferation and cytokine gene expression in primary human T lymphocytes. Methods Cell proliferation was determined with enzyme immunoassays and the tritiated thymidine uptake method. Cytokine production and gene expression were analyzed with reverse transcription-polymerase chain reaction. Results AC inhibited primary human T lymphocytes proliferation activated by anti-CD3/CD28 Ab. Cell viability test indicated that the inhibitory effects of AC on primary human T lymphocyte proliferation were not due to direct cytotoxicity. AC suppressed interleukin-2 (IL-2) and interferon-γ (IFN-γ) production in a concentration-dependent manner. Furthermore, AC decreased the IL-2 and IFN-γ gene expression in primary human T lymphocytes induced by anti-CD3/CD28 Ab. Reporter gene analyses revealed that AC decreased NF-AT-mediated reporter gene expression. Conclusion AC inhibited T lymphocyte proliferation and decreased the gene expression of IL-2, IFN-γ and NF-AT. PMID:21435270

Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods.

PubMed

Yang, Ze-Hui; Zheng, Rui; Gao, Yuan; Zhang, Qiang

2016-09-01

With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies. We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods. Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity. Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01). Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research. © 2015 John Wiley & Sons Ltd.

Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays.

PubMed

Goldstein, Darlene R

2006-10-01

Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time. A strategic alternative to exact multi-chip quantification on a full large chip set is to approximate expression values based on subsets of chips. This paper introduces an extrapolation method, Extrapolation Averaging (EA), and a resampling method, Partition Resampling (PR), to approximate expression in large studies. An examination of properties indicates that subset-based methods can perform well compared with exact expression quantification. The focus is on short oligonucleotide chips, but the same ideas apply equally well to any array type for which expression is quantified using an entire set of arrays, rather than for only a single array at a time. Software implementing Partition Resampling and Extrapolation Averaging is under development as an R package for the BioConductor project.

A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

PubMed

Choi, Yoonha; Coram, Marc; Peng, Jie; Tang, Hua

2017-07-01

Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

Network-Induced Classification Kernels for Gene Expression Profile Analysis

PubMed Central

Dror, Gideon; Shamir, Ron

2012-01-01

Abstract Computational classification of gene expression profiles into distinct disease phenotypes has been highly successful to date. Still, robustness, accuracy, and biological interpretation of the results have been limited, and it was suggested that use of protein interaction information jointly with the expression profiles can improve the results. Here, we study three aspects of this problem. First, we show that interactions are indeed relevant by showing that co-expressed genes tend to be closer in the network of interactions. Second, we show that the improved performance of one extant method utilizing expression and interactions is not really due to the biological information in the network, while in another method this is not the case. Finally, we develop a new kernel method—called NICK—that integrates network and expression data for SVM classification, and demonstrate that overall it achieves better results than extant methods while running two orders of magnitude faster. PMID:22697242

Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data.

PubMed

Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

2017-01-01

Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

PubMed Central

Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

2001-01-01

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

Methods and compositions for regulating gene expression in plant cells

NASA Technical Reports Server (NTRS)

Dai, Shunhong (Inventor); Beachy, Roger N. (Inventor); Luis, Maria Isabel Ordiz (Inventor)

2010-01-01

Novel chimeric plant promoter sequences are provided, together with plant gene expression cassettes comprising such sequences. In certain preferred embodiments, the chimeric plant promoters comprise the BoxII cis element and/or derivatives thereof. In addition, novel transcription factors are provided, together with nucleic acid sequences encoding such transcription factors and plant gene expression cassettes comprising such nucleic acid sequences. In certain preferred embodiments, the novel transcription factors comprise the acidic domain, or fragments thereof, of the RF2a transcription factor. Methods for using the chimeric plant promoter sequences and novel transcription factors in regulating the expression of at least one gene of interest are provided, together with transgenic plants comprising such chimeric plant promoter sequences and novel transcription factors.

A highly sensitive and accurate gene expression analysis by sequencing ("bead-seq") for a single cell.

PubMed

Matsunaga, Hiroko; Goto, Mari; Arikawa, Koji; Shirai, Masataka; Tsunoda, Hiroyuki; Huang, Huan; Kambara, Hideki

2015-02-15

Analyses of gene expressions in single cells are important for understanding detailed biological phenomena. Here, a highly sensitive and accurate method by sequencing (called "bead-seq") to obtain a whole gene expression profile for a single cell is proposed. A key feature of the method is to use a complementary DNA (cDNA) library on magnetic beads, which enables adding washing steps to remove residual reagents in a sample preparation process. By adding the washing steps, the next steps can be carried out under the optimal conditions without losing cDNAs. Error sources were carefully evaluated to conclude that the first several steps were the key steps. It is demonstrated that bead-seq is superior to the conventional methods for single-cell gene expression analyses in terms of reproducibility, quantitative accuracy, and biases caused during sample preparation and sequencing processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Global gene expression analysis by combinatorial optimization.

PubMed

Ameur, Adam; Aurell, Erik; Carlsson, Mats; Westholm, Jakub Orzechowski

2004-01-01

Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.

Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments

PubMed Central

Maza, Elie; Frasse, Pierre; Senin, Pavel; Bouzayen, Mondher; Zouine, Mohamed

2013-01-01

In recent years, RNA-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. The present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. Real RNA-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named “Median Ratio Normalization” (MRN) gives the lower number of false discoveries. Within this group the MRN method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. The newly proposed MRN method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that MRN is more consistent and robust than existing methods. PMID:26442135

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

A statistical method for measuring activation of gene regulatory networks.

PubMed

Esteves, Gustavo H; Reis, Luiz F L

2018-06-13

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

[Effect of EMP-1 gene on human esophageal cancer cell line].

PubMed

Wang, Hai-tao; Liu, Zhi-hua; Wang, Xiu-qin; Wu, Min

2002-03-01

EMP-1 was selected from a series of differential expressed genes obtained from cDNA microarray in the authors' lab. Epithelial membrane pnteiu-1 gene (EMP-1) was expressed 6 fold lower in esophageal cancer than in normal tissue. The authors further designed the experiment to study the effect of human EMP-1 gene on human esophageal cancer cell line in order to explain the function of this gene on the carcinogensis and progression esophageal cancer. EMP-1 gene was cloned into eukaryotic vector and transfected into the human esophageal cancer cell line. The transfection effect was qualified by Western blot and RT-PCR method. The cell growth curve was observed and the cell cycle was checked by FACS method. EMP-1 was transfected into EC9706 cell line and its expression was up-regulated. The cell growth is accelerated and expression of EMP-1 is linked to induction of S phase arrest. EMP-1 gene has some relationship with carcinogenesis of esophagus.

Computerized system for recognition of autism on the basis of gene expression microarray data.

PubMed

Latkowski, Tomasz; Osowski, Stanislaw

2015-01-01

The aim of this paper is to provide a means to recognize a case of autism using gene expression microarrays. The crucial task is to discover the most important genes which are strictly associated with autism. The paper presents an application of different methods of gene selection, to select the most representative input attributes for an ensemble of classifiers. The set of classifiers is responsible for distinguishing autism data from the reference class. Simultaneous application of a few gene selection methods enables analysis of the ill-conditioned gene expression matrix from different points of view. The results of selection combined with a genetic algorithm and SVM classifier have shown increased accuracy of autism recognition. Early recognition of autism is extremely important for treatment of children and increases the probability of their recovery and return to normal social communication. The results of this research can find practical application in early recognition of autism on the basis of gene expression microarray analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery

PubMed Central

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-01-01

Background DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. Results GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. Conclusion GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at . PMID:19728865

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

PubMed

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-09-03

DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

PubMed Central

2012-01-01

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods. Reviewers This article was reviewed by Arcady Mushegian, Byung-Soo Kim and Joel Bader. PMID:23227854

Development of multitissue microfluidic dynamic array for assessing changes in gene expression associated with channel catfish appetite, growth, metabolism, and intestinal health

USDA-ARS?s Scientific Manuscript database

Large-scale, gene expression methods allow for high throughput analysis of physiological pathways at a fraction of the cost of individual gene expression analysis. Systems, such as the Fluidigm quantitative PCR array described here, can provide powerful assessments of the effects of diet, environme...

Simultaneous knockdown of six non-family genes using a single synthetic RNAi fragment in Arabidopsis thaliana

DOE Office of Scientific and Technical Information (OSTI.GOV)

Czarnecki, Olaf; Bryan, Anthony C.; Jawdy, Sara S.

Genetic engineering of plants that results in successful establishment of new biochemical or regulatory pathways requires stable introduction of one or more genes into the plant genome. It might also be necessary to down-regulate or turn off expression of endogenous genes in order to reduce activity of competing pathways. An established way to knockdown gene expression in plants is expressing a hairpin-RNAi construct, eventually leading to degradation of a specifically targeted mRNA. Knockdown of multiple genes that do not share homologous sequences is still challenging and involves either sophisticated cloning strategies to create vectors with different serial expression constructs ormore » multiple transformation events that is often restricted by a lack of available transformation markers. Synthetic RNAi fragments were assembled in yeast carrying homologous sequences to six or seven non-family genes and introduced into pAGRIKOLA. Transformation of Arabidopsis thaliana and subsequent expression analysis of targeted genes proved efficient knockdown of all target genes. In conclusion, we present a simple and cost-effective method to create constructs to simultaneously knockdown multiple non-family genes or genes that do not share sequence homology. The presented method can be applied in plant and animal synthetic biology as well as traditional plant and animal genetic engineering.« less

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm.

PubMed

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene x gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene x gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

PubMed Central

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms. PMID:18466570

Simultaneous knockdown of six non-family genes using a single synthetic RNAi fragment in Arabidopsis thaliana

DOE PAGES

Czarnecki, Olaf; Bryan, Anthony C.; Jawdy, Sara S.; ...

2016-02-17

Genetic engineering of plants that results in successful establishment of new biochemical or regulatory pathways requires stable introduction of one or more genes into the plant genome. It might also be necessary to down-regulate or turn off expression of endogenous genes in order to reduce activity of competing pathways. An established way to knockdown gene expression in plants is expressing a hairpin-RNAi construct, eventually leading to degradation of a specifically targeted mRNA. Knockdown of multiple genes that do not share homologous sequences is still challenging and involves either sophisticated cloning strategies to create vectors with different serial expression constructs ormore » multiple transformation events that is often restricted by a lack of available transformation markers. Synthetic RNAi fragments were assembled in yeast carrying homologous sequences to six or seven non-family genes and introduced into pAGRIKOLA. Transformation of Arabidopsis thaliana and subsequent expression analysis of targeted genes proved efficient knockdown of all target genes. In conclusion, we present a simple and cost-effective method to create constructs to simultaneously knockdown multiple non-family genes or genes that do not share sequence homology. The presented method can be applied in plant and animal synthetic biology as well as traditional plant and animal genetic engineering.« less

The use of a viral 2A sequence for the simultaneous over-expression of both the vgf gene and enhanced green fluorescent protein (eGFP) in vitro and in vivo

PubMed Central

Lewis, Jo E.; Brameld, John M.; Hill, Phil; Barrett, Perry; Ebling, Francis J.P.; Jethwa, Preeti H.

2015-01-01

Introduction The viral 2A sequence has become an attractive alternative to the traditional internal ribosomal entry site (IRES) for simultaneous over-expression of two genes and in combination with recombinant adeno-associated viruses (rAAV) has been used to manipulate gene expression in vitro. New method To develop a rAAV construct in combination with the viral 2A sequence to allow long-term over-expression of the vgf gene and fluorescent marker gene for tracking of the transfected neurones in vivo. Results Transient transfection of the AAV plasmid containing the vgf gene, viral 2A sequence and eGFP into SH-SY5Y cells resulted in eGFP fluorescence comparable to a commercially available reporter construct. This increase in fluorescent cells was accompanied by an increase in VGF mRNA expression. Infusion of the rAAV vector containing the vgf gene, viral 2A sequence and eGFP resulted in eGFP fluorescence in the hypothalamus of both mice and Siberian hamsters, 32 weeks post infusion. In situ hybridisation confirmed that the location of VGF mRNA expression in the hypothalamus corresponded to the eGFP pattern of fluorescence. Comparison with old method The viral 2A sequence is much smaller than the traditional IRES and therefore allowed over-expression of the vgf gene with fluorescent tracking without compromising viral capacity. Conclusion The use of the viral 2A sequence in the AAV plasmid allowed the simultaneous expression of both genes in vitro. When used in combination with rAAV it resulted in long-term over-expression of both genes at equivalent locations in the hypothalamus of both Siberian hamsters and mice, without any adverse effects. PMID:26300182

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

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.

Cardiac Gene Therapy: Optimization of Gene Delivery Techniques In Vivo

PubMed Central

Katz, Michael G.; Swain, JaBaris D.; White, Jennifer D.; Low, David; Stedman, Hansell

2010-01-01

Abstract Vector-mediated cardiac gene therapy holds tremendous promise as a translatable platform technology for treating many cardiovascular diseases. The ideal technique is one that is efficient and practical, allowing for global cardiac gene expression, while minimizing collateral expression in other organs. Here we survey the available in vivo vector-mediated cardiac gene delivery methods—including transcutaneous, intravascular, intramuscular, and cardiopulmonary bypass techniques—with consideration of the relative merits and deficiencies of each. Review of available techniques suggests that an optimal method for vector-mediated gene delivery to the large animal myocardium would ideally employ retrograde and/or anterograde transcoronary gene delivery,extended vector residence time in the coronary circulation, an increased myocardial transcapillary gradient using physical methods, increased endothelial permeability with pharmacological agents, minimal collateral gene expression by isolation of the cardiac circulation from the systemic, and have low immunogenicity. PMID:19947886

Id-1 and Id-2 genes and products as markers of epithelial cancer

DOEpatents

Desprez, Pierre-Yves [El Cerrito, CA; Campisi, Judith [Berkeley, CA

2008-09-30

A method for detection and prognosis of breast cancer and other types of cancer. The method comprises detecting expression, if any, for both an Id-1 and an Id-2 genes, or the ratio thereof, of gene products in samples of breast tissue obtained from a patient. When expressed, Id-1 gene is a prognostic indicator that breast cancer cells are invasive and metastatic, whereas Id-2 gene is a prognostic indicator that breast cancer cells are localized and noninvasive in the breast tissue.

Id-1 and Id-2 genes and products as markers of epithelial cancer

DOEpatents

Desprez, Pierre-Yves [El Cerrito, CA; Campisi, Judith [Berkeley, CA

2011-10-04

A method for detection and prognosis of breast cancer and other types of cancer. The method comprises detecting expression, if any, for both an Id-1 and an Id-2 genes, or the ratio thereof, of gene products in samples of breast tissue obtained from a patient. When expressed, Id-1 gene is a prognostic indicator that breast cancer cells are invasive and metastatic, whereas Id-2 gene is a prognostic indicator that breast cancer cells are localized and noninvasive in the breast tissue.

IDP-ASE: haplotyping and quantifying allele-specific expression at the gene and gene isoform level by hybrid sequencing

PubMed Central

Deonovic, Benjamin; Wang, Yunhao; Weirather, Jason; Wang, Xiu-Jie; Au, Kin Fai

2017-01-01

Abstract Allele-specific expression (ASE) is a fundamental problem in studying gene regulation and diploid transcriptome profiles, with two key challenges: (i) haplotyping and (ii) estimation of ASE at the gene isoform level. Existing ASE analysis methods are limited by a dependence on haplotyping from laborious experiments or extra genome/family trio data. In addition, there is a lack of methods for gene isoform level ASE analysis. We developed a tool, IDP-ASE, for full ASE analysis. By innovative integration of Third Generation Sequencing (TGS) long reads with Second Generation Sequencing (SGS) short reads, the accuracy of haplotyping and ASE quantification at the gene and gene isoform level was greatly improved as demonstrated by the gold standard data GM12878 data and semi-simulation data. In addition to methodology development, applications of IDP-ASE to human embryonic stem cells and breast cancer cells indicate that the imbalance of ASE and non-uniformity of gene isoform ASE is widespread, including tumorigenesis relevant genes and pluripotency markers. These results show that gene isoform expression and allele-specific expression cooperate to provide high diversity and complexity of gene regulation and expression, highlighting the importance of studying ASE at the gene isoform level. Our study provides a robust bioinformatics solution to understand ASE using RNA sequencing data only. PMID:27899656

Gene expression in the rectus abdominus muscle of patients with and without pelvic organ prolapse.

PubMed

Hundley, Andrew F; Yuan, Lingwen; Visco, Anthony G

2008-02-01

The objective of the study was to compare gene expression in a group of actin and myosin-related proteins in the rectus muscle of 15 patients with pelvic organ prolapse and 13 controls. Six genes previously identified by microarray GeneChip analysis were examined using real-time quantitative reverse transcriptase-polymerase chain reaction analysis, including 2 genes showing differential expression in pubococcygeus muscle. Samples and controls were run in triplicate in multiplexed wells, and levels of gene expression were analyzed using the comparative critical threshold method. One gene, MYH3, was 3.2 times overexpressed in patients with prolapse (P = .032), but no significant differences in expression were seen for the other genes examined. An age-matched subset of 9 patients and controls showed that MYH3 gene expression was no longer significantly different (P = .058). Differential messenger ribonucleic acid levels of actin and myosin-related genes in patients with pelvic organ prolapse and controls may be limited to skeletal muscle from the pelvic floor.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

PubMed

Xie, Xin-Ping; Xie, Yu-Feng; Wang, Hong-Qiang

2017-08-23

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal. This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis. Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

Digital Detection of Multiple Minority Mutants and Expression Levels of Multiple Colorectal Cancer-Related Genes Using Digital-PCR Coupled with Bead-Array

PubMed Central

Huang, Huan; Li, Shuo; Sun, Lizhou; Zhou, Guohua

2015-01-01

To simultaneously analyze mutations and expression levels of multiple genes on one detection platform, we proposed a method termed “multiplex ligation-dependent probe amplification–digital amplification coupled with hydrogel bead-array” (MLPA–DABA) and applied it to diagnose colorectal cancer (CRC). CRC cells and tissues were sampled to extract nucleic acid, perform MLPA with sequence-tagged probes, perform digital emulsion polymerase chain reaction (PCR), and produce a hydrogel bead-array to immobilize beads and form a single bead layer on the array. After hybridization with fluorescent probes, the number of colored beads, which reflects the abundance of expressed genes and the mutation rate, was counted for diagnosis. Only red or green beads occurred on the chips in the mixed samples, indicating the success of single-molecule PCR. When a one-source sample was analyzed using mixed MLPA probes, beads of only one color occurred, suggesting the high specificity of the method in analyzing CRC mutation and gene expression. In gene expression analysis of a CRC tissue from one CRC patient, the mutant percentage was 3.1%, and the expression levels of CRC-related genes were much higher than those of normal tissue. The highly sensitive MLPA–DABA succeeds in the relative quantification of mutations and gene expressions of exfoliated cells in stool samples of CRC patients on the same chip platform. MLPA–DABA coupled with hydrogel bead-array is a promising method in the non-invasive diagnosis of CRC. PMID:25880764

Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative

PubMed Central

Bickel, David R.; Montazeri, Zahra; Hsieh, Pei-Chun; Beatty, Mary; Lawit, Shai J.; Bate, Nicholas J.

2009-01-01

Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions. Availability: http://www.oisb.ca points to R code implementing the methods (R Development Core Team 2004). Contact: dbickel@uottawa.ca Supplementary information: http://www.davidbickel.com PMID:19218351

Selection of reliable reference genes for gene expression studies in Trichoderma afroharzianum LTR-2 under oxalic acid stress.

PubMed

Lyu, Yuping; Wu, Xiaoqing; Ren, He; Zhou, Fangyuan; Zhou, Hongzi; Zhang, Xinjian; Yang, Hetong

2017-10-01

An appropriate reference gene is required to get reliable results from gene expression analysis by quantitative real-time reverse transcription PCR (qRT-PCR). In order to identify stable and reliable reference genes in Trichoderma afroharzianum under oxalic acid (OA) stress, six commonly used housekeeping genes, i.e., elongation factor 1, ubiquitin, ubiquitin-conjugating enzyme, glyceraldehyde-3-phosphate dehydrogenase, α-tubulin, actin, from the effective biocontrol isolate T. afroharzianum strain LTR-2 were tested for their expression during growth in liquid culture amended with OA. Four in silico programs (comparative ΔCt, NormFinder, geNorm and BestKeeper) were used to evaluate the expression stabilities of six candidate reference genes. The elongation factor 1 gene EF-1 was identified as the most stably expressed reference gene, and was used as the normalizer to quantify the expression level of the oxalate decarboxylase coding gene OXDC in T. afroharzianum strain LTR-2 under OA stress. The result showed that the expression of OXDC was significantly up-regulated as expected. This study provides an effective method to quantify expression changes of target genes in T. afroharzianum under OA stress. Copyright © 2017 Elsevier B.V. All rights reserved.

Adaptation of video game UVW mapping to 3D visualization of gene expression patterns

NASA Astrophysics Data System (ADS)

Vize, Peter D.; Gerth, Victor E.

2007-01-01

Analysis of gene expression patterns within an organism plays a critical role in associating genes with biological processes in both health and disease. During embryonic development the analysis and comparison of different gene expression patterns allows biologists to identify candidate genes that may regulate the formation of normal tissues and organs and to search for genes associated with congenital diseases. No two individual embryos, or organs, are exactly the same shape or size so comparing spatial gene expression in one embryo to that in another is difficult. We will present our efforts in comparing gene expression data collected using both volumetric and projection approaches. Volumetric data is highly accurate but difficult to process and compare. Projection methods use UV mapping to align texture maps to standardized spatial frameworks. This approach is less accurate but is very rapid and requires very little processing. We have built a database of over 180 3D models depicting gene expression patterns mapped onto the surface of spline based embryo models. Gene expression data in different models can easily be compared to determine common regions of activity. Visualization software, both Java and OpenGL optimized for viewing 3D gene expression data will also be demonstrated.

Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2017-01-25

With the current microarray and RNA-seq technologies, two-sample genome-wide expression data have been widely collected in biological and medical studies. The related differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available. In practice, discordant molecular behaviors among a series of data sets can be of biological and clinical interest. In this study, a statistical method is proposed for detecting discordance gene set enrichment. Our method is based on a two-level multivariate normal mixture model. It is statistically efficient with linearly increased parameter space when the number of data sets is increased. The model-based probability of discordance enrichment can be calculated for gene set detection. We apply our method to a microarray expression data set collected from forty-five matched tumor/non-tumor pairs of tissues for studying pancreatic cancer. We divided the data set into a series of non-overlapping subsets according to the tumor/non-tumor paired expression ratio of gene PNLIP (pancreatic lipase, recently shown it association with pancreatic cancer). The log-ratio ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). Our purpose is to understand whether any gene sets are enriched in discordant behaviors among these subsets (when the log-ratio is increased from negative to positive). We focus on KEGG pathways. The detected pathways will be useful for our further understanding of the role of gene PNLIP in pancreatic cancer research. Among the top list of detected pathways, the neuroactive ligand receptor interaction and olfactory transduction pathways are the most significant two. Then, we consider gene TP53 that is well-known for its role as tumor suppressor in cancer research. The log-ratio also ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). We divided the microarray data set again according to the expression ratio of gene TP53. After the discordance enrichment analysis, we observed overall similar results and the above two pathways are still the most significant detections. More interestingly, only these two pathways have been identified for their association with pancreatic cancer in a pathway analysis of genome-wide association study (GWAS) data. This study illustrates that some disease-related pathways can be enriched in discordant molecular behaviors when an important disease-related gene changes its expression. Our proposed statistical method is useful in the detection of these pathways. Furthermore, our method can also be applied to genome-wide expression data collected by the recent RNA-seq technology.

Combinatorial Screening for Transgenic Yeasts with High Cellulase Activities in Combination with a Tunable Expression System

PubMed Central

Ito, Yoichiro; Yamanishi, Mamoru; Ikeuchi, Akinori; Imamura, Chie; Matsuyama, Takashi

2015-01-01

Combinatorial screening used together with a broad library of gene expression cassettes is expected to produce a powerful tool for the optimization of the simultaneous expression of multiple enzymes. Recently, we proposed a highly tunable protein expression system that utilized multiple genome-integrated target genes to fine-tune enzyme expression in yeast cells. This tunable system included a library of expression cassettes each composed of three gene-expression control elements that in different combinations produced a wide range of protein expression levels. In this study, four gene expression cassettes with graded protein expression levels were applied to the expression of three cellulases: cellobiohydrolase 1, cellobiohydrolase 2, and endoglucanase 2. After combinatorial screening for transgenic yeasts simultaneously secreting these three cellulases, we obtained strains with higher cellulase expressions than a strain harboring three cellulase-expression constructs within one high-performance gene expression cassette. These results show that our method will be of broad use throughout the field of metabolic engineering. PMID:26692026

Transcriptome assembly and digital gene expression atlas of the rainbow trout

USDA-ARS?s Scientific Manuscript database

Background: Transcriptome analysis is a preferred method for gene discovery, marker development and gene expression profiling in non-model organisms. Previously, we sequenced a transcriptome reference using Sanger-based and 454-pyrosequencing, however, a transcriptome assembly is still incomplete an...

Commentary: Gene-Environment Interplay in the Context of Genetics, Epigenetics, and Gene Expression.

ERIC Educational Resources Information Center

Kramer, Douglas A.

2005-01-01

Objective: To comment on the article in this issue of the Journal by Professor Michael Rutter, "Environmentally Mediated Risks for Psychopathology: Research Strategies and Findings," in the context of current research findings on gene-environment interaction, epigenetics, and gene expression. Method: Animal and human studies are reviewed that…

Identification of Potential Anticancer Activities of Novel Ganoderma lucidum Extracts Using Gene Expression and Pathway Network Analysis

PubMed Central

Kao, Chi H.J.; Bishop, Karen S.; Xu, Yuanye; Han, Dug Yeo; Murray, Pamela M.; Marlow, Gareth J.; Ferguson, Lynnette R.

2016-01-01

Ganoderma lucidum (lingzhi) has been used for the general promotion of health in Asia for many centuries. The common method of consumption is to boil lingzhi in water and then drink the liquid. In this study, we examined the potential anticancer activities of G. lucidum submerged in two commonly consumed forms of alcohol in East Asia: malt whiskey and rice wine. The anticancer effect of G. lucidum, using whiskey and rice wine-based extraction methods, has not been previously reported. The growth inhibition of G. lucidum whiskey and rice wine extracts on the prostate cancer cell lines, PC3 and DU145, was determined. Using Affymetrix gene expression assays, several biologically active pathways associated with the anticancer activities of G. lucidum extracts were identified. Using gene expression analysis (real-time polymerase chain reaction [RT-PCR]) and protein analysis (Western blotting), we confirmed the expression of key genes and their associated proteins that were initially identified with Affymetrix gene expression analysis. PMID:27006591

Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data

PubMed Central

George, Barbara Jane; Reif, David M.; Gallagher, Jane E.; Williams-DeVane, ClarLynda R.; Heidenfelder, Brooke L.; Hudgens, Edward E.; Jones, Wendell; Neas, Lucas; Hubal, Elaine A. Cohen; Edwards, Stephen W.

2015-01-01

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels. PMID:25643280

DEEP--a tool for differential expression effector prediction.

PubMed

Degenhardt, Jost; Haubrock, Martin; Dönitz, Jürgen; Wingender, Edgar; Crass, Torsten

2007-07-01

High-throughput methods for measuring transcript abundance, like SAGE or microarrays, are widely used for determining differences in gene expression between different tissue types, dignities (normal/malignant) or time points. Further analysis of such data frequently aims at the identification of gene interaction networks that form the causal basis for the observed properties of the systems under examination. To this end, it is usually not sufficient to rely on the measured gene expression levels alone; rather, additional biological knowledge has to be taken into account in order to generate useful hypotheses about the molecular mechanism leading to the realization of a certain phenotype. We present a method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH database on signal transduction, to predict additional--and not necessarily differentially expressed--genes or gene products which might participate in processes specific for either of the examined tissues or conditions. In a first step, significance values for over-expression in tissue/condition A or B are assigned to all genes in the expression data set. Genes with a significance value exceeding a certain threshold are used as starting points for the reconstruction of a graph with signaling components as nodes and signaling events as edges. In a subsequent graph traversal process, again starting from the previously identified differentially expressed genes, all encountered nodes 'inherit' all their starting nodes' significance values. In a final step, the graph is visualized, the nodes being colored according to a weighted average of their inherited significance values. Each node's, or sub-network's, predominant color, ranging from green (significant for tissue/condition A) over yellow (not significant for either tissue/condition) to red (significant for tissue/condition B), thus gives an immediate visual clue on which molecules--differentially expressed or not--may play pivotal roles in the tissues or conditions under examination. The described method has been implemented in Java as a client/server application and a web interface called DEEP (Differential Expression Effector Prediction). The client, which features an easy-to-use graphical interface, can freely be downloaded from the following URL: http://deep.bioinf.med.uni-goettingen.de.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

PubMed

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Validation of Reference Genes for Real-Time Quantitative PCR (qPCR) Analysis of Avibacterium paragallinarum.

PubMed

Wen, Shuxiang; Chen, Xiaoling; Xu, Fuzhou; Sun, Huiling

2016-01-01

Real-time quantitative reverse transcription PCR (qRT-PCR) offers a robust method for measurement of gene expression levels. Selection of reliable reference gene(s) for gene expression study is conducive to reduce variations derived from different amounts of RNA and cDNA, the efficiency of the reverse transcriptase or polymerase enzymes. Until now reference genes identified for other members of the family Pasteurellaceae have not been validated for Avibacterium paragallinarum. The aim of this study was to validate nine reference genes of serovars A, B, and C strains of A. paragallinarum in different growth phase by qRT-PCR. Three of the most widely used statistical algorithms, geNorm, NormFinder and ΔCT method were used to evaluate the expression stability of reference genes. Data analyzed by overall rankings showed that in exponential and stationary phase of serovar A, the most stable reference genes were gyrA and atpD respectively; in exponential and stationary phase of serovar B, the most stable reference genes were atpD and recN respectively; in exponential and stationary phase of serovar C, the most stable reference genes were rpoB and recN respectively. This study provides recommendations for stable endogenous control genes for use in further studies involving measurement of gene expression levels.

Bayesian median regression for temporal gene expression data

NASA Astrophysics Data System (ADS)

Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

Parkinson's disease candidate gene prioritization based on expression profile of midbrain dopaminergic neurons

PubMed Central

2010-01-01

Background Parkinson's disease is the second most common neurodegenerative disorder. The pathological hallmark of the disease is degeneration of midbrain dopaminergic neurons. Genetic association studies have linked 13 human chromosomal loci to Parkinson's disease. Identification of gene(s), as part of the etiology of Parkinson's disease, within the large number of genes residing in these loci can be achieved through several approaches, including screening methods, and considering appropriate criteria. Since several of the indentified Parkinson's disease genes are expressed in substantia nigra pars compact of the midbrain, expression within the neurons of this area could be a suitable criterion to limit the number of candidates and identify PD genes. Methods In this work we have used the combination of findings from six rodent transcriptome analysis studies on the gene expression profile of midbrain dopaminergic neurons and the PARK loci in OMIM (Online Mendelian Inheritance in Man) database, to identify new candidate genes for Parkinson's disease. Results Merging the two datasets, we identified 20 genes within PARK loci, 7 of which are located in an orphan Parkinson's disease locus and one, which had been identified as a disease gene. In addition to identifying a set of candidates for further genetic association studies, these results show that the criteria of expression in midbrain dopaminergic neurons may be used to narrow down the number of genes in PARK loci for such studies. PMID:20716345

Identification and resolution of artifacts in the interpretation of imprinted gene expression

PubMed Central

Proudhon, Charlotte

2010-01-01

Genomic imprinting refers to genes that are epigenetically programmed in the germline to express exclusively or preferentially one allele in a parent-of-origin manner. Expression-based genome-wide screening for the identification of imprinted genes has failed to uncover a significant number of new imprinted genes, probably because of the high tissue- and developmental-stage specificity of imprinted gene expression. A very large number of technical and biological artifacts can also lead to the erroneous evidence of imprinted gene expression. In this article, we focus on three common sources of potential confounding effects: (i) random monoallelic expression in monoclonal cell populations, (ii) genetically determined monoallelic expression and (iii) contamination or infiltration of embryonic tissues with maternal material. This last situation specifically applies to genes that occur as maternally expressed in the placenta. Beside the use of reciprocal crosses that are instrumental to confirm the parental specificity of expression, we provide additional methods for the detection and elimination of these situations that can be misinterpreted as cases of imprinted expression. PMID:20829207

A multi-strategy approach to informative gene identification from gene expression data.

PubMed

Liu, Ziying; Phan, Sieu; Famili, Fazel; Pan, Youlian; Lenferink, Anne E G; Cantin, Christiane; Collins, Catherine; O'Connor-McCourt, Maureen D

2010-02-01

An unsupervised multi-strategy approach has been developed to identify informative genes from high throughput genomic data. Several statistical methods have been used in the field to identify differentially expressed genes. Since different methods generate different lists of genes, it is very challenging to determine the most reliable gene list and the appropriate method. This paper presents a multi-strategy method, in which a combination of several data analysis techniques are applied to a given dataset and a confidence measure is established to select genes from the gene lists generated by these techniques to form the core of our final selection. The remainder of the genes that form the peripheral region are subject to exclusion or inclusion into the final selection. This paper demonstrates this methodology through its application to an in-house cancer genomics dataset and a public dataset. The results indicate that our method provides more reliable list of genes, which are validated using biological knowledge, biological experiments, and literature search. We further evaluated our multi-strategy method by consolidating two pairs of independent datasets, each pair is for the same disease, but generated by different labs using different platforms. The results showed that our method has produced far better results.

Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis.

PubMed

Li, Wenli; Turner, Amy; Aggarwal, Praful; Matter, Andrea; Storvick, Erin; Arnett, Donna K; Broeckel, Ulrich

2015-12-16

Whole transcriptome sequencing (RNA-seq) represents a powerful approach for whole transcriptome gene expression analysis. However, RNA-seq carries a few limitations, e.g., the requirement of a significant amount of input RNA and complications led by non-specific mapping of short reads. The Ion AmpliSeq Transcriptome Human Gene Expression Kit (AmpliSeq) was recently introduced by Life Technologies as a whole-transcriptome, targeted gene quantification kit to overcome these limitations of RNA-seq. To assess the performance of this new methodology, we performed a comprehensive comparison of AmpliSeq with RNA-seq using two well-established next-generation sequencing platforms (Illumina HiSeq and Ion Torrent Proton). We analyzed standard reference RNA samples and RNA samples obtained from human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). Using published data from two standard RNA reference samples, we observed a strong concordance of log2 fold change for all genes when comparing AmpliSeq to Illumina HiSeq (Pearson's r = 0.92) and Ion Torrent Proton (Pearson's r = 0.92). We used ROC, Matthew's correlation coefficient and RMSD to determine the overall performance characteristics. All three statistical methods demonstrate AmpliSeq as a highly accurate method for differential gene expression analysis. Additionally, for genes with high abundance, AmpliSeq outperforms the two RNA-seq methods. When analyzing four closely related hiPSC-CM lines, we show that both AmpliSeq and RNA-seq capture similar global gene expression patterns consistent with known sources of variations. Our study indicates that AmpliSeq excels in the limiting areas of RNA-seq for gene expression quantification analysis. Thus, AmpliSeq stands as a very sensitive and cost-effective approach for very large scale gene expression analysis and mRNA marker screening with high accuracy.

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

A Pipeline for High-Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics

PubMed Central

House, John S.; Grimm, Fabian A.; Jima, Dereje D.; Zhou, Yi-Hui; Rusyn, Ivan; Wright, Fred A.

2017-01-01

Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose–response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration–response points of departure. The methods are extensible to other forms of concentration–response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state. PMID:29163636

Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

PubMed Central

Emmert-Streib, Frank; Glazko, Galina V.; Altay, Gökmen; de Matos Simoes, Ricardo

2012-01-01

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms. PMID:22408642

Hormone Receptor Expression Analyses in Neoplastic and Non-Neoplastic Canine Mammary Tissue by a Bead Based Multiplex Branched DNA Assay: A Gene Expression Study in Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Samples.

PubMed

Mohr, Annika; Lüder Ripoli, Florenza; Hammer, Susanne Conradine; Willenbrock, Saskia; Hewicker-Trautwein, Marion; Kiełbowicz, Zdzisław; Murua Escobar, Hugo; Nolte, Ingo

2016-01-01

Immunohistochemistry (IHC) is currently considered the method of choice for steroid hormone receptor status evaluation in human breast cancer and, therefore, it is commonly utilized for assessing canine mammary tumors. In case of low hormone receptor expression, IHC is limited and thus is complemented by molecular analyses. In the present study, a multiplex bDNA assay was evaluated as a method for hormone receptor gene expression detection in canine mammary tissues. Estrogen receptor (ESR1), progesterone receptor (PGR), prolactin receptor (PRLR) and growth hormone receptor (GHR) gene expressions were evaluated in neoplastic and non-neoplastic canine mammary tissues. A set of 119 fresh frozen and 180 formalin-fixed, paraffin-embedded (FFPE) was comparatively analyzed and used for assay evaluation. Furthermore, a possible association between the hormone receptor expression in different histological subtypes of canine malignant mammary tumors and the castration status, breed and invasive growth of the tumor were analyzed. The multiplex bDNA assay proved to be more sensitive for fresh frozen specimens. Hormone receptor expression found was significantly decreased in malignant mammary tumors in comparison to non-neoplastic tissue and benign mammary tumors. Among the histological subtypes the lowest gene expression levels of ESR1, PGR and PRLR were found in solid, anaplastic and ductal carcinomas. In summary, the evaluation showed that the measurement of hormone receptors with the multiplex bDNA assay represents a practicable method for obtaining detailed quantitative information about gene expression in canine mammary tissue for future studies. Still, comparison with IHC or quantitative real-time PCR is needed for further validation of the present method.

Identification of Reference Genes for Normalizing Quantitative Real-Time PCR in Urechis unicinctus

NASA Astrophysics Data System (ADS)

Bai, Yajiao; Zhou, Di; Wei, Maokai; Xie, Yueyang; Gao, Beibei; Qin, Zhenkui; Zhang, Zhifeng

2018-06-01

The reverse transcription quantitative real-time PCR (RT-qPCR) has become one of the most important techniques of studying gene expression. A set of valid reference genes are essential for the accurate normalization of data. In this study, five candidate genes were analyzed with geNorm, NormFinder, BestKeeper and ΔCt methods to identify the genes stably expressed in echiuran Urechis unicinctus, an important commercial marine benthic worm, under abiotic (sulfide stress) and normal (adult tissues, embryos and larvae at different development stages) conditions. The comprehensive results indicated that the expression of TBP was the most stable at sulfide stress and in developmental process, while the expression of EF- 1- α was the most stable at sulfide stress and in various tissues. TBP and EF- 1- α were recommended as a suitable reference gene combination to accurately normalize the expression of target genes at sulfide stress; and EF- 1- α, TBP and TUB were considered as a potential reference gene combination for normalizing the expression of target genes in different tissues. No suitable gene combination was obtained among these five candidate genes for normalizing the expression of target genes for developmental process of U. unicinctus. Our results provided a valuable support for quantifying gene expression using RT-qPCR in U. unicinctus.

Reference genes for quantitative PCR in the adipose tissue of mice with metabolic disease.

PubMed

Almeida-Oliveira, Fernanda; Leandro, João G B; Ausina, Priscila; Sola-Penna, Mauro; Majerowicz, David

2017-04-01

Obesity and diabetes are metabolic diseases and they are increasing in prevalence. The dynamics of gene expression associated with these diseases is fundamental to identifying genes involved in related biological processes. qPCR is a sensitive technique for mRNA quantification and the most commonly used method in gene-expression studies. However, the reliability of these results is directly influenced by data normalization. As reference genes are the major normalization method used, this work aims to identify reference genes for qPCR in adipose tissues of mice with type-I diabetes or obesity. We selected 12 genes that are commonly used as reference genes. The expression of these genes in the adipose tissues of mice was analyzed in the context of three different experimental protocols: 1) untreated animals; 2) high-fat-diet animals; and 3) streptozotocin-treated animals. Gene-expression stability was analyzed using four different algorithms. Our data indicate that TATA-binding protein is stably expressed across adipose tissues in control animals. This gene was also a useful reference when the brown adipose tissues of control and obese mice were analyzed. The mitochondrial ATP synthase F1 complex gene exhibits stable expression in subcutaneous and perigonadal adipose tissue from control and obese mice. Moreover, this gene is the best reference for qPCR normalization in adipose tissue from streptozotocin-treated animals. These results show that there is no perfect stable gene suited for use under all experimental conditions. In conclusion, the selection of appropriate genes is a prerequisite to ensure qPCR reliability and must be performed separately for different experimental protocols. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Gene expression related to oxidative stress in the heart of mice after intestinal ischemia

PubMed Central

Somaio Neto, Frederico; Ikejiri, Adauto Tsutomu; Bertoletto, Paulo Roberto; Chaves, José Carlos Bertoletto; Teruya, Roberto; Fagundes, Djalma José; Taha, Murched Omar

2014-01-01

Background Intestinal ischemia-reperfusion is a frequent clinical event associated to injury in distant organs, especially the heart. Objective To investigate the gene expression of oxidative stress and antioxidant defense in the heart of inbred mice subjected to intestinal ischemia and reperfusion (IR). Methods Twelve mice (C57BL / 6) were assigned to: IR Group (GIR) with 60 minutes of superior mesenteric artery occlusion followed by 60 minutes of reperfusion; Control Group (CG) which underwent anesthesia and laparotomy without IR procedure and was observed for 120 minutes. Intestine and heart samples were processed using the RT-qPCR / Reverse transcriptase-quantitative Polymerase Chain Reaction method for the gene expression of 84 genes related to oxidative stress and oxidative defense (Student's "t" test, p < 0.05). Results The intestinal tissue (GIR) was noted to have an up-regulation of 65 genes (74.71%) in comparison to normal tissue (CG), and 37 genes (44.04%) were hyper-expressed (greater than three times the threshold allowed by the algorithm). Regarding the remote effects of intestinal I/R in cardiac tissue an up-regulation of 28 genes (33.33%) was seen, but only eight genes (9.52%) were hyper-expressed three times above threshold. Four (7.14%) of these eight genes were expressed in both intestinal and cardiac tissues. Cardiomyocytes with smaller and pyknotic nuclei, rich in heterochromatin with rare nucleoli, indicating cardiac distress, were observed in the GIR. Conclusion Intestinal I/R caused a statistically significant over expression of 8 genes associated with oxidative stress in remote myocardial tissue. PMID:24346830

Golden Gate Assembly of CRISPR gRNA expression array for simultaneously targeting multiple genes.

PubMed

Vad-Nielsen, Johan; Lin, Lin; Bolund, Lars; Nielsen, Anders Lade; Luo, Yonglun

2016-11-01

The engineered CRISPR/Cas9 technology has developed as the most efficient and broadly used genome editing tool. However, simultaneously targeting multiple genes (or genomic loci) in the same individual cells using CRISPR/Cas9 remain one technical challenge. In this article, we have developed a Golden Gate Assembly method for the generation of CRISPR gRNA expression arrays, thus enabling simultaneous gene targeting. Using this method, the generation of CRISPR gRNA expression array can be accomplished in 2 weeks, and contains up to 30 gRNA expression cassettes. We demonstrated in the study that simultaneously targeting 10 genomic loci or simultaneously inhibition of multiple endogenous genes could be achieved using the multiplexed gRNA expression array vector in human cells. The complete set of plasmids is available through the non-profit plasmid repository Addgene.

[Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

PubMed

Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

2015-08-01

Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material base and molecular action mechanism of TCM.

Combining Evidence of Preferential Gene-Tissue Relationships from Multiple Sources

PubMed Central

Guo, Jing; Hammar, Mårten; Öberg, Lisa; Padmanabhuni, Shanmukha S.; Bjäreland, Marcus; Dalevi, Daniel

2013-01-01

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity. PMID:23950964

Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes

PubMed Central

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

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

NASA Astrophysics Data System (ADS)

Ehler, Martin; Rajapakse, Vinodh; Zeeberg, Barry; Brooks, Brian; Brown, Jacob; Czaja, Wojciech; Bonner, Robert F.

The gene networks underlying closure of the optic fissure during vertebrate eye development are poorly understood. We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates. This new methodology builds on the advantages of LCM to isolate pure phenotypic populations within complex tissues and allows improved ability to identify critical gene products expressed at lower copy number. The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks that specify mammalian organogenesis.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes.

PubMed

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

PubMed Central

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice. PMID:25374455

Refinement of light-responsive transcript lists using rice oligonucleotide arrays: evaluation of gene-redundancy.

PubMed

Jung, Ki-Hong; Dardick, Christopher; Bartley, Laura E; Cao, Peijian; Phetsom, Jirapa; Canlas, Patrick; Seo, Young-Su; Shultz, Michael; Ouyang, Shu; Yuan, Qiaoping; Frank, Bryan C; Ly, Eugene; Zheng, Li; Jia, Yi; Hsia, An-Ping; An, Kyungsook; Chou, Hui-Hsien; Rocke, David; Lee, Geun Cheol; Schnable, Patrick S; An, Gynheung; Buell, C Robin; Ronald, Pamela C

2008-10-06

Studies of gene function are often hampered by gene-redundancy, especially in organisms with large genomes such as rice (Oryza sativa). We present an approach for using transcriptomics data to focus functional studies and address redundancy. To this end, we have constructed and validated an inexpensive and publicly available rice oligonucleotide near-whole genome array, called the rice NSF45K array. We generated expression profiles for light- vs. dark-grown rice leaf tissue and validated the biological significance of the data by analyzing sources of variation and confirming expression trends with reverse transcription polymerase chain reaction. We examined trends in the data by evaluating enrichment of gene ontology terms at multiple false discovery rate thresholds. To compare data generated with the NSF45K array with published results, we developed publicly available, web-based tools (www.ricearray.org). The Oligo and EST Anatomy Viewer enables visualization of EST-based expression profiling data for all genes on the array. The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms. Finally, we incorporated gene expression and biochemical pathway data to reduce the number of candidate gene products putatively participating in the eight steps of the photorespiration pathway from 52 to 10, based on expression levels of putatively functionally redundant genes. We confirmed the efficacy of this method to cope with redundancy by correctly predicting participation in photorespiration of a gene with five paralogs. Applying these methods will accelerate rice functional genomics.

Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome

PubMed Central

Roymondal, Uttam; Das, Shibsankar; Sahoo, Satyabrata

2009-01-01

We present an expression measure of a gene, devised to predict the level of gene expression from relative codon bias (RCB). There are a number of measures currently in use that quantify codon usage in genes. Based on the hypothesis that gene expressivity and codon composition is strongly correlated, RCB has been defined to provide an intuitively meaningful measure of an extent of the codon preference in a gene. We outline a simple approach to assess the strength of RCB (RCBS) in genes as a guide to their likely expression levels and illustrate this with an analysis of Escherichia coli (E. coli) genome. Our efforts to quantitatively predict gene expression levels in E. coli met with a high level of success. Surprisingly, we observe a strong correlation between RCBS and protein length indicating natural selection in favour of the shorter genes to be expressed at higher level. The agreement of our result with high protein abundances, microarray data and radioactive data demonstrates that the genomic expression profile available in our method can be applied in a meaningful way to the study of cell physiology and also for more detailed studies of particular genes of interest. PMID:19131380

Simple method for assembly of CRISPR synergistic activation mediator gRNA expression array.

PubMed

Vad-Nielsen, Johan; Nielsen, Anders Lade; Luo, Yonglun

2018-05-20

When studying complex interconnected regulatory networks, effective methods for simultaneously manipulating multiple genes expression are paramount. Previously, we have developed a simple method for generation of an all-in-one CRISPR gRNA expression array. We here present a Golden Gate Assembly-based system of synergistic activation mediator (SAM) compatible CRISPR/dCas9 gRNA expression array for the simultaneous activation of multiple genes. Using this system, we demonstrated the simultaneous activation of the transcription factors, TWIST, SNAIL, SLUG, and ZEB1 a human breast cancer cell line. Copyright © 2018 Elsevier B.V. All rights reserved.

Whole Genome Gene Expression Meta-Analysis of Inflammatory Bowel Disease Colon Mucosa Demonstrates Lack of Major Differences between Crohn's Disease and Ulcerative Colitis

PubMed Central

Østvik, Ann E.; Drozdov, Ignat; Gustafsson, Bjørn I.; Kidd, Mark; Beisvag, Vidar; Torp, Sverre H.; Waldum, Helge L.; Martinsen, Tom Christian; Damås, Jan Kristian; Espevik, Terje; Sandvik, Arne K.

2013-01-01

Background In inflammatory bowel disease (IBD), genetic susceptibility together with environmental factors disturbs gut homeostasis producing chronic inflammation. The two main IBD subtypes are Ulcerative colitis (UC) and Crohn’s disease (CD). We present the to-date largest microarray gene expression study on IBD encompassing both inflamed and un-inflamed colonic tissue. A meta-analysis including all available, comparable data was used to explore important aspects of IBD inflammation, thereby validating consistent gene expression patterns. Methods Colon pinch biopsies from IBD patients were analysed using Illumina whole genome gene expression technology. Differential expression (DE) was identified using LIMMA linear model in the R statistical computing environment. Results were enriched for gene ontology (GO) categories. Sets of genes encoding antimicrobial proteins (AMP) and proteins involved in T helper (Th) cell differentiation were used in the interpretation of the results. All available data sets were analysed using the same methods, and results were compared on a global and focused level as t-scores. Results Gene expression in inflamed mucosa from UC and CD are remarkably similar. The meta-analysis confirmed this. The patterns of AMP and Th cell-related gene expression were also very similar, except for IL23A which was consistently higher expressed in UC than in CD. Un-inflamed tissue from patients demonstrated minimal differences from healthy controls. Conclusions There is no difference in the Th subgroup involvement between UC and CD. Th1/Th17 related expression, with little Th2 differentiation, dominated both diseases. The different IL23A expression between UC and CD suggests an IBD subtype specific role. AMPs, previously little studied, are strongly overexpressed in IBD. The presented meta-analysis provides a sound background for further research on IBD pathobiology. PMID:23468882

Transcriptional network inference from functional similarity and expression data: a global supervised approach.

PubMed

Ambroise, Jérôme; Robert, Annie; Macq, Benoit; Gala, Jean-Luc

2012-01-06

An important challenge in system biology is the inference of biological networks from postgenomic data. Among these biological networks, a gene transcriptional regulatory network focuses on interactions existing between transcription factors (TFs) and and their corresponding target genes. A large number of reverse engineering algorithms were proposed to infer such networks from gene expression profiles, but most current methods have relatively low predictive performances. In this paper, we introduce the novel TNIFSED method (Transcriptional Network Inference from Functional Similarity and Expression Data), that infers a transcriptional network from the integration of correlations and partial correlations of gene expression profiles and gene functional similarities through a supervised classifier. In the current work, TNIFSED was applied to predict the transcriptional network in Escherichia coli and in Saccharomyces cerevisiae, using datasets of 445 and 170 affymetrix arrays, respectively. Using the area under the curve of the receiver operating characteristics and the F-measure as indicators, we showed the predictive performance of TNIFSED to be better than unsupervised state-of-the-art methods. TNIFSED performed slightly worse than the supervised SIRENE algorithm for the target genes identification of the TF having a wide range of yet identified target genes but better for TF having only few identified target genes. Our results indicate that TNIFSED is complementary to the SIRENE algorithm, and particularly suitable to discover target genes of "orphan" TFs.

Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks.

PubMed

Hung, Fei-Hung; Chiu, Hung-Wen

2015-01-01

Gene expression profiles differ in different diseases. Even if diseases are at the same stage, such diseases exhibit different gene expressions, not to mention the different subtypes at a single lesion site. Distinguishing different disease subtypes at a single lesion site is difficult. In early cases, subtypes were initially distinguished by doctors. Subsequently, further differences were found through pathological experiments. For example, a brain tumor can be classified according to its origin, its cell-type origin, or the tumor site. Because of the advancements in bioinformatics and the techniques for accumulating gene expressions, researchers can use gene expression data to classify disease subtypes. Because the operation of a biopathway is closely related to the disease mechanism, the application of gene expression profiles for clustering disease subtypes is insufficient. In this study, we collected gene expression data of healthy and four myelodysplastic syndrome subtypes and applied a method that integrated protein-protein interaction and gene expression data to identify different patterns of disease subtypes. We hope it is efficient for the classification of disease subtypes in adventure.

Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments.

PubMed

Duan, Fenghai; Xu, Ye

2017-01-01

To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. In total, 1640 probe sets (genes) were reliably identified to have turning points along with the age at diagnosis in their expression profiling, of which 927 expressed lower after turning points and 713 expressed higher after the turning points. K-means clustered them into 3 groups with turning points centering at 54, 62.5, and 72, respectively. The pathway analysis showed that the identified genes were actively involved in various cancer-related functions or networks. In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis.

Delivery of Na/I symporter gene into skeletal muscle using nanobubbles and ultrasound: visualization of gene expression by PET.

PubMed

Watanabe, Yukiko; Horie, Sachiko; Funaki, Yoshihito; Kikuchi, Youhei; Yamazaki, Hiromichi; Ishii, Keizo; Mori, Shiro; Vassaux, Georges; Kodama, Tetsuya

2010-06-01

The development of nonviral gene delivery systems is essential in gene therapy, and the use of a minimally invasive imaging methodology can provide important clinical endpoints. In the current study, we present a new methodology for gene therapy-a delivery system using nanobubbles and ultrasound as a nonviral gene delivery method. We assessed whether the gene transfer allowed by this methodology was detectable by PET and bioluminescence imaging. Two kinds of reported vectors (luciferase and human Na/I symporter [hNIS]) were transfected or cotransfected into the skeletal muscles of normal mice (BALB/c) using the ultrasound-nanobubbles method. The kinetics of luciferase gene expression were analyzed in vivo using bioluminescence imaging. At the peak of gene transfer, PET of hNIS expression was performed using our recently developed PET scanner, after (124)I injection. The imaging data were confirmed using reverse-transcriptase polymerase chain reaction amplification, biodistribution, and a blocking study. The imaging potential of the 2 methodologies was evaluated in 2 mouse models of human pathology (McH/lpr-RA1 mice showing vascular disease and C57BL/10-mdx Jic mice showing muscular dystrophy). Peak luciferase gene activity was observed in the skeletal muscle 4 d after transfection. On day 2 after hNIS and luciferase cotransfection, the expression of these genes was confirmed by reverse-transcriptase polymerase chain reaction on a muscle biopsy. PET of the hNIS gene, biodistribution, the blocking study, and autoradiography were performed on day 4 after transfection, and it was indicated that hNIS expression was restricted to the site of plasmid administration (skeletal muscle). Similar localized PET and (124)I accumulation were successfully obtained in the disease-model mice. The hNIS gene was delivered into the skeletal muscle of healthy and disease-model mice by the ultrasound-nanobubbles method, and gene expression was successfully visualized with PET. The combination of ultrasound-nanobubble gene transfer and PET may be applied to gene therapy clinical protocols.

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis.

PubMed

Asgari, Yazdan; Khosravi, Pegah; Zabihinpour, Zahra; Habibi, Mahnaz

2018-02-19

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

PubMed

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

Selection and Validation of Reference Genes for qRT-PCR Expression Analysis of Candidate Genes Involved in Olfactory Communication in the Butterfly Bicyclus anynana

PubMed Central

Arun, Alok; Baumlé, Véronique; Amelot, Gaël; Nieberding, Caroline M.

2015-01-01

Real-time quantitative reverse transcription PCR (qRT-PCR) is a technique widely used to quantify the transcriptional expression level of candidate genes. qRT-PCR requires the selection of one or several suitable reference genes, whose expression profiles remain stable across conditions, to normalize the qRT-PCR expression profiles of candidate genes. Although several butterfly species (Lepidoptera) have become important models in molecular evolutionary ecology, so far no study aimed at identifying reference genes for accurate data normalization for any butterfly is available. The African bush brown butterfly Bicyclus anynana has drawn considerable attention owing to its suitability as a model for evolutionary ecology, and we here provide a maiden extensive study to identify suitable reference gene in this species. We monitored the expression profile of twelve reference genes: eEF-1α, FK506, UBQL40, RpS8, RpS18, HSP, GAPDH, VATPase, ACT3, TBP, eIF2 and G6PD. We tested the stability of their expression profiles in three different tissues (wings, brains, antennae), two developmental stages (pupal and adult) and two sexes (male and female), all of which were subjected to two food treatments (food stress and control feeding ad libitum). The expression stability and ranking of twelve reference genes was assessed using two algorithm-based methods, NormFinder and geNorm. Both methods identified RpS8 as the best suitable reference gene for expression data normalization. We also showed that the use of two reference genes is sufficient to effectively normalize the qRT-PCR data under varying tissues and experimental conditions that we used in B. anynana. Finally, we tested the effect of choosing reference genes with different stability on the normalization of the transcript abundance of a candidate gene involved in olfactory communication in B. anynana, the Fatty Acyl Reductase 2, and we confirmed that using an unstable reference gene can drastically alter the expression profile of the target candidate genes. PMID:25793735

Selection and validation of reference genes for qRT-PCR expression analysis of candidate genes involved in olfactory communication in the butterfly Bicyclus anynana.

PubMed

Arun, Alok; Baumlé, Véronique; Amelot, Gaël; Nieberding, Caroline M

2015-01-01

Real-time quantitative reverse transcription PCR (qRT-PCR) is a technique widely used to quantify the transcriptional expression level of candidate genes. qRT-PCR requires the selection of one or several suitable reference genes, whose expression profiles remain stable across conditions, to normalize the qRT-PCR expression profiles of candidate genes. Although several butterfly species (Lepidoptera) have become important models in molecular evolutionary ecology, so far no study aimed at identifying reference genes for accurate data normalization for any butterfly is available. The African bush brown butterfly Bicyclus anynana has drawn considerable attention owing to its suitability as a model for evolutionary ecology, and we here provide a maiden extensive study to identify suitable reference gene in this species. We monitored the expression profile of twelve reference genes: eEF-1α, FK506, UBQL40, RpS8, RpS18, HSP, GAPDH, VATPase, ACT3, TBP, eIF2 and G6PD. We tested the stability of their expression profiles in three different tissues (wings, brains, antennae), two developmental stages (pupal and adult) and two sexes (male and female), all of which were subjected to two food treatments (food stress and control feeding ad libitum). The expression stability and ranking of twelve reference genes was assessed using two algorithm-based methods, NormFinder and geNorm. Both methods identified RpS8 as the best suitable reference gene for expression data normalization. We also showed that the use of two reference genes is sufficient to effectively normalize the qRT-PCR data under varying tissues and experimental conditions that we used in B. anynana. Finally, we tested the effect of choosing reference genes with different stability on the normalization of the transcript abundance of a candidate gene involved in olfactory communication in B. anynana, the Fatty Acyl Reductase 2, and we confirmed that using an unstable reference gene can drastically alter the expression profile of the target candidate genes.

Escherichia coli DNA contamination in AmpliTaq Gold polymerase interferes with TaqMan analysis of lacZ.

PubMed

Koponen, Jonna K; Turunen, Anna-Mari; Ylä-Herttuala, Seppo

2002-03-01

Real-time PCR is a powerful method for the quantification of gene expression in biological samples. This method uses TaqMan chemistry based on the 5' -exonuclease activity of the AmpliTaq Gold DNA polymerase which releases fluorescence from hybridized probes during synthesis of each new PCR product. Many gene therapy studies use lacZ, encoding Escherichia coli beta-galactosidase, as a marker gene. Our results demonstrate that E. coli DNA contamination in AmpliTaq Gold polymerase interferes with TaqMan analysis of lacZ gene expression and decreases sensitivity of the method below the level required for biodistribution and long-term gene expression studies. In biodistribution analyses the contamination can lead to false-negative results by masking low-level lacZ expression in target and ectopic tissues, and false-positive results if sufficient controls are not used. We conclude that, to get reliable TaqMan results with lacZ, adequate controls should be included in each run to rule out contamination from AmpliTaq Gold polymerase.

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.

PubMed

Zhang, Ao; Tian, Suyan

2018-05-01

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance.

PubMed

Emad, Amin; Cairns, Junmei; Kalari, Krishna R; Wang, Liewei; Sinha, Saurabh

2017-08-11

Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

PubMed Central

Kang, Eun Yong; Martin, Lisa J.; Mangul, Serghei; Isvilanonda, Warin; Zou, Jennifer; Ben-David, Eyal; Han, Buhm; Lusis, Aldons J.; Shifman, Sagiv; Eskin, Eleazar

2016-01-01

The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cis-acting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases. PMID:27765809

Endogenous Reference Genes for Gene Expression Studies on Bicuspid Aortic Valve Associated Aortopathy in Humans.

PubMed

Harrison, Oliver J; Moorjani, Narain; Torrens, Christopher; Ohri, Sunil K; Cagampang, Felino R

2016-01-01

Bicuspid aortic valve (BAV) disease is the most common congenital cardiac abnormality and predisposes patients to life-threatening aortic complications including aortic aneurysm. Quantitative real-time reverse transcription PCR (qRT-PCR) is one of the most commonly used methods to investigate underlying molecular mechanisms involved in aortopathy. The accuracy of the gene expression data is dependent on normalization by appropriate housekeeping (HK) genes, whose expression should remain constant regardless of aortic valve morphology, aortic diameter and other factors associated with aortopathy. Here, we identified an appropriate set of HK genes to be used as endogenous reference for quantifying gene expression in ascending aortic tissue using a spin column-based RNA extraction method. Ascending aortic biopsies were collected intra-operatively from patients undergoing aortic valve and/or ascending aortic surgery. These patients had BAV or tricuspid aortic valve (TAV), and the aortas were either dilated (≥4.5cm) or undilated. The cohort had an even distribution of gender, valve disease and hypertension. The expression stability of 12 reference genes were investigated (ATP5B, ACTB, B2M, CYC1, EIF4A2, GAPDH, SDHA, RPL13A, TOP1, UBC, YWHAZ, and 18S) using geNorm software. The most stable HK genes were found to be GAPDH, UBC and ACTB. Both GAPDH and UBC demonstrated relative stability regardless of valve morphology, aortic diameter, gender and age. The expression of B2M and SDHA were found to be the least stable HK genes. We propose the use of GAPDH, UBC and ACTB as reference genes for gene expression studies of BAV aortopathy using ascending aortic tissue.

Quantification of multiple gene expression in individual cells.

PubMed

Peixoto, António; Monteiro, Marta; Rocha, Benedita; Veiga-Fernandes, Henrique

2004-10-01

Quantitative gene expression analysis aims to define the gene expression patterns determining cell behavior. So far, these assessments can only be performed at the population level. Therefore, they determine the average gene expression within a population, overlooking possible cell-to-cell heterogeneity that could lead to different cell behaviors/cell fates. Understanding individual cell behavior requires multiple gene expression analyses of single cells, and may be fundamental for the understanding of all types of biological events and/or differentiation processes. We here describe a new reverse transcription-polymerase chain reaction (RT-PCR) approach allowing the simultaneous quantification of the expression of 20 genes in the same single cell. This method has broad application, in different species and any type of gene combination. RT efficiency is evaluated. Uniform and maximized amplification conditions for all genes are provided. Abundance relationships are maintained, allowing the precise quantification of the absolute number of mRNA molecules per cell, ranging from 2 to 1.28 x 10(9) for each individual gene. We evaluated the impact of this approach on functional genetic read-outs by studying an apparently homogeneous population (monoclonal T cells recovered 4 d after antigen stimulation), using either this method or conventional real-time RT-PCR. Single-cell studies revealed considerable cell-to-cell variation: All T cells did not express all individual genes. Gene coexpression patterns were very heterogeneous. mRNA copy numbers varied between different transcripts and in different cells. As a consequence, this single-cell assay introduces new and fundamental information regarding functional genomic read-outs. By comparison, we also show that conventional quantitative assays determining population averages supply insufficient information, and may even be highly misleading.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes.

PubMed

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-08-29

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes

PubMed Central

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-01-01

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/. PMID:28850115

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

PubMed Central

2009-01-01

Background A central task in contemporary biosciences is the identification of biological processes showing response in genome-wide differential gene expression experiments. Two types of analysis are common. Either, one generates an ordered list based on the differential expression values of the probed genes and examines the tail areas of the list for over-representation of various functional classes. Alternatively, one monitors the average differential expression level of genes belonging to a given functional class. So far these two types of method have not been combined. Results We introduce a scoring function, Gene Set Z-score (GSZ), for the analysis of functional class over-representation that combines two previous analysis methods. GSZ encompasses popular functions such as correlation, hypergeometric test, Max-Mean and Random Sets as limiting cases. GSZ is stable against changes in class size as well as across different positions of the analysed gene list in tests with randomized data. GSZ shows the best overall performance in a detailed comparison to popular functions using artificial data. Likewise, GSZ stands out in a cross-validation of methods using split real data. A comparison of empirical p-values further shows a strong difference in favour of GSZ, which clearly reports better p-values for top classes than the other methods. Furthermore, GSZ detects relevant biological themes that are missed by the other methods. These observations also hold when comparing GSZ with popular program packages. Conclusion GSZ and improved versions of earlier methods are a useful contribution to the analysis of differential gene expression. The methods and supplementary material are available from the website http://ekhidna.biocenter.helsinki.fi/users/petri/public/GSZ/GSZscore.html. PMID:19775443

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression.

PubMed

Lemieux, Sébastien

2006-08-25

The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.

The opportunities and challenges of large-scale molecular approaches to songbird neurobiology

PubMed Central

Mello, C.V.; Clayton, D.F.

2014-01-01

High-through put methods for analyzing genome structure and function are having a large impact in song-bird neurobiology. Methods include genome sequencing and annotation, comparative genomics, DNA microarrays and transcriptomics, and the development of a brain atlas of gene expression. Key emerging findings include the identification of complex transcriptional programs active during singing, the robust brain expression of non-coding RNAs, evidence of profound variations in gene expression across brain regions, and the identification of molecular specializations within song production and learning circuits. Current challenges include the statistical analysis of large datasets, effective genome curations, the efficient localization of gene expression changes to specific neuronal circuits and cells, and the dissection of behavioral and environmental factors that influence brain gene expression. The field requires efficient methods for comparisons with organisms like chicken, which offer important anatomical, functional and behavioral contrasts. As sequencing costs plummet, opportunities emerge for comparative approaches that may help reveal evolutionary transitions contributing to vocal learning, social behavior and other properties that make songbirds such compelling research subjects. PMID:25280907

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

PubMed

Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-03-09

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. Copyright © 2015 Brown et al.

Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

PubMed Central

Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-01-01

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 “pathway phenotypes” that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38×10−5). These phenotypes are more heritable (h2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. PMID:25758824

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

PubMed

Mansourian, Robert; Mutch, David M; Antille, Nicolas; Aubert, Jerome; Fogel, Paul; Le Goff, Jean-Marc; Moulin, Julie; Petrov, Anton; Rytz, Andreas; Voegel, Johannes J; Roberts, Matthew-Alan

2004-11-01

Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. The GEA code for R software is freely available upon request to authors.

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.

Analysis of gene expression levels in individual bacterial cells without image segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kwak, In Hae; Son, Minjun; Hagen, Stephen J., E-mail: sjhagen@ufl.edu

2012-05-11

Highlights: Black-Right-Pointing-Pointer We present a method for extracting gene expression data from images of bacterial cells. Black-Right-Pointing-Pointer The method does not employ cell segmentation and does not require high magnification. Black-Right-Pointing-Pointer Fluorescence and phase contrast images of the cells are correlated through the physics of phase contrast. Black-Right-Pointing-Pointer We demonstrate the method by characterizing noisy expression of comX in Streptococcus mutans. -- Abstract: Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on amore » segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly.« less

A Single-Wing Removal Method to Assess Correspondence Between Gene Expression and Phenotype in Butterflies: The Case of Distal-less.

PubMed

Adhikari, Kiran; Otaki, Joji M

2016-02-01

It is often desirable but difficult to retrieve information on the mature phenotype of an immature tissue sample that has been subjected to gene expression analysis. This problem cannot be ignored when individual variation within a species is large. To circumvent this problem in the butterfly wing system, we developed a new surgical method for removing a single forewing from a pupa using Junonia orithya; the operated pupa was left to develop to an adult without eclosion. The removed right forewing was subjected to gene expression analysis, whereas the non-removed left forewing was examined for color patterns. As a test case, we focused on Distal-less (Dll), which likely plays an active role in inducing elemental patterns, including eyespots. The Dll expression level in forewings was paired with eyespot size data from the same individual. One third of the operated pupae survived and developed wing color patterns. Dll expression levels were significantly higher in males than in females, although male eyespots were smaller in size than female eyespots. Eyespot size data showed weak but significant correlations with the Dll expression level in females. These results demonstrate that a single-wing removal method was successfully applied to the butterfly wing system and suggest the weak and non-exclusive contribution of Dll to eyespot size determination in this butterfly. Our novel methodology for establishing correspondence between gene expression and phenotype can be applied to other candidate genes for color pattern development in butterflies. Conceptually similar methods may also be applicable in other developmental systems.

Reference genes for measuring mRNA expression.

PubMed

Dundas, Jitesh; Ling, Maurice

2012-12-01

The aim of this review is to find answers to some of the questions surrounding reference genes and their reliability for quantitative experiments. Reference genes are assumed to be at a constant expression level, over a range of conditions such as temperature. These genes, such as GADPH and beta-actin, are used extensively for gene expression studies using techniques like quantitative PCR. There have been several studies carried out on identifying reference genes. However, a lot of evidence indicates issues to the general suitability of these genes. Recent studies had shown that different factors, including the environment and methods, play an important role in changing the expression levels of the reference genes. Thus, we conclude that there is no reference gene that can deemed suitable for all the experimental conditions. In addition, we believe that every experiment will require the scientific evaluation and selection of the best candidate gene for use as a reference gene to obtain reliable scientific results.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Biasogram: Visualization of Confounding Technical Bias in Gene Expression Data

PubMed Central

Krzystanek, Marcin; Szallasi, Zoltan; Eklund, Aron C.

2013-01-01

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results. PMID:23613961

A Dual-Color Reporter Assay of Cohesin-Mediated Gene Regulation in Budding Yeast Meiosis.

PubMed

Fan, Jinbo; Jin, Hui; Yu, Hong-Guo

2017-01-01

In this chapter, we describe a quantitative fluorescence-based assay of gene expression using the ratio of the reporter green fluorescence protein (GFP) to the internal red fluorescence protein (RFP) control. With this dual-color heterologous reporter assay, we have revealed cohesin-regulated genes and discovered a cis-acting DNA element, the Ty1-LTR, which interacts with cohesin and regulates gene expression during yeast meiosis. The method described here provides an effective cytological approach for quantitative analysis of global gene expression in budding yeast meiosis.

Oligo-dT anchored cDNA-SCoT: a novel differential display method for analyzing differential gene expression in response to several stress treatments in mango (Mangifera indica L.).

PubMed

Luo, Cong; He, Xin-Hua; Hu, Ying; Yu, Hai-xia; Ou, Shi-Jin; Fang, Zhong-Bin

2014-09-15

Differential display is a powerful technique for analyzing differences in gene expression. Oligo-dT cDNAstart codon targeted marker (cDNA-SCoT) technique is a novel, simple, cheap, rapid, and efficient method for differential gene expression research. In the present study, the oligo-dT anchored cDNA-SCoT technique was exploited to identify differentially expressed genes during several stress treatments in mango. A total of 37 primers combined with oligo-dT anchor primers 3side amplified approximately 150 fragments of 150 bp to 1500 bp in length. Up to 100 fragments were differentially expressed among the stress treatments and control samples, among which 92 were obtained and sequenced. Out of the 92 transcript derived fragments (TDFs), 70% were highly homologous to known genes, and 30% encoded unclassified proteins with unknown functions. The expression pattern of nine genes with known functions involved in several abiotic stresses in other species was confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) under cold (4 °C), salinity (NaCl), polyethylene glycol (PEG, MW 6000), and heavy metal treatments in leaves and stems at different time points (0, 24, 48, and 72 h). The expression patterns of the genes (TDF4, TDF7, TDF23, TDF45, TDF49, TDF50, TDF57, TDF91 and TDF92) that had direct or indirect relationships with cold, salinity, drought and heavy metal stress response were analyzed through qRT-PCR. The possible roles of these genes are discussed. This study suggests that the oligo-dT anchored cDNA-SCoT differential display method is a useful tool to serve as an initial step for characterizing transcriptional changes induced by abiotic stresses and provide gene information for further study and application in genetic improvement and breeding in mango. Copyright © 2014 Elsevier B.V. All rights reserved.

Identifying and engineering promoters for high level and sustainable therapeutic recombinant protein production in cultured mammalian cells.

PubMed

Ho, Steven C L; Yang, Yuansheng

2014-08-01

Promoters are essential on plasmid vectors to initiate transcription of the transgenes when generating therapeutic recombinant proteins expressing mammalian cell lines. High and sustained levels of gene expression are desired during therapeutic protein production while gene expression is useful for cell engineering. As many finely controlled promoters exhibit cell and product specificity, new promoters need to be identified, optimized and carefully evaluated before use. Suitable promoters can be identified using techniques ranging from simple molecular biology methods to modern high-throughput omics screenings. Promoter engineering is often required after identification to either obtain high and sustained expression or to provide a wider range of gene expression. This review discusses some of the available methods to identify and engineer promoters for therapeutic recombinant protein expression in mammalian cells.

Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies.

PubMed

Martini, Paolo; Risso, Davide; Sales, Gabriele; Romualdi, Chiara; Lanfranchi, Gerolamo; Cagnin, Stefano

2011-04-11

In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes. In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets. We describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an a priori step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach). In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies. STEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

Finding gene clusters for a replicated time course study

PubMed Central

2014-01-01

Background Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. Findings In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. Conclusions The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism. PMID:24460656

Analysis of multiplex gene expression maps obtained by voxelation.

PubMed

An, Li; Xie, Hongbo; Chin, Mark H; Obradovic, Zoran; Smith, Desmond J; Megalooikonomou, Vasileios

2009-04-29

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum. The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

IAOseq: inferring abundance of overlapping genes using RNA-seq data.

PubMed

Sun, Hong; Yang, Shuang; Tun, Liangliang; Li, Yixue

2015-01-01

Overlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data. We developed a new tool (IAOseq) that is based on reads distributions along the transcribed regions to identify the expression levels of overlapping genes from standard RNA-seq data. Compared with five commonly used quantification methods, IAOseq showed better performance in the estimation accuracy of overlapping transcription levels. For the same strand overlapping transcription, currently existing high-throughput methods are rarely available to distinguish which strand was present in the original mRNA template. The IAOseq results showed that the commonly used methods gave an average of 1.6 fold overestimation of the expression levels of same strand overlapping genes. This work provides a useful tool for mining overlapping transcription levels from standard RNA-seq libraries. IAOseq could be used to help us understand the complex regulatory mechanism mediated by overlapping transcripts. IAOseq is freely available at http://lifecenter.sgst.cn/main/en/IAO_seq.jsp.

Analysis of high-throughput biological data using their rank values.

PubMed

Dembélé, Doulaye

2018-01-01

High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Fanconi anemia genes are highly expressed in primitive CD34+ hematopoietic cells

PubMed Central

Aubé, Michel; Lafrance, Matthieu; Brodeur, Isabelle; Delisle, Marie-Chantal; Carreau, Madeleine

2003-01-01

Background Fanconi anemia (FA) is a complex recessive genetic disease characterized by progressive bone marrow failure (BM) and a predisposition to cancer. We have previously shown using the Fancc mouse model that the progressive BM failure results from a hematopoietic stem cell defect suggesting that function of the FA genes may reside in primitive hematopoietic stem cells. Methods Since genes involved in stem cell differentiation and/or maintenance are usually regulated at the transcription level, we used a semiquantitative RT-PCR method to evaluate FA gene transcript levels in purified hematopoietic stem cells. Results We show that most FA genes are highly expressed in primitive CD34-positive and negative cells compared to lower levels in more differentiated cells. However, in CD34- stem cells the Fancc gene was found to be expressed at low levels while Fancg was undetectable in this population. Furthermore, Fancg expression is significantly decreased in Fancc -/- stem cells as compared to wild-type cells while the cancer susceptibility genes Brca1 and Fancd1/Brac2 are upregulated in Fancc-/- hematopoietic cells. Conclusions These results suggest that FA genes are regulated at the mRNA level, that increased Fancc expression in LTS-CD34+ cells correlates with a role at the CD34+ differentiation stage and that lack of Fancc affects the expression of other FA gene, more specifically Fancg and Fancd1/Brca2, through an unknown mechanism. PMID:12809565

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets.

PubMed

Faruki, Hawazin; Mayhew, Gregory M; Fan, Cheng; Wilkerson, Matthew D; Parker, Scott; Kam-Morgan, Lauren; Eisenberg, Marcia; Horten, Bruce; Hayes, D Neil; Perou, Charles M; Lai-Goldman, Myla

2016-06-01

Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen.

PubMed

Huang, Ruili; Wallqvist, Anders; Covell, David G

2006-03-01

We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.

Genome-Wide Gene Expression in relation to Age in Large Laboratory Cohorts of Drosophila melanogaster

PubMed Central

Carlson, Kimberly A.; Gardner, Kylee; Pashaj, Anjeza; Carlson, Darby J.; Yu, Fang; Eudy, James D.; Zhang, Chi; Harshman, Lawrence G.

2015-01-01

Aging is a complex process characterized by a steady decline in an organism's ability to perform life-sustaining tasks. In the present study, two cages of approximately 12,000 mated Drosophila melanogaster females were used as a source of RNA from individuals sampled frequently as a function of age. A linear model for microarray data method was used for the microarray analysis to adjust for the box effect; it identified 1,581 candidate aging genes. Cluster analyses using a self-organizing map algorithm on the 1,581 significant genes identified gene expression patterns across different ages. Genes involved in immune system function and regulation, chorion assembly and function, and metabolism were all significantly differentially expressed as a function of age. The temporal pattern of data indicated that gene expression related to aging is affected relatively early in life span. In addition, the temporal variance in gene expression in immune function genes was compared to a random set of genes. There was an increase in the variance of gene expression within each cohort, which was not observed in the set of random genes. This observation is compatible with the hypothesis that D. melanogaster immune function genes lose control of gene expression as flies age. PMID:26090231

Reprogramming Methods Do Not Affect Gene Expression Profile of Human Induced Pluripotent Stem Cells.

PubMed

Trevisan, Marta; Desole, Giovanna; Costanzi, Giulia; Lavezzo, Enrico; Palù, Giorgio; Barzon, Luisa

2017-01-20

Induced pluripotent stem cells (iPSCs) are pluripotent cells derived from adult somatic cells. After the pioneering work by Yamanaka, who first generated iPSCs by retroviral transduction of four reprogramming factors, several alternative methods to obtain iPSCs have been developed in order to increase the yield and safety of the process. However, the question remains open on whether the different reprogramming methods can influence the pluripotency features of the derived lines. In this study, three different strategies, based on retroviral vectors, episomal vectors, and Sendai virus vectors, were applied to derive iPSCs from human fibroblasts. The reprogramming efficiency of the methods based on episomal and Sendai virus vectors was higher than that of the retroviral vector-based approach. All human iPSC clones derived with the different methods showed the typical features of pluripotent stem cells, including the expression of alkaline phosphatase and stemness maker genes, and could give rise to the three germ layer derivatives upon embryoid bodies assay. Microarray analysis confirmed the presence of typical stem cell gene expression profiles in all iPSC clones and did not identify any significant difference among reprogramming methods. In conclusion, the use of different reprogramming methods is equivalent and does not affect gene expression profile of the derived human iPSCs.

“Real time” genetic manipulation: a new tool for ecological field studies

PubMed Central

Schäfer, Martin; Brütting, Christoph; Gase, Klaus; Reichelt, Michael; Baldwin, Ian; Meldau, Stefan

2014-01-01

Summary Field experiments with transgenic plants often reveal the functional significance of genetic traits important for plant performance in their natural environments. Until now, only constitutive overexpression, ectopic expression and gene silencing methods have been used to analyze gene-related phenotypes in natural habitats. These methods do not allow sufficient control over gene expression to study ecological interactions in real-time, genetic traits playing essential roles in development, or dose-dependent effects. We applied the sensitive dexamethasone (DEX)-inducible pOp6/LhGR expression system to the ecological model plant Nicotiana attenuata and established a lanolin-based DEX application method to facilitate ectopic gene expression and RNAi mediated gene silencing in the field and under challenging conditions (e.g. high temperature, wind and UV radiation). Fully established field-grown plants were used to silence phytoene desaturase and thereby cause photobleaching only in specific plant sectors, and to activate expression of the cytokinin (CK) biosynthesis gene isopentenyl transferase (ipt). We used ipt expression to analyze the role of CK’s in both the glasshouse and field to understand resistance to the native herbivore Tupiocoris notatus, which attack plants at small spatial scales. By spatially restricting ipt expression and elevating CK levels in single leaves, T. notatus damage increased, demonstrating CK’s role in this plant-herbivore interaction at a small scale. As the arena of most ecological interactions is highly constrained in time and space, these tools will advance the genetic analysis of dynamic traits that matter for plant performance in nature. PMID:23906159

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis.

PubMed

Mallik, Saurav; Zhao, Zhongming

2017-12-28

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.

PubMed

Li, Jiangeng; Su, Lei; Pang, Zenan

2015-12-01

Feature selection techniques have been widely applied to tumor gene expression data analysis in recent years. A filter feature selection method named marginal Fisher analysis score (MFA score) which is based on graph embedding has been proposed, and it has been widely used mainly because it is superior to Fisher score. Considering the heavy redundancy in gene expression data, we proposed a new filter feature selection technique in this paper. It is named MFA score+ and is based on MFA score and redundancy excluding. We applied it to an artificial dataset and eight tumor gene expression datasets to select important features and then used support vector machine as the classifier to classify the samples. Compared with MFA score, t test and Fisher score, it achieved higher classification accuracy.

Identification of cis-elements conferring high levels of gene expression in non-green plastids.

PubMed

Zhang, Jiang; Ruf, Stephanie; Hasse, Claudia; Childs, Liam; Scharff, Lars B; Bock, Ralph

2012-10-01

Although our knowledge about the mechanisms of gene expression in chloroplasts has increased substantially over the past decades, next to nothing is known about the signals and factors that govern expression of the plastid genome in non-green tissues. Here we report the development of a quantitative method suitable for determining the activity of cis-acting elements for gene expression in non-green plastids. The in vivo assay is based on stable transformation of the plastid genome and the discovery that root length upon seedling growth in the presence of the plastid translational inhibitor kanamycin is directly proportional to the expression strength of the resistance gene nptII in transgenic tobacco plastids. By testing various combinations of promoters and translation initiation signals, we have used this experimental system to identify cis-elements that are highly active in non-green plastids. Surprisingly, heterologous expression elements from maize plastids were significantly more efficient in conferring high expression levels in root plastids than homologous expression elements from tobacco. Our work has established a quantitative method for characterization of gene expression in non-green plastid types, and has led to identification of cis-elements for efficient plastid transgene expression in non-green tissues, which are valuable tools for future transplastomic studies in basic and applied research. © 2012 The Authors. The Plant Journal © 2012 Blackwell Publishing Ltd.

Genome-wide profiling identifies a subset of methamphetamine (METH)-induced genes associated with METH-induced increased H4K5Ac binding in the rat striatum

PubMed Central

2013-01-01

Background METH is an illicit drug of abuse that influences gene expression in the rat striatum. Histone modifications regulate gene transcription. Methods We therefore used microarray analysis and genome-scale approaches to examine potential relationships between the effects of METH on gene expression and on DNA binding of histone H4 acetylated at lysine 4 (H4K5Ac) in the rat dorsal striatum of METH-naïve and METH-pretreated rats. Results Acute and chronic METH administration caused differential changes in striatal gene expression. METH also increased H4K5Ac binding around the transcriptional start sites (TSSs) of genes in the rat striatum. In order to relate gene expression to histone acetylation, we binned genes of similar expression into groups of 100 genes and proceeded to relate gene expression to H4K5Ac binding. We found a positive correlation between gene expression and H4K5Ac binding in the striatum of control rats. Similar correlations were observed in METH-treated rats. Genes that showed acute METH-induced increased expression in saline-pretreated rats also showed METH-induced increased H4K5Ac binding. The acute METH injection caused similar increases in H4K5Ac binding in METH-pretreated rats, without affecting gene expression to the same degree. Finally, genes that showed METH-induced decreased expression exhibited either decreases or no changes in H4K5Ac binding. Conclusion Acute METH injections caused increased gene expression of genes that showed increased H4K5Ac binding near their transcription start sites. PMID:23937714

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.

PubMed

Yang, Lingjian; Ainali, Chrysanthi; Tsoka, Sophia; Papageorgiou, Lazaros G

2014-12-05

Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes

PubMed Central

VanderKraats, Nathan D.; Hiken, Jeffrey F.; Decker, Keith F.; Edwards, John R.

2013-01-01

Methylation of the CpG-rich region (CpG island) overlapping a gene’s promoter is a generally accepted mechanism for silencing expression. While recent technological advances have enabled measurement of DNA methylation and expression changes genome-wide, only modest correlations between differential methylation at gene promoters and expression have been found. We hypothesize that stronger associations are not observed because existing analysis methods oversimplify their representation of the data and do not capture the diversity of existing methylation patterns. Recently, other patterns such as CpG island shore methylation and long partially hypomethylated domains have also been linked with gene silencing. Here, we detail a new approach for discovering differential methylation patterns associated with expression change using genome-wide high-resolution methylation data: we represent differential methylation as an interpolated curve, or signature, and then identify groups of genes with similarly shaped signatures and corresponding expression changes. Our technique uncovers a diverse set of patterns that are conserved across embryonic stem cell and cancer data sets. Overall, we find strong associations between these methylation patterns and expression. We further show that an extension of our method also outperforms other approaches by generating a longer list of genes with higher quality associations between differential methylation and expression. PMID:23748561

Screening and identification of gastric adenocarcinoma metastasis-related genes by using cDNA microarray coupled to FDD-PCR.

PubMed

Wang, Jian-Hua; Chen, Shi-Shu

2002-07-01

To clone gastric adenocarcinoma metastasis related genes, RF-1 cell line (primary tumor of a gastric adenocarcinoma patient ) and RF-48 cell line (its metastatic counterpart) were used as a model for studying the molecular mechanism of tumor metastasis. Two fluorescent cDNA probes, labeled with Cy3 and Cy5 dyes, were prepared from RF-1 and RF-48 mRNA samples by reverse transcription method. The two color probes were then mixed and hybridized to the cDNA chip constructed by double-dots of 4 096 human genes, and scanned at two wavelengths. The experiment was repeated for 2 times. Differential expression genes from the above two cells were analyzed using the computer. 138 in all genes (3.4%) revealed differential expression in RF-48 cells compared with RF-1 cells: 81(2.1%) genes revealed apparent up-regulation, and 56(1.3%) genes revealed down-regulation. 45 genes involved in gastric adenocarcinoma metastasis were cloned using fluorescent differential display-PCR (FDD-PCR), including 3 novel genes. There were 7 differential expression genes that agreed with each other in two detection methods. The possible roles of some differential expressed genes, which maybe involved in the mechanism of tumor metastasis, were discussed. cDNA chip was used to analyze gene expression in a high-throughput and large scale manner, in combination with FDD-PCR for cloning unknown novel genes. In conclusion, some genes related to metastasis were preliminarily scanned, which would contribute to disclose the molecular mechanism of gastric adenocarcinoma metastasis.

A single EBV-based vector for stable episomal maintenance and expression of GFP in human embryonic stem cells.

PubMed

Thyagarajan, Bhaskar; Scheyhing, Kelly; Xue, Haipeng; Fontes, Andrew; Chesnut, Jon; Rao, Mahendra; Lakshmipathy, Uma

2009-03-01

Stable expression of transgenes in stem cells has been a challenge due to the nonavailability of efficient transfection methods and the inability of transgenes to support sustained gene expression. Several methods have been reported to stably modify both embryonic and adult stem cells. These methods rely on integration of the transgene into the genome of the host cell, which could result in an expression pattern dependent on the number of integrations and the genomic locus of integration. To overcome this issue, site-specific integration methods mediated by integrase, adeno-associated virus or via homologous recombination have been used to generate stable human embryonic stem cell (hESC) lines. In this study, we describe a vector that is maintained episomally in hESCs. The vector used in this study is based on components derived from the Epstein-Barr virus, containing the Epstein-Barr virus nuclear antigen 1 expression cassette and the OriP origin of replication. The vector also expresses the drug-resistance marker gene hygromycin, which allows for selection and long-term maintenance of cells harboring the plasmid. Using this vector system, we show sustained expression of green fluorescent protein in undifferentiated hESCs and their differentiating embryoid bodies. In addition, the stable hESC clones show comparable expression with and without drug selection. Consistent with this observation, bulk-transfected adipose tissue-derived mesenchymal stem cells showed persistent marker gene expression as they differentiate into adipocytes, osteoblasts and chondroblasts. Episomal vectors offer a fast and efficient method to create hESC reporter lines, which in turn allows one to test the effect of overexpression of various genes on stem cell growth, proliferation and differentiation.

Purification of cardiac myocytes from human heart biopsies for gene expression analysis.

PubMed

Kosloski, L M; Bales, I K; Allen, K B; Walker, B L; Borkon, A M; Stuart, R S; Pak, A F; Wacker, M J

2009-09-01

The collection of gene expression data from human heart biopsies is important for understanding the cellular mechanisms of arrhythmias and diseases such as cardiac hypertrophy and heart failure. Many clinical and basic research laboratories conduct gene expression analysis using RNA from whole cardiac biopsies. This allows for the analysis of global changes in gene expression in areas of the heart, while eliminating the need for more complex and technically difficult single-cell isolation procedures (such as flow cytometry, laser capture microdissection, etc.) that require expensive equipment and specialized training. The abundance of fibroblasts and other cell types in whole biopsies, however, can complicate gene expression analysis and the interpretation of results. Therefore, we have designed a technique to quickly and easily purify cardiac myocytes from whole cardiac biopsies for RNA extraction. Human heart tissue samples were collected, and our purification method was compared with the standard nonpurification method. Cell imaging using acridine orange staining of the purified sample demonstrated that >98% of total RNA was contained within identifiable cardiac myocytes. Real-time RT-PCR was performed comparing nonpurified and purified samples for the expression of troponin T (myocyte marker), vimentin (fibroblast marker), and alpha-smooth muscle actin (smooth muscle marker). Troponin T expression was significantly increased, and vimentin and alpha-smooth muscle actin were significantly decreased in the purified sample (n = 8; P < 0.05). Extracted RNA was analyzed during each step of the purification, and no significant degradation occurred. These results demonstrate that this isolation method yields a more purified cardiac myocyte RNA sample suitable for downstream applications, such as real-time RT-PCR, and allows for more accurate gene expression changes in cardiac myocytes from heart biopsies.

Microarray Detection Call Methodology as a Means to Identify and Compare Transcripts Expressed within Syncytial Cells from Soybean (Glycine max) Roots Undergoing Resistant and Susceptible Reactions to the Soybean Cyst Nematode (Heterodera glycines)

PubMed Central

Klink, Vincent P.; Overall, Christopher C.; Alkharouf, Nadim W.; MacDonald, Margaret H.; Matthews, Benjamin F.

2010-01-01

Background. A comparative microarray investigation was done using detection call methodology (DCM) and differential expression analyses. The goal was to identify genes found in specific cell populations that were eliminated by differential expression analysis due to the nature of differential expression methods. Laser capture microdissection (LCM) was used to isolate nearly homogeneous populations of plant root cells. Results. The analyses identified the presence of 13,291 transcripts between the 4 different sample types. The transcripts filtered down into a total of 6,267 that were detected as being present in one or more sample types. A comparative analysis of DCM and differential expression methods showed a group of genes that were not differentially expressed, but were expressed at detectable amounts within specific cell types. Conclusion. The DCM has identified patterns of gene expression not shown by differential expression analyses. DCM has identified genes that are possibly cell-type specific and/or involved in important aspects of plant nematode interactions during the resistance response, revealing the uniqueness of a particular cell population at a particular point during its differentiation process. PMID:20508855

Method for increasing thermostability in cellulase ennzymes

DOEpatents

Adney, William S.; Thomas, Steven R.; Baker, John O.; Himmel, Michael E.; Chou, Yat-Chen

1998-01-01

The gene encoding Acidothermus cellulolyticus E1 endoglucanase is cloned and expressed in Pichia pastoris. A new modified E1 endoglucanase enzyme comprising the catalytic domain of the full size E1 enzyme demonstrates enhanced thermostability and is produced by two methods. The first method of producing the new modified E1 is proteolytic cleavage to remove the cellulose binding domain and linker peptide of the full size E1. The second method of producing the new modified E1 is genetic truncation of the gene encoding the full size E1 so that the catalytic domain is expressed in the expression product.

Identification of two integration sites in favor of transgene expression in Trichoderma reesei.

PubMed

Qin, Lina; Jiang, Xianzhang; Dong, Zhiyang; Huang, Jianzhong; Chen, Xiuzhen

2018-01-01

The ascomycete fungus Trichoderma reesei was widely used as a biotechnological workhorse for production of cellulases and recombinant proteins due to its large capacity of protein secretion. Transgenesis by random integration of a gene of interest (GOI) into the genome of T. reesei can generate series of strains that express different levels of the indicated transgene. The insertion site of the GOI plays an important role in the ultimate production of the targeted proteins. However, so far no systematic studies have been made to identify transgene integration loci for optimal expression of the GOI in T. reesei . Currently, only the locus of exocellobiohydrolases I encoding gene ( cbh1) is widely used as a promising integration site to lead to high expression level of the GOI. No additional sites associated with efficient gene expression have been characterized. To search for gene integration sites that benefit for the secreted expression of GOI, the food-and-mouth disease virus 2A protein was applied for co-expression of an Aspergillus niger lipA gene and Discosoma sp. DsRed1 gene in T. reesei, by random integration of the expression cassette into the genome. We demonstrated that the fluorescent intensity of RFP (red fluorescent protein) inside of the cell was well correlated with the secreted lipase yields, based on which, we successfully developed a high-throughput screening method to screen strains with relatively higher secreted expression of the GOI (in this study, lipase). The copy number and the insertion sites of the transgene were investigated among the selected highly expressed strains. Eventually, in addition to cbh1 gene locus, two other genome insertion loci that efficiently facilitate gene expression in T. reesei were identified. We have successfully developed a high-throughput screening method to screen strains with optimal expression of the indicated secreted proteins in T. reesei . Moreover, we identified two optimal genome loci for transgene expression, which could provide new approach to modulate gene expression levels while retaining the indicated promoter and culture conditions.

A regulatory sequence from the retinoid X receptor γ gene directs expression to horizontal cells and photoreceptors in the embryonic chicken retina.

PubMed

Blixt, Maria K E; Hallböök, Finn

2016-01-01

Combining techniques of episomal vector gene-specific Cre expression and genomic integration using the piggyBac transposon system enables studies of gene expression-specific cell lineage tracing in the chicken retina. In this work, we aimed to target the retinal horizontal cell progenitors. A 208 bp gene regulatory sequence from the chicken retinoid X receptor γ gene (RXRγ208) was used to drive Cre expression. RXRγ is expressed in progenitors and photoreceptors during development. The vector was combined with a piggyBac "donor" vector containing a floxed STOP sequence followed by enhanced green fluorescent protein (EGFP), as well as a piggyBac helper vector for efficient integration into the host cell genome. The vectors were introduced into the embryonic chicken retina with in ovo electroporation. Tissue electroporation targets specific developmental time points and in specific structures. Cells that drove Cre expression from the regulatory RXRγ208 sequence excised the floxed STOP-sequence and expressed GFP. The approach generated a stable lineage with robust expression of GFP in retinal cells that have activated transcription from the RXRγ208 sequence. Furthermore, GFP was expressed in cells that express horizontal or photoreceptor markers when electroporation was performed between developmental stages 22 and 28. Electroporation of a stage 12 optic cup gave multiple cell types in accordance with RXRγ gene expression in the early retina. In this study, we describe an easy, cost-effective, and time-efficient method for testing regulatory sequences in general. More specifically, our results open up the possibility for further studies of the RXRγ-gene regulatory network governing the formation of photoreceptor and horizontal cells. In addition, the method presents approaches to target the expression of effector genes, such as regulators of cell fate or cell cycle progression, to these cells and their progenitor.

Clinical omics analysis of colorectal cancer incorporating copy number aberrations and gene expression data.

PubMed

Yoshida, Tsuyoshi; Kobayashi, Takumi; Itoda, Masaya; Muto, Taika; Miyaguchi, Ken; Mogushi, Kaoru; Shoji, Satoshi; Shimokawa, Kazuro; Iida, Satoru; Uetake, Hiroyuki; Ishikawa, Toshiaki; Sugihara, Kenichi; Mizushima, Hiroshi; Tanaka, Hiroshi

2010-07-29

Colorectal cancer (CRC) is one of the most frequently occurring cancers in Japan, and thus a wide range of methods have been deployed to study the molecular mechanisms of CRC. In this study, we performed a comprehensive analysis of CRC, incorporating copy number aberration (CRC) and gene expression data. For the last four years, we have been collecting data from CRC cases and organizing the information as an "omics" study by integrating many kinds of analysis into a single comprehensive investigation. In our previous studies, we had experienced difficulty in finding genes related to CRC, as we observed higher noise levels in the expression data than in the data for other cancers. Because chromosomal aberrations are often observed in CRC, here, we have performed a combination of CNA analysis and expression analysis in order to identify some new genes responsible for CRC. This study was performed as part of the Clinical Omics Database Project at Tokyo Medical and Dental University. The purpose of this study was to investigate the mechanism of genetic instability in CRC by this combination of expression analysis and CNA, and to establish a new method for the diagnosis and treatment of CRC. Comprehensive gene expression analysis was performed on 79 CRC cases using an Affymetrix Gene Chip, and comprehensive CNA analysis was performed using an Affymetrix DNA Sty array. To avoid the contamination of cancer tissue with normal cells, laser micro-dissection was performed before DNA/RNA extraction. Data analysis was performed using original software written in the R language. We observed a high percentage of CNA in colorectal cancer, including copy number gains at 7, 8q, 13 and 20q, and copy number losses at 8p, 17p and 18. Gene expression analysis provided many candidates for CRC-related genes, but their association with CRC did not reach the level of statistical significance. The combination of CNA and gene expression analysis, together with the clinical information, suggested UGT2B28, LOC440995, CXCL6, SULT1B1, RALBP1, TYMS, RAB12, RNMT, ARHGDIB, S1000A2, ABHD2, OIT3 and ABHD12 as genes that are possibly associated with CRC. Some of these genes have already been reported as being related to CRC. TYMS has been reported as being associated with resistance to the anti-cancer drug 5-fluorouracil, and we observed a copy number increase for this gene. RALBP1, ARHGDIB and S100A2 have been reported as oncogenes, and we observed copy number increases in each. ARHGDIB has been reported as a metastasis-related gene, and our data also showed copy number increases of this gene in cases with metastasis. The combination of CNA analysis and gene expression analysis was a more effective method for finding genes associated with the clinicopathological classification of CRC than either analysis alone. Using this combination of methods, we were able to detect genes that have already been associated with CRC. We also identified additional candidate genes that may be new markers or targets for this form of cancer.

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots.

PubMed

Zhou, Zhe; Cong, Peihua; Tian, Yi; Zhu, Yanmin

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization.

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots

PubMed Central

Zhou, Zhe; Cong, Peihua; Tian, Yi

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization. PMID:28934340

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development.

PubMed

Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-12-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. © 2014 American Society of Plant Biologists. All rights reserved.

Mining the archives: a cross-platform analysis of gene ...

EPA Pesticide Factsheets

Formalin-fixed paraffin-embedded (FFPE) tissue samples represent a potentially invaluable resource for genomic research into the molecular basis of disease. However, use of FFPE samples in gene expression studies has been limited by technical challenges resulting from degradation of nucleic acids. Here we evaluated gene expression profiles derived from fresh-frozen (FRO) and FFPE mouse liver tissues using two DNA microarray protocols and two whole transcriptome sequencing (RNA-seq) library preparation methodologies. The ribo-depletion protocol outperformed the other three methods by having the highest correlations of differentially expressed genes (DEGs) and best overlap of pathways between FRO and FFPE groups. We next tested the effect of sample time in formalin (18 hours or 3 weeks) on gene expression profiles. Hierarchical clustering of the datasets indicated that test article treatment, and not preservation method, was the main driver of gene expression profiles. Meta- and pathway analyses indicated that biological responses were generally consistent for 18-hour and 3-week FFPE samples compared to FRO samples. However, clear erosion of signal intensity with time in formalin was evident, and DEG numbers differed by platform and preservation method. Lastly, we investigated the effect of age in FFPE block on genomic profiles. RNA-seq analysis of 8-, 19-, and 26-year-old control blocks using the ribo-depletion protocol resulted in comparable quality metrics, inc

Impact of missing data imputation methods on gene expression clustering and classification.

PubMed

de Souto, Marcilio C P; Jaskowiak, Pablo A; Costa, Ivan G

2015-02-26

Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. We performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/ .

Identification of reference genes for RT-qPCR in ovine mammary tissue during late pregnancy and lactation and in response to maternal nutritional programming.

PubMed

Paten, A M; Pain, S J; Peterson, S W; Blair, H T; Kenyon, P R; Dearden, P K; Duncan, E J

2014-08-01

The mammary gland is a complex tissue consisting of multiple cell types which, over the lifetime of an animal, go through repeated cycles of development associated with pregnancy, lactation and involution. The mammary gland is also known to be sensitive to maternal programming by environmental stimuli such as nutrition. The molecular basis of these adaptations is of significant interest, but requires robust methods to measure gene expression. Reverse-transcription quantitative PCR (RT-qPCR) is commonly used to measure gene expression, and is currently the method of choice for validating genome-wide expression studies. RT-qPCR requires the selection of reference genes that are stably expressed over physiological states and treatments. In this study we identify suitable reference genes to normalize RT-qPCR data for the ovine mammary gland in two physiological states; late pregnancy and lactation. Biopsies were collected from offspring of ewes that had been subjected to different nutritional paradigms during pregnancy to examine effects of maternal programming on the mammary gland of the offspring. We evaluated eight candidate reference genes and found that two reference genes (PRPF3 and CUL1) are required for normalising RT-qPCR data from pooled RNA samples, but five reference genes are required for analyzing gene expression in individual animals (SENP2, EIF6, MRPL39, ATP1A1, CUL1). Using these stable reference genes, we showed that TET1, a key regulator of DNA methylation, is responsive to maternal programming and physiological state. The identification of these novel reference genes will be of utility to future studies of gene expression in the ovine mammary gland. Copyright © 2014 the American Physiological Society.

Senescence responsive transcriptional element

DOEpatents

Campisi, Judith; Testori, Alessandro

1999-01-01

Recombinant polynucleotides have expression control sequences that have a senescence responsive element and a minimal promoter, and which are operatively linked to a heterologous nucleotide sequence. The molecules are useful for achieving high levels of expression of genes in senescent cells. Methods of inhibiting expression of genes in senescent cells also are provided.

Global Gene Expression Analysis of Yeast Cells during Sake Brewing▿ †

PubMed Central

Wu, Hong; Zheng, Xiaohong; Araki, Yoshio; Sahara, Hiroshi; Takagi, Hiroshi; Shimoi, Hitoshi

2006-01-01

During the brewing of Japanese sake, Saccharomyces cerevisiae cells produce a high concentration of ethanol compared with other ethanol fermentation methods. We analyzed the gene expression profiles of yeast cells during sake brewing using DNA microarray analysis. This analysis revealed some characteristics of yeast gene expression during sake brewing and provided a scaffold for a molecular level understanding of the sake brewing process. PMID:16997994

Single-feature polymorphism discovery in the barley transcriptome

PubMed Central

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

The Effect of Garlic Extract on Expression of INFγ And Inos Genes in Macrophages Infected with Leishmania major

PubMed Central

Gharavi, MJ; Nobakht, M; Khademvatan, SH; Bandani, E; Bakhshayesh, M; Roozbehani, M

2011-01-01

Background The study was aimed to show the effect of molecular mechanism of Aqueous Garlic Extract (AGE) on expression of IFNγ and iNOS genes in Leishmania major. Methods Leishmania major promastigotes (MRHO/IR/75/ER) were added to the in-vitro cultured J774 cell line, the cells were incubated for 72 hours. Various concentrations of garlic extract (9.25, 18.5, 37, 74, 148 mg/ml) were added to the infected cells. MTT assay was applied for cellular proliferation. After 72 hours of incubation, supernatants were collected and total RNA was extracted from the infected cells. The express of IFNγ and iNOS genes were studied by RT-PCR method. Results The colorimetric MTT assay after 3 days of incubation showed cytotoxic effect of garlic extract with an IC50 of 37 mg/ml. In addition, IFNγ and iNOS genes expression by RT-PCR indicated that garlic extract lead to over expression of these genes in J774 cell line infected with L. major. Conclusion Garlic extract exerts cytotoxic effect on infected J774 cell line. In addition, the hypothesis that garlic can improve cellular immunity with raising the expression of IFNγ and of iNOS genes confirmed. PMID:22347300

Analysis of differential gene expression by bead-based fiber-optic array in growth-hormone-secreting pituitary adenomas.

PubMed

Jiang, Zhiquan; Gui, Songbo; Zhang, Yazhuo

2010-09-01

Growth-hormone-secreting pituitary adenomas (GHomas) account for approximately 20% of all pituitary neoplasms. However, the pathogenesis of GHomas remains to be elucidated. To explore the possible pathogenesis of GHomas, we used bead-based fiber-optic arrays to examine the gene expression in five GHomas and compared them to three healthy pituitaries. Four differentially expressed genes were chosen randomly for validation by quantitative real-time reverse transcription-polymerase chain reaction. We then performed pathway analysis on the identified differentially expressed genes using the Kyoto Encyclopedia of Genes and Genomes. Array analysis showed significant increases in the expression of 353 genes and 206 expressed sequence tags (ESTs) and decreases in 565 genes and 29 ESTs. Bioinformatic analysis showed that the genes HIGD1B, HOXB2, ANGPT2, HPGD and BTG2 may play an important role in the tumorigenesis and progression of GHomas. Pathway analysis showed that the wingless-type signaling pathway and extracellular-matrix receptor interactions may play a key role in the tumorigenesis and progression of GHomas. Our data suggested that there are numerous aberrantly expressed genes and pathways involved in the pathogenesis of GHomas. Bead-based fiber-optic arrays combined with pathway analysis of differentially expressed genes appear to be a valid method for investigating the pathogenesis of tumors.

Analysis of differential gene expression by bead-based fiber-optic array in growth-hormone-secreting pituitary adenomas

PubMed Central

JIANG, ZHIQUAN; GUI, SONGBO; ZHANG, YAZHUO

2010-01-01

Growth-hormone-secreting pituitary adenomas (GHomas) account for approximately 20% of all pituitary neoplasms. However, the pathogenesis of GHomas remains to be elucidated. To explore the possible pathogenesis of GHomas, we used bead-based fiber-optic arrays to examine the gene expression in five GHomas and compared them to three healthy pituitaries. Four differentially expressed genes were chosen randomly for validation by quantitative real-time reverse transcription-polymerase chain reaction. We then performed pathway analysis on the identified differentially expressed genes using the Kyoto Encyclopedia of Genes and Genomes. Array analysis showed significant increases in the expression of 353 genes and 206 expressed sequence tags (ESTs) and decreases in 565 genes and 29 ESTs. Bioinformatic analysis showed that the genes HIGD1B, HOXB2, ANGPT2, HPGD and BTG2 may play an important role in the tumorigenesis and progression of GHomas. Pathway analysis showed that the wingless-type signaling pathway and extracellular-matrix receptor interactions may play a key role in the tumorigenesis and progression of GHomas. Our data suggested that there are numerous aberrantly expressed genes and pathways involved in the pathogenesis of GHomas. Bead-based fiber-optic arrays combined with pathway analysis of differentially expressed genes appear to be a valid method for investigating the pathogenesis of tumors. PMID:22993617

Challenges in projecting clustering results across gene expression-profiling datasets.

PubMed

Lusa, Lara; McShane, Lisa M; Reid, James F; De Cecco, Loris; Ambrogi, Federico; Biganzoli, Elia; Gariboldi, Manuela; Pierotti, Marco A

2007-11-21

Gene expression microarray studies for several types of cancer have been reported to identify previously unknown subtypes of tumors. For breast cancer, a molecular classification consisting of five subtypes based on gene expression microarray data has been proposed. These subtypes have been reported to exist across several breast cancer microarray studies, and they have demonstrated some association with clinical outcome. A classification rule based on the method of centroids has been proposed for identifying the subtypes in new collections of breast cancer samples; the method is based on the similarity of the new profiles to the mean expression profile of the previously identified subtypes. Previously identified centroids of five breast cancer subtypes were used to assign 99 breast cancer samples, including a subset of 65 estrogen receptor-positive (ER+) samples, to five breast cancer subtypes based on microarray data for the samples. The effect of mean centering the genes (i.e., transforming the expression of each gene so that its mean expression is equal to 0) on subtype assignment by method of centroids was assessed. Further studies of the effect of mean centering and of class prevalence in the test set on the accuracy of method of centroids classifications of ER status were carried out using training and test sets for which ER status had been independently determined by ligand-binding assay and for which the proportion of ER+ and ER- samples were systematically varied. When all 99 samples were considered, mean centering before application of the method of centroids appeared to be helpful for correctly assigning samples to subtypes, as evidenced by the expression of genes that had previously been used as markers to identify the subtypes. However, when only the 65 ER+ samples were considered for classification, many samples appeared to be misclassified, as evidenced by an unexpected distribution of ER+ samples among the resultant subtypes. When genes were mean centered before classification of samples for ER status, the accuracy of the ER subgroup assignments was highly dependent on the proportion of ER+ samples in the test set; this effect of subtype prevalence was not seen when gene expression data were not mean centered. Simple corrections such as mean centering of genes aimed at microarray platform or batch effect correction can have undesirable consequences because patient population effects can easily be confused with these assay-related effects. Careful thought should be given to the comparability of the patient populations before attempting to force data comparability for purposes of assigning subtypes to independent subjects.

NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples.

PubMed

Yu, Hua; Jiao, Bingke; Lu, Lu; Wang, Pengfei; Chen, Shuangcheng; Liang, Chengzhi; Liu, Wei

2018-01-01

Accurately reconstructing gene co-expression network is of great importance for uncovering the genetic architecture underlying complex and various phenotypes. The recent availability of high-throughput RNA-seq sequencing has made genome-wide detecting and quantifying of the novel, rare and low-abundance transcripts practical. However, its potential merits in reconstructing gene co-expression network have still not been well explored. Using massive-scale RNA-seq samples, we have designed an ensemble pipeline, called NetMiner, for building genome-scale and high-quality Gene Co-expression Network (GCN) by integrating three frequently used inference algorithms. We constructed a RNA-seq-based GCN in one species of monocot rice. The quality of network obtained by our method was verified and evaluated by the curated gene functional association data sets, which obviously outperformed each single method. In addition, the powerful capability of network for associating genes with functions and agronomic traits was shown by enrichment analysis and case studies. In particular, we demonstrated the potential value of our proposed method to predict the biological roles of unknown protein-coding genes, long non-coding RNA (lncRNA) genes and circular RNA (circRNA) genes. Our results provided a valuable and highly reliable data source to select key candidate genes for subsequent experimental validation. To facilitate identification of novel genes regulating important biological processes and phenotypes in other plants or animals, we have published the source code of NetMiner, making it freely available at https://github.com/czllab/NetMiner.

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.

PubMed

Ma, Chuang; Wang, Xiangfeng

2012-09-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey's biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

Application of the Gini Correlation Coefficient to Infer Regulatory Relationships in Transcriptome Analysis[W][OA

PubMed Central

Ma, Chuang; Wang, Xiangfeng

2012-01-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey’s biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses. PMID:22797655

Comparison of the QuantiGene 2.0 Assay and Real-Time RT-PCR in the Detection of p53 Isoform mRNA Expression in Formalin-Fixed Paraffin-Embedded Tissues- A Preliminary Study

PubMed Central

Morten, Brianna C.; Scott, Rodney J.; Avery-Kiejda, Kelly A.

2016-01-01

p53 is expressed as multiple smaller isoforms whose functions in cancer are not well understood. The p53 isoforms demonstrate abnormal expression in different cancers, suggesting they are important in modulating the function of full-length p53 (FLp53). The quantification of relative mRNA expression has routinely been performed using real-time PCR (qPCR). However, there are serious limitations when detecting p53 isoforms using this method, particularly for formalin-fixed paraffin-embedded (FFPE) tissues. The use of FFPE tumours would be advantageous to correlate expression of p53 isoforms with important clinical features of cancer. One alternative method of RNA detection is the hybridization-based QuantiGene 2.0 Assay, which has been shown to be advantageous for the detection of RNA from FFPE tissues. In this pilot study, we compared the QuantiGene 2.0 Assay to qPCR for the detection of FLp53 and its isoform Δ40p53 in matched fresh frozen (FF) and FFPE breast tumours. FLp53 mRNA expression was detected using qPCR in FF and FFPE tissues, but Δ40p53 mRNA was only detectable in FF tissues. Similar results were obtained for the QuantiGene 2.0 Assay. FLp53 relative mRNA expression was shown to be strongly correlated between the two methods (R2 = 0.9927, p = 0.0031) in FF tissues, however Δ40p53 was not (R2 = 0.4429, p = 0.3345). When comparing the different methods for the detection of FLp53 mRNA from FFPE and FF samples, no correlation (R2 = 0.0002, p = 0.9863) was shown using the QuantiGene 2.0 Assay, and in contrast, the level of expression was highly correlated between the two tissues using qPCR (R2 = 0.8753, p = 0.0644). These results suggest that both the QuantiGene 2.0 Assay and qPCR methods are inadequate for the quantification of Δ40p53 mRNA in FFPE tissues. Therefore, alternative methods of RNA detection and quantification are required to study the relative expression of Δ40p53 in FFPE samples. PMID:27832134

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data.

PubMed

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R; Del Río-Navarro, Blanca E; Mendoza-Vargas, Alfredo; Sánchez, Filiberto; Ochoa-Leyva, Adrian

2017-01-01

In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6-10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

The targeting expression of the vascular endothelial growth factor gene in endothelial cells regulated by HRE.ppET-1.

PubMed

Zheng, Xiangrong; Zhang, Shangshang; Yang, Yujia; Wang, Xia; Zhong, Le; Yu, Xiaohe

2008-11-01

The success of gene therapy depends largely on the efficacy of gene delivery vector systems that can deliver genes to target organs or cells selectively and efficiently with minimal toxicity. Here, we show that by using the HRE.ppET-1 regulatory element, we were able to restrict expression of the transgene of vascular endothelial growth factor (VEGF) to endothelial cells exclusively in hypoxic conditions. Eukaryotic expression vectors such as pEGFP-HRE.ppET-1, pcDNA3.1-VEGF+Pa, pcDNA3.1-ppET-1+ EGF+Pa, and pcDNA3.1-HRE.ppET-1+VEGF+Pa were constructed by using a series of nuclear molecule handling methods like PCR, enzyme digestion. The recombinant vectors were transfected into HUVEC cells and HL7702 cells by the lipofectin method. GFP expression was observed with a fluorescence microscope to validate the specificity of expression in endothelial cells under the regulation of HRE.ppET-1 element. Cobalt chloride (final concentration 100 mumol/L) was added to the medium to mimic hypoxia in vitro. After transfection of vectors, the expression of VEGF mRNA was detected by RT-PCR, and the expression of VEGF was detected by Western blotting and ELISA methods under normoxia and hypoxia, respectively. The cell proliferation rate was detected by the MTT test. The expression of GFP revealed that the exterior gene was transcripted effectively in endothelial cells regulated by the HRE.ppET-1 element, while the expression of GFP was very weak in nonendothelial cells. The results of RT-PCR, Western blotting and ELISA showed that VEGF gene expression in the pcDNA3.1-HRE.ppET-1+VEGF+Pa group and in the pcDNA3.1-ppET-1+VEGF+Pa group was higher in hypoxia than it was in normoxia (P<0.05). The MTT test showed that the proliferation rate of HUVEC transfected with HPVA under hypoxia exceeded that of the control group. We conclude that the HRE.ppET-1 element was expressed specifically in endothelial cells, and can increase the expression of VEGF in hypoxia and stimulate proliferation of endothelial cells. Taking advantage of these facts could greatly improve the efficiency of gene therapy. The vector would be valuable for various gene transfer studies targeting endothelial cells.

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

PubMed Central

Lozoya, Oswaldo A.; Santos, Janine H.; Woychik, Richard P.

2018-01-01

To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays. PMID:29868123

Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

PubMed

Huang, Shi-Ming; Zhao, Xia; Zhao, Xue-Mei; Wang, Xiao-Ying; Li, Shan-Shan; Zhu, Yu-Hui

2014-01-01

Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent. MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone. A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection. We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.

Gene for ataxia-telangiectasia complementation group D (ATDC)

DOEpatents

Murnane, John P.; Painter, Robert B.; Kapp, Leon N.; Yu, Loh-Chung

1995-03-07

Disclosed herein is a new gene, an AT gene for complementation group D, the ATDC gene and fragments thereof. Nucleic acid probes for said gene are provided as well as proteins encoded by said gene, cDNA therefrom, preferably a 3 kilobase (kb) cDNA, and recombinant nucleic acid molecules for expression of said proteins. Further disclosed are methods to detect mutations in said gene, preferably methods employing the polymerase chain reaction (PCR). Also disclosed are methods to detect AT genes from other AT complementation groups.

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

Selection and validation of reference genes for gene expression analysis in apomictic and sexual Cenchrus ciliaris

PubMed Central

2013-01-01

Background Apomixis is a naturally occurring asexual mode of seed reproduction resulting in offspring genetically identical to the maternal plant. Identifying differential gene expression patterns between apomictic and sexual plants is valuable to help deconstruct the trait. Quantitative RT-PCR (qRT-PCR) is a popular method for analyzing gene expression. Normalizing gene expression data using proper reference genes which show stable expression under investigated conditions is critical in qRT-PCR analysis. We used qRT-PCR to validate expression and stability of six potential reference genes (EF1alpha, EIF4A, UBCE, GAPDH, ACT2 and TUBA) in vegetative and reproductive tissues of B-2S and B-12-9 accessions of C. ciliaris. Findings Among tissue types evaluated, EF1alpha showed the highest level of expression while TUBA showed the lowest. When all tissue types were evaluated and compared between genotypes, EIF4A was the most stable reference gene. Gene expression stability for specific ovary stages of B-2S and B-12-9 was also determined. Except for TUBA, all other tested reference genes could be used for any stage-specific ovary tissue normalization, irrespective of the mode of reproduction. Conclusion Our gene expression stability assay using six reference genes, in sexual and apomictic accessions of C. ciliaris, suggests that EIF4A is the most stable gene across all tissue types analyzed. All other tested reference genes, with the exception of TUBA, could be used for gene expression comparison studies between sexual and apomictic ovaries over multiple developmental stages. This reference gene validation data in C. ciliaris will serve as an important base for future apomixis-related transcriptome data validation. PMID:24083672

Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data

PubMed Central

Demidenko, Natalia V.; Logacheva, Maria D.; Penin, Aleksey A.

2011-01-01

Quantitative reverse transcription PCR (qRT-PCR) is one of the most precise and widely used methods of gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes. We studied the expression stability of potential reference genes in common buckwheat (Fagopyrum esculentum) in order to find the optimal reference for gene expression analysis in this economically important crop. Recently sequenced buckwheat floral transcriptome was used as source of sequence information. Expression stability of eight candidate reference genes was assessed in different plant structures (leaves and inflorescences at two stages of development and fruits). These genes are the orthologs of Arabidopsis genes identified as stable in a genome-wide survey gene of expression stability and a traditionally used housekeeping gene GAPDH. Three software applications – geNorm, NormFinder and BestKeeper - were used to estimate expression stability and provided congruent results. The orthologs of AT4G33380 (expressed protein of unknown function, Expressed1), AT2G28390 (SAND family protein, SAND) and AT5G46630 (clathrin adapter complex subunit family protein, CACS) are revealed as the most stable. We recommend using the combination of Expressed1, SAND and CACS for the normalization of gene expression data in studies on buckwheat using qRT-PCR. These genes are listed among five the most stably expressed in Arabidopsis that emphasizes utility of the studies on model plants as a framework for other species. PMID:21589908

Transcriptomic biomarkers of altered erythropoiesis to detect autologous blood transfusion.

PubMed

Salamin, Olivier; Mignot, Jonathan; Kuuranne, Tiia; Saugy, Martial; Leuenberger, Nicolas

2018-03-01

Autologous blood transfusion is a powerful means of improving performance and remains one of the most challenging methods to detect. Recent investigations have identified 3 candidate reticulocytes genes whose expression was significantly influenced by blood transfusion. Using quantitative reverse transcription polymerase chain reaction as an alternative quantitative method, the present study supports that delta-aminolevulinate synthase 2 (ALAS2), carbonic anhydrase (CA1), and solute carrier family 4 member 1 (SLC4A1) genes are down-regulated post-transfusion. The expression of these genes exhibited stronger correlation with immature reticulocyte fraction than with reticulocytes percentage. Moreover, the repression of reticulocytes' gene expression was more pronounced than the diminution of immature reticulocyte fraction and reticulocyte percentage following blood transfusion. It suggests that the 3 candidate genes are reliable predictors of bone marrow's response to blood transfusion and that they represent potential biomarkers for the detection of this method prohibited in sports. Copyright © 2017 John Wiley & Sons, Ltd.

Reverse engineering of gene regulatory networks.

PubMed

Cho, K H; Choo, S M; Jung, S H; Kim, J R; Choi, H S; Kim, J

2007-05-01

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

PubMed Central

2016-01-01

Abstract Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications. PMID:26504096

Using deep RNA sequencing for the structural annotation of the laccaria bicolor mycorrhizal transcriptome.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, P. E.; Trivedi, G.; Sreedasyam, A.

2010-07-06

Accurate structural annotation is important for prediction of function and required for in vitro approaches to characterize or validate the gene expression products. Despite significant efforts in the field, determination of the gene structure from genomic data alone is a challenging and inaccurate process. The ease of acquisition of transcriptomic sequence provides a direct route to identify expressed sequences and determine the correct gene structure. We developed methods to utilize RNA-seq data to correct errors in the structural annotation and extend the boundaries of current gene models using assembly approaches. The methods were validated with a transcriptomic data set derivedmore » from the fungus Laccaria bicolor, which develops a mycorrhizal symbiotic association with the roots of many tree species. Our analysis focused on the subset of 1501 gene models that are differentially expressed in the free living vs. mycorrhizal transcriptome and are expected to be important elements related to carbon metabolism, membrane permeability and transport, and intracellular signaling. Of the set of 1501 gene models, 1439 (96%) successfully generated modified gene models in which all error flags were successfully resolved and the sequences aligned to the genomic sequence. The remaining 4% (62 gene models) either had deviations from transcriptomic data that could not be spanned or generated sequence that did not align to genomic sequence. The outcome of this process is a set of high confidence gene models that can be reliably used for experimental characterization of protein function. 69% of expressed mycorrhizal JGI 'best' gene models deviated from the transcript sequence derived by this method. The transcriptomic sequence enabled correction of a majority of the structural inconsistencies and resulted in a set of validated models for 96% of the mycorrhizal genes. The method described here can be applied to improve gene structural annotation in other species, provided that there is a sequenced genome and a set of gene models.« less

Isolated Fungal Promoters and Gene Transcription Terminators and Methods of Protein and Chemical Production in a Fungus

DOEpatents

Dai, Ziyu; Lasure, Linda L.; Magnuson, Jon K.

2008-11-11

The present invention encompasses isolated gene regulatory elements and gene transcription terminators that are differentially expressed in a native fungus exhibiting a first morphology relative to the native fungus exhibiting a second morphology. The invention also encompasses a method of utilizing a fungus for protein or chemical production. A transformed fungus is produced by transforming a fungus with a recombinant polynucleotide molecule. The recombinant polynucleotide molecule contains an isolated polynucleotide sequence linked operably to another molecule comprising a coding region of a gene of interest. The gene regulatory element and gene transcription terminator may temporally and spatially regulate expression of particular genes for optimum production of compounds of interest in a transgenic fungus.

Isolated fungal promoters and gene transcription terminators and methods of protein and chemical production in a fungus

DOEpatents

Dai, Ziyu; Lasure, Linda L.; Magnuson, Jon K.

2008-11-11

The present invention encompasses isolated gene regulatory elements and gene transcription terminators that are differentially expressed in a native fungus exhibiting a first morphology relative to the native fungus exhibiting a second morphology. The invention also encompasses a method of utilizing a fungus for protein or chemical production. A transformed fungus is produced by transforming a fungus with a recombinant polynucleotide molecule. The recombinant polynucleotide molecule contains an isolated polynucleotide sequence linked operably to another molecule comprising a coding region of a gene of interest. The gene regulatory element and gene transcription terminator may temporally and spatially regulate expression of particular genes for optimum production of compounds of interest in a transgenic fungus.

Isolated fungal promoters and gene transcription terminators and methods of protein and chemical production in a fungus

DOEpatents

Dai, Ziyu; Lasure, Linda L; Magnuson, Jon K

2014-05-27

The present invention encompasses isolated gene regulatory elements and gene transcription terminators that are differentially expressed in a native fungus exhibiting a first morphology relative to the native fungus exhibiting a second morphology. The invention also encompasses a method of utilizing a fungus for protein or chemical production. A transformed fungus is produced by transforming a fungus with a recombinant polynucleotide molecule. The recombinant polynucleotide molecule contains an isolated polynucleotide sequence linked operably to another molecule comprising a coding region of a gene of interest. The gene regulatory element and gene transcription terminator may temporally and spatially regulate expression of particular genes for optimum production of compounds of interest in a transgenic fungus.

Digital analysis of the expression levels of multiple colorectal cancer-related genes by multiplexed digital-PCR coupled with hydrogel bead-array.

PubMed

Qi, Zongtai; Ma, Yinjiao; Deng, Lili; Wu, Haiping; Zhou, Guohua; Kajiyama, Tomoharu; Kambara, Hideki

2011-06-07

To digitally analyze expression levels of multiple genes in one reaction, we proposed a method termed as 'MDHB' (Multiplexed Digital-PCR coupled with Hydrogel Bead-array). The template for bead-based emulsion PCR (emPCR) was prepared by reverse transcription using sequence-tagged primers. The beads recovered from emPCR were immobilized with hydrogel to form a single-bead layer on a chip, and then decoded by gene-specific probe hybridization and Cy3-dUTP based primer extension reaction. The specificity of probe hybridization was improved by using electrophoresis to remove mismatched probes on the bead's surface. The number of positive beads reflects the abundance of expressed genes; the expression levels of target genes were normalized to a housekeeping gene and expressed as the number ratio of green beads to red beads. The discrimination limit of MDHB is 0.1% (i.e., one target molecule from 1000 background molecules), and the sensitivity of the method is below 100 cells when using the β-actin gene as the detection target. We have successfully employed MDHB to detect the relative expression levels of four colorectal cancer (CRC)-related genes (c-myc, COX-2, MMP7, and DPEP1) in 8 tissue samples and 9 stool samples from CRC patients, giving the detection rates of 100% and 77%, respectively. The results suggest that MDHB could be a potential tool for early non-invasive diagnosis of CRC.

Selection of reference genes for tissue/organ samples on day 3 fifth-instar larvae in silkworm, Bombyx mori.

PubMed

Wang, Genhong; Chen, Yanfei; Zhang, Xiaoying; Bai, Bingchuan; Yan, Hao; Qin, Daoyuan; Xia, Qingyou

2018-06-01

The silkworm, Bombyx mori, is one of the world's most economically important insect. Surveying variations in gene expression among multiple tissue/organ samples will provide clues for gene function assignments and will be helpful for identifying genes related to economic traits or specific cellular processes. To ensure their accuracy, commonly used gene expression quantification methods require a set of stable reference genes for data normalization. In this study, 24 candidate reference genes were assessed in 10 tissue/organ samples of day 3 fifth-instar B. mori larvae using geNorm and NormFinder. The results revealed that, using the combination of the expression of BGIBMGA003186 and BGIBMGA008209 was the optimum choice for normalizing the expression data of the B. mori tissue/organ samples. The most stable gene, BGIBMGA003186, is recommended if just one reference gene is used. Moreover, the commonly used reference gene encoding cytoplasmic actin was the least appropriate reference gene of the samples investigated. The reliability of the selected reference genes was further confirmed by evaluating the expression profiles of two cathepsin genes. Our results may be useful for future studies involving the quantification of relative gene expression levels of different tissue/organ samples in B. mori. © 2018 Wiley Periodicals, Inc.

Selecting and validating reference genes for quantitative real-time PCR in Plutella xylostella (L.).

PubMed

You, Yanchun; Xie, Miao; Vasseur, Liette; You, Minsheng

2018-05-01

Gene expression analysis provides important clues regarding gene functions, and quantitative real-time PCR (qRT-PCR) is a widely used method in gene expression studies. Reference genes are essential for normalizing and accurately assessing gene expression. In the present study, 16 candidate reference genes (ACTB, CyPA, EF1-α, GAPDH, HSP90, NDPk, RPL13a, RPL18, RPL19, RPL32, RPL4, RPL8, RPS13, RPS4, α-TUB, and β-TUB) from Plutella xylostella were selected to evaluate gene expression stability across different experimental conditions using five statistical algorithms (geNorm, NormFinder, Delta Ct, BestKeeper, and RefFinder). The results suggest that different reference genes or combinations of reference genes are suitable for normalization in gene expression studies of P. xylostella according to the different developmental stages, strains, tissues, and insecticide treatments. Based on the given experimental sets, the most stable reference genes were RPS4 across different developmental stages, RPL8 across different strains and tissues, and EF1-α across different insecticide treatments. A comprehensive and systematic assessment of potential reference genes for gene expression normalization is essential for post-genomic functional research in P. xylostella, a notorious pest with worldwide distribution and a high capacity to adapt and develop resistance to insecticides.

Identification of Genes Expressed in Premalignant Breast Disease by Microscopy-Directed Cloning

NASA Astrophysics Data System (ADS)

Jensen, Roy A.; Page, David L.; Holt, Jeffrey T.

1994-09-01

Histopathologic study of human breast biopsy samples has identified specific lesions which are associated with a high risk of development of invasive breast cancer. Presumably, these lesions (collectively termed premalignant breast disease) represent the earliest recognizable morphologic expression of fundamental molecular events that lead to the development of invasive breast cancer. To study molecular events underlying premalignant breast disease, we have developed a method for isolating RNA from histologically identified lesions from frozen human breast tissue. This method specifically obtains mRNA from breast epithelial cells and has identified three genes which are differentially expressed in premalignant breast epithelial lesions. One gene identified by this method is overexpressed in four of five noncomedo ductal carcinoma in situ lesions and appears to be the human homologue of the gene encoding the M2 subunit of ribonucleotide reductase, an enzyme involved in DNA synthesis.

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

An out-of-lab trial: a case example for the effect of intensive exercise on rhythms of human clock gene expression

PubMed Central

2013-01-01

Background Although out-of-lab investigation of the human circadian clock at the clock gene expression level remains difficult, a recent method using hair follicle cells might be useful. While exercise may function as an entrainment cue for circadian rhythms, it remains unclear whether exercise affects human circadian clock gene expression. Methods Efforts to observe apparent effects of exercise on clock gene expression require that several specific conditions be met: intense exercise should be habitually performed at a relatively uncommon time of day over an extended period; and any relative phase shift thereby observed should be validated by comparison of exercise and no-exercise periods. Wake-up and meal times should be kept almost constant over the experimental period. The present study was conducted using a professional fighter who met these strict criteria as subject. Facial hair samples were collected at 4-h intervals around the clock to ascertain rhythms of clock gene expression. Results During a period in which nighttime training (from 20:00 to 22:00) was habitually performed, circadian clock gene expression was phase-delayed by 2 to 4 h compared with that during a no-exercise period. Maximum level and circadian amplitude of clock gene expression were not affected by the nighttime training. Conclusion Our trial observations illustrate the possibility that heavy physical exercise might strongly affect the circadian phase of clock gene expression. Exercise might be therefore effective for the clinical care of circadian disorders. The results also suggest that athletes may require careful scheduling of heavy physical exercise to maintain normal circadian phase and ensure optimal athletic performance. PMID:24004634

Phage-mediated Delivery of Targeted sRNA Constructs to Knock Down Gene Expression in E. coli.

PubMed

Bernheim, Aude G; Libis, Vincent K; Lindner, Ariel B; Wintermute, Edwin H

2016-03-20

RNA-mediated knockdowns are widely used to control gene expression. This versatile family of techniques makes use of short RNA (sRNA) that can be synthesized with any sequence and designed to complement any gene targeted for silencing. Because sRNA constructs can be introduced to many cell types directly or using a variety of vectors, gene expression can be repressed in living cells without laborious genetic modification. The most common RNA knockdown technology, RNA interference (RNAi), makes use of the endogenous RNA-induced silencing complex (RISC) to mediate sequence recognition and cleavage of the target mRNA. Applications of this technique are therefore limited to RISC-expressing organisms, primarily eukaryotes. Recently, a new generation of RNA biotechnologists have developed alternative mechanisms for controlling gene expression through RNA, and so made possible RNA-mediated gene knockdowns in bacteria. Here we describe a method for silencing gene expression in E. coli that functionally resembles RNAi. In this system a synthetic phagemid is designed to express sRNA, which may designed to target any sequence. The expression construct is delivered to a population of E. coli cells with non-lytic M13 phage, after which it is able to stably replicate as a plasmid. Antisense recognition and silencing of the target mRNA is mediated by the Hfq protein, endogenous to E. coli. This protocol includes methods for designing the antisense sRNA, constructing the phagemid vector, packaging the phagemid into M13 bacteriophage, preparing a live cell population for infection, and performing the infection itself. The fluorescent protein mKate2 and the antibiotic resistance gene chloramphenicol acetyltransferase (CAT) are targeted to generate representative data and to quantify knockdown effectiveness.

Evaluation of RNA extraction methods and identification of putative reference genes for real-time quantitative polymerase chain reaction expression studies on olive (Olea europaea L.) fruits.

PubMed

Nonis, Alberto; Vezzaro, Alice; Ruperti, Benedetto

2012-07-11

Genome wide transcriptomic surveys together with targeted molecular studies are uncovering an ever increasing number of differentially expressed genes in relation to agriculturally relevant processes in olive (Olea europaea L). These data need to be supported by quantitative approaches enabling the precise estimation of transcript abundance. qPCR being the most widely adopted technique for mRNA quantification, preliminary work needs to be done to set up robust methods for extraction of fully functional RNA and for the identification of the best reference genes to obtain reliable quantification of transcripts. In this work, we have assessed different methods for their suitability for RNA extraction from olive fruits and leaves and we have evaluated thirteen potential candidate reference genes on 21 RNA samples belonging to fruit developmental/ripening series and to leaves subjected to wounding. By using two different algorithms, GAPDH2 and PP2A1 were identified as the best reference genes for olive fruit development and ripening, and their effectiveness for normalization of expression of two ripening marker genes was demonstrated.

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection.

PubMed

Sun, Yingqiang; Lu, Chengbo; Li, Xiaobo

2018-05-17

The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes.

Evaluation of stability and validation of reference genes for RT-qPCR expression studies in rice plants under water deficit.

PubMed

Auler, Priscila Ariane; Benitez, Letícia Carvalho; do Amaral, Marcelo Nogueira; Vighi, Isabel Lopes; Dos Santos Rodrigues, Gabriela; da Maia, Luciano Carlos; Braga, Eugenia Jacira Bolacel

2017-05-01

Many studies use strategies that allow for the identification of a large number of genes expressed in response to different stress conditions to which the plant is subjected throughout its cycle. In order to obtain accurate and reliable results in gene expression studies, it is necessary to use reference genes, which must have uniform expression in the majority of cells in the organism studied. RNA isolation of leaves and expression analysis in real-time quantitative polymerase chain reaction (RT-qPCR) were carried out. In this study, nine candidate reference genes were tested, actin 11 (ACT11), ubiquitin conjugated to E2 enzyme (UBC-E2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta tubulin (β-tubulin), eukaryotic initiation factor 4α (eIF-4α), ubiquitin 10 (UBQ10), ubiquitin 5 (UBQ5), aquaporin TIP41 (TIP41-Like) and cyclophilin, in two genotypes of rice, AN Cambará and BRS Querência, with different levels of soil moisture (20%, 10% and recovery) in the vegetative (V5) and reproductive stages (period preceding flowering). Currently, there are different softwares that perform stability analyses and define the most suitable reference genes for a particular study. In this study, we used five different methods: geNorm, BestKeeper, ΔCt method, NormFinder and RefFinder. The results indicate that UBC-E2 and UBQ5 can be used as reference genes in all samples and softwares evaluated. The genes β-tubulin and eIF-4α, traditionally used as reference genes, along with GAPDH, presented lower stability values. The gene expression of basic leucine zipper (bZIP23 and bZIP72) was used to validate the selected reference genes, demonstrating that the use of an inappropriate reference can induce erroneous results.

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants.

PubMed

Kudo, Toru; Sasaki, Yohei; Terashima, Shin; Matsuda-Imai, Noriko; Takano, Tomoyuki; Saito, Misa; Kanno, Maasa; Ozaki, Soichi; Suwabe, Keita; Suzuki, Go; Watanabe, Masao; Matsuoka, Makoto; Takayama, Seiji; Yano, Kentaro

2016-10-13

In quantitative gene expression analysis, normalization using a reference gene as an internal control is frequently performed for appropriate interpretation of the results. Efforts have been devoted to exploring superior novel reference genes using microarray transcriptomic data and to evaluating commonly used reference genes by targeting analysis. However, because the number of specifically detectable genes is totally dependent on probe design in the microarray analysis, exploration using microarray data may miss some of the best choices for the reference genes. Recently emerging RNA sequencing (RNA-seq) provides an ideal resource for comprehensive exploration of reference genes since this method is capable of detecting all expressed genes, in principle including even unknown genes. We report the results of a comprehensive exploration of reference genes using public RNA-seq data from plants such as Arabidopsis thaliana (Arabidopsis), Glycine max (soybean), Solanum lycopersicum (tomato) and Oryza sativa (rice). To select reference genes suitable for the broadest experimental conditions possible, candidates were surveyed by the following four steps: (1) evaluation of the basal expression level of each gene in each experiment; (2) evaluation of the expression stability of each gene in each experiment; (3) evaluation of the expression stability of each gene across the experiments; and (4) selection of top-ranked genes, after ranking according to the number of experiments in which the gene was expressed stably. Employing this procedure, 13, 10, 12 and 21 top candidates for reference genes were proposed in Arabidopsis, soybean, tomato and rice, respectively. Microarray expression data confirmed that the expression of the proposed reference genes under broad experimental conditions was more stable than that of commonly used reference genes. These novel reference genes will be useful for analyzing gene expression profiles across experiments carried out under various experimental conditions.

Comparative analysis of laparoscopic and ultrasound-guided biopsy methods for gene expression analysis in transgenic goats.

PubMed

Melo, C H; Sousa, F C; Batista, R I P T; Sanchez, D J D; Souza-Fabjan, J M G; Freitas, V J F; Melo, L M; Teixeira, D I A

2015-07-31

The present study aimed to compare laparoscopic (LP) and ultrasound-guided (US) biopsy methods to obtain either liver or splenic tissue samples for ectopic gene expression analysis in transgenic goats. Tissue samples were collected from human granulocyte colony stimulating factor (hG-CSF)-transgenic bucks and submitted to real-time PCR for the endogenous genes (Sp1, Baff, and Gapdh) and the transgene (hG-CSF). Both LP and US biopsy methods were successful in obtaining liver and splenic samples that could be analyzed by PCR (i.e., sufficient sample sizes and RNA yield were obtained). Although the number of attempts made to obtain the tissue samples was similar (P > 0.05), LP procedures took considerably longer than the US method (P = 0.03). Finally, transgene transcripts were not detected in spleen or liver samples. Thus, for the phenotypic characterization of a transgenic goat line, investigation of ectopic gene expression can be made successfully by LP or US biopsy, avoiding the traditional approach of euthanasia.

[Study on screening differentially expressed genes in mice livers by silver staining DD-PCR].

PubMed

Luan, Xin-Hong; Hu, Zhong-Ming; Liu, Wei-Quan; Jiang, Yu; Wang, Kai; Wu, Yong-Kui; Li, Qian-Xue

2005-08-01

To screen swimming-fatigue related genes in mice and lay theoretic basis for researching the molecular mechanism of fatigue. 30 male BALB/c mice (20 +/- 2g) were divided into control group, dipping in water group and swimming-fatigue group respectively. After fatigue for swimming in swimming-fatigue group, with control group and dipping in water group, liver tissues in mice were collected. With improved silver staining mRNA differential display method, the differentially expressed genes in mice livers were screened and evaluated by reversed Northern blot. The positive segments were analyzed homology by BLAST. 7 of DD-ESTs were gained. Two of them only expressed in swimming-fatigue group, two down-regulated expressed, and three up-regulated. One of them was a novel gene and was accepted by GenBank, AY615302. Seven DD-ESTs in swimming-fatigue mice were gained by silver staining mRNA differential display method.

Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data.

PubMed

Yip, Shun H; Sham, Pak Chung; Wang, Junwen

2018-02-21

Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

Generation of Envelope-Modified Baculoviruses for Gene Delivery into Mammalian Cells.

PubMed

Hofmann, Christian

2016-01-01

Genetically modified baculoviruses can efficiently deliver and express genes in mammalian cells. The major prerequisite for the expression of a gene transferred by baculovirus is its control by a promoter that is active in mammalian cells. This chapter describes methods for producing second generation baculovirus vectors through modification of their envelope. Envelope modified baculoviruses offer additional new applications of the system, such as their use in in vivo gene delivery, targeting, and vaccination. Methods of generating a recombinant baculovirus vector with a modified envelope and its amplification and purification, including technical scale production, are discussed. A variety of notes give clues regarding specific technical procedures. Finally, methods to analyze the virus and transduction procedures are presented.

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

PubMed Central

Nookaew, Intawat; Papini, Marta; Pornputtapong, Natapol; Scalcinati, Gionata; Fagerberg, Linn; Uhlén, Matthias; Nielsen, Jens

2012-01-01

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation ≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation ≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data. PMID:22965124

Histological staining methods preparatory to laser capture microdissection significantly affect the integrity of the cellular RNA.

PubMed

Wang, Hongyang; Owens, James D; Shih, Joanna H; Li, Ming-Chung; Bonner, Robert F; Mushinski, J Frederic

2006-04-27

Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays. The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases. RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts.

Histological staining methods preparatory to laser capture microdissection significantly affect the integrity of the cellular RNA

PubMed Central

Wang, Hongyang; Owens, James D; Shih, Joanna H; Li, Ming-Chung; Bonner, Robert F; Mushinski, J Frederic

2006-01-01

Background Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays. Results The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases. Conclusion RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts. PMID:16643667

Single-cell transcriptional analysis of taste sensory neuron pair in Caenorhabditis elegans.

PubMed

Takayama, Jun; Faumont, Serge; Kunitomo, Hirofumi; Lockery, Shawn R; Iino, Yuichi

2010-01-01

The nervous system is composed of a wide variety of neurons. A description of the transcriptional profiles of each neuron would yield enormous information about the molecular mechanisms that define morphological or functional characteristics. Here we show that RNA isolation from single neurons is feasible by using an optimized mRNA tagging method. This method extracts transcripts in the target cells by co-immunoprecipitation of the complexes of RNA and epitope-tagged poly(A) binding protein expressed specifically in the cells. With this method and genome-wide microarray, we compared the transcriptional profiles of two functionally different neurons in the main C. elegans gustatory neuron class ASE. Eight of the 13 known subtype-specific genes were successfully detected. Additionally, we identified nine novel genes including a receptor guanylyl cyclase, secreted proteins, a TRPC channel and uncharacterized genes conserved among nematodes, suggesting the two neurons are substantially different than previously thought. The expression of these novel genes was controlled by the previously known regulatory network for subtype differentiation. We also describe unique motif organization within individual gene groups classified by the expression patterns in ASE. Our study paves the way to the complete catalog of the expression profiles of individual C. elegans neurons.

Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development[W][OPEN

PubMed Central

Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-01-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. PMID:25490918

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Key gene regulating cell wall biosynthesis and recalcitrance in Populus, gene Y

DOEpatents

Chen, Jay; Engle, Nancy; Gunter, Lee E.; Jawdy, Sara; Tschaplinski, Timothy J.; Tuskan, Gerald A.

2015-12-08

This disclosure provides methods and transgenic plants for improved production of renewable biofuels and other plant-derived biomaterials by altering the expression and/or activity of Gene Y, an O-acetyltransferase. This disclosure also provides expression vectors containing a nucleic acid (Gene Y) which encodes the polypeptide of SEQ ID NO: 1 and is operably linked to a heterologous promoter.

Polycistronic gene expression in Aspergillus niger.

PubMed

Schuetze, Tabea; Meyer, Vera

2017-09-25

Genome mining approaches predict dozens of biosynthetic gene clusters in each of the filamentous fungal genomes sequenced so far. However, the majority of these gene clusters still remain cryptic because they are not expressed in their natural host. Simultaneous expression of all genes belonging to a biosynthetic pathway in a heterologous host is one approach to activate biosynthetic gene clusters and to screen the metabolites produced for bioactivities. Polycistronic expression of all pathway genes under control of a single and tunable promoter would be the method of choice, as this does not only simplify cloning procedures, but also offers control on timing and strength of expression. However, polycistronic gene expression is a feature not commonly found in eukaryotic host systems, such as Aspergillus niger. In this study, we tested the suitability of the viral P2A peptide for co-expression of three genes in A. niger. Two genes descend from Fusarium oxysporum and are essential to produce the secondary metabolite enniatin (esyn1, ekivR). The third gene (luc) encodes the reporter luciferase which was included to study position effects. Expression of the polycistronic gene cassette was put under control of the Tet-On system to ensure tunable gene expression in A. niger. In total, three polycistronic expression cassettes which differed in the position of luc were constructed and targeted to the pyrG locus in A. niger. This allowed direct comparison of the luciferase activity based on the position of the luciferase gene. Doxycycline-mediated induction of the Tet-On expression cassettes resulted in the production of one long polycistronic mRNA as proven by Northern analyses, and ensured comparable production of enniatin in all three strains. Notably, gene position within the polycistronic expression cassette matters, as, luciferase activity was lowest at position one and had a comparable activity at positions two and three. The P2A peptide can be used to express at least three genes polycistronically in A. niger. This approach can now be applied to heterologously express entire secondary metabolite gene clusters polycistronically or to co-express any genes of interest in equimolar amounts.

FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

PubMed Central

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out. PMID:25602758

FastGCN: a GPU accelerated tool for fast gene co-expression networks.

PubMed

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

Microarray expression profiling identifies genes with altered expression in HDL-deficient mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Callow, Matthew J.; Dudoit, Sandrine; Gong, Elaine L.

2000-05-05

Based on the assumption that severe alterations in the expression of genes known to be involved in HDL metabolism may affect the expression of other genes we screened an array of over 5000 mouse expressed sequence tags (ESTs) for altered gene expression in the livers of two lines of mice with dramatic decreases in HDL plasma concentrations. Labeled cDNA from livers of apolipoprotein AI (apo AI) knockout mice, Scavenger Receptor BI (SR-BI) transgenic mice and control mice were co-hybridized to microarrays. Two-sample t-statistics were used to identify genes with altered expression levels in the knockout or transgenic mice compared withmore » the control mice. In the SR-BI group we found 9 array elements representing at least 5 genes to be significantly altered on the basis of an adjusted p value of less than 0.05. In the apo AI knockout group 8 array elements representing 4 genes were altered compared with the control group (p < 0.05). Several of the genes identified in the SR-BI transgenic suggest altered sterol metabolism and oxidative processes. These studies illustrate the use of multiple-testing methods for the identification of genes with altered expression in replicated microarray experiments of apo AI knockout and SR-BI transgenic mice.« less

Fast and robust group-wise eQTL mapping using sparse graphical models.

PubMed

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.

A Novel Method to Screen for Dominant Negative ATM Mutations in Familial Breast Cancer

DTIC Science & Technology

2005-04-01

carry dominant negative mutation in ATM due to natural variation amongst LCLs. Microarrays have been performed to determine differences in gene expression... genes that are altered in their expression in ATMmutation carriers. The validation of this data in carriers of different ATM mutation indicated that the...heterozygous carriers of T727 1 G mutation display a gene expression phenotype that appears identical to carriers of protein truncating mutations in

Detection of Diurnal Variation of Tomato Transcriptome through the Molecular Timetable Method in a Sunlight-Type Plant Factory.

PubMed

Higashi, Takanobu; Tanigaki, Yusuke; Takayama, Kotaro; Nagano, Atsushi J; Honjo, Mie N; Fukuda, Hirokazu

2016-01-01

The timing of measurement during plant growth is important because many genes are expressed periodically and orchestrate physiological events. Their periodicity is generated by environmental fluctuations as external factors and the circadian clock as the internal factor. The circadian clock orchestrates physiological events such as photosynthesis or flowering and it enables enhanced growth and herbivory resistance. These characteristics have possible applications for agriculture. In this study, we demonstrated the diurnal variation of the transcriptome in tomato (Solanum lycopersicum) leaves through molecular timetable method in a sunlight-type plant factory. Molecular timetable methods have been developed to detect periodic genes and estimate individual internal body time from these expression profiles in mammals. We sampled tomato leaves every 2 h for 2 days and acquired time-course transcriptome data by RNA-Seq. Many genes were expressed periodically and these expressions were stable across the 1st and 2nd days of measurement. We selected 143 time-indicating genes whose expression indicated periodically, and estimated internal time in the plant from these expression profiles. The estimated internal time was generally the same as the external environment time; however, there was a difference of more than 1 h between the two for some sampling points. Furthermore, the stress-responsive genes also showed weakly periodic expression, implying that they were usually expressed periodically, regulated by light-dark cycles as an external factor or the circadian clock as the internal factor, and could be particularly expressed when the plant experiences some specific stress under agricultural situations. This study suggests that circadian clock mediate the optimization for fluctuating environments in the field and it has possibilities to enhance resistibility to stress and floral induction by controlling circadian clock through light supplement and temperature control.

Detection of Diurnal Variation of Tomato Transcriptome through the Molecular Timetable Method in a Sunlight-Type Plant Factory

PubMed Central

Higashi, Takanobu; Tanigaki, Yusuke; Takayama, Kotaro; Nagano, Atsushi J.; Honjo, Mie N.; Fukuda, Hirokazu

2016-01-01

The timing of measurement during plant growth is important because many genes are expressed periodically and orchestrate physiological events. Their periodicity is generated by environmental fluctuations as external factors and the circadian clock as the internal factor. The circadian clock orchestrates physiological events such as photosynthesis or flowering and it enables enhanced growth and herbivory resistance. These characteristics have possible applications for agriculture. In this study, we demonstrated the diurnal variation of the transcriptome in tomato (Solanum lycopersicum) leaves through molecular timetable method in a sunlight-type plant factory. Molecular timetable methods have been developed to detect periodic genes and estimate individual internal body time from these expression profiles in mammals. We sampled tomato leaves every 2 h for 2 days and acquired time-course transcriptome data by RNA-Seq. Many genes were expressed periodically and these expressions were stable across the 1st and 2nd days of measurement. We selected 143 time-indicating genes whose expression indicated periodically, and estimated internal time in the plant from these expression profiles. The estimated internal time was generally the same as the external environment time; however, there was a difference of more than 1 h between the two for some sampling points. Furthermore, the stress-responsive genes also showed weakly periodic expression, implying that they were usually expressed periodically, regulated by light–dark cycles as an external factor or the circadian clock as the internal factor, and could be particularly expressed when the plant experiences some specific stress under agricultural situations. This study suggests that circadian clock mediate the optimization for fluctuating environments in the field and it has possibilities to enhance resistibility to stress and floral induction by controlling circadian clock through light supplement and temperature control. PMID:26904059

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

PubMed Central

Wagner, Florian

2015-01-01

Method Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. Results I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets. PMID:26575370

Minimising Immunohistochemical False Negative ER Classification Using a Complementary 23 Gene Expression Signature of ER Status

PubMed Central

Li, Qiyuan; Eklund, Aron C.; Juul, Nicolai; Haibe-Kains, Benjamin; Workman, Christopher T.; Richardson, Andrea L.; Szallasi, Zoltan; Swanton, Charles

2010-01-01

Background Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold[1], [2]. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome. Methodology/Principal Findings Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer. Conclusion/Significance Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies. PMID:21152022

Electric pulses used in electrochemotherapy and electrogene therapy do not significantly change the expression profile of genes involved in the development of cancer in malignant melanoma cells.

PubMed

Mlakar, Vid; Todorovic, Vesna; Cemazar, Maja; Glavac, Damjan; Sersa, Gregor

2009-08-26

Electroporation is a versatile method for in vitro or in vivo delivery of different molecules into cells. However, no study so far has analysed the effects of electric pulses used in electrochemotherapy (ECT pulses) or electric pulses used in electrogene therapy (EGT pulses) on malignant cells. We studied the effect of ECT and EGT pulses on human malignant melanoma cells in vitro in order to understand and predict the possible effect of electric pulses on gene expression and their possible effect on cell behaviour. We used microarrays with 2698 different oligonucleotides to obtain the expression profile of genes involved in apoptosis and cancer development in a malignant melanoma cell line (SK-MEL28) exposed to ECT pulses and EGT pulses. Cells exposed to ECT pulses showed a 68.8% average survival rate, while cells exposed to EGT pulses showed a 31.4% average survival rate. Only seven common genes were found differentially expressed in cells 16 h after exposure to ECT and EGT pulses. We found that ECT and EGT pulses induce an HSP70 stress response mechanism, repress histone protein H4, a major protein involved in chromatin assembly, and down-regulate components involved in protein synthesis. Our results show that electroporation does not significantly change the expression profile of major tumour suppressor genes or oncogenes of the cell cycle. Moreover, electroporation also does not changes the expression of genes involved in the stability of DNA, supporting current evidence that electroporation is a safe method that does not promote tumorigenesis. However, in spite of being considered an isothermal method, it does to some extent induce stress, which resulted in the expression of the environmental stress response mechanism, HSP70.

[Endoplasmic reticulum stress in INS-1-3 cell associated with the expression changes of MODY gene pathway].

PubMed

Liu, Y T; Li, S R; Wang, Z; Xiao, J Z

2016-09-13

Objective: To profile the gene expression changes associated with endoplasmic reticulum stress in INS-1-3 cells induced by thapsigargin (TG) and tunicamycin (TM). Methods: Normal cultured INS-1-3 cells were used as a control. TG and TM were used to induce endoplasmic reticulum stress in INS-1-3 cells. Digital gene expression profiling technique was used to detect differentially expressed gene. The changes of gene expression were detected by expression pattern clustering analysis, gene ontology (GO) function and pathway enrichment analysis. Real time polymerase chain reaction (RT-PCR) was used to verify the key changes of gene expression. Results: Compared with the control group, there were 57 (45 up-regulated, 12 down-regulated) and 135 (99 up-regulated, 36 down-regulated) differentially expressed genes in TG and TM group, respectively. GO function enrichment analyses indicated that the main enrichment was in the endoplasmic reticulum. In signaling pathway analysis, the identified pathways were related with endoplasmic reticulum stress, antigen processing and presentation, protein export, and most of all, the maturity onset diabetes of the young (MODY) pathway. Conclusion: Under the condition of endoplasmic reticulum stress, the related expression changes of transcriptional factors in MODY signaling pathway may be related with the impaired function in islet beta cells.

[Construction of rAAV2-GPIIb/IIIa vector and test of its expression and function in vitro].

PubMed

Wang, Kai; Peng, Jian-Qiang; Chen, Fang-Ping; Wu, Xiao-Bin

2006-04-01

This study was aimed to explore the possibility of rAAV2 vector-mediating gene therapy for Glanzmann' s thrombasthenia. The rAAV2-GPIIb/IIIa vector was constructed. The GPIIb/IIIa gene expression in mammal cell were examined by different methods, such as: detection of mRNA expression in BHK-21 cells after 24 hours of infection (MOI = 1 x 10(5) v.g/cell) was performed by RT-PCR; the relation between MOI and quantity of GPII6/IIIa gene expression was detected by FACS after 48 hours of infection; GPIIb/IIIa protein expression in BHK-21 cells after 48 hours of infection (MOI = 10(5) v x g/cell) was assayed by Western blot, GPIIb/IIIa protein expression on cell surface was detected by immunofluorescence, and the biological function of expressing product was determined by PAC-1 conjunct experiments. The results showed that GPIIb/IIIa gene expression in mRNA level could be detected in BHK-21 cells after 24 hours of infection at MOI = 1 x 10(5) v x g/cell and the GPIIb/IIIa gene expression in protein level could be detected in BHK-21 cells after 48 hours of infection at MOI = 1 x 10(5) v x g/cell. In certain range, quantity of GPIIb/IIIa gene expression increased with MOI, but overdose of MOI decreased quantity of GPIIb/IIIa gene expression. Activated product of GPIIb/IIIa gene expression could combined with PAC-I, and possesed normal biological function. In conclusion, rAAV2 vactor can effectively mediate GPIIb and GPIIIa gene expressing in mammal cells, and the products of these genes exhibit biological function. This result may provide a basis for application of rAAV2 vector in Glanzmann's thrombasthenia gene therapy in furture.

Optimal consistency in microRNA expression analysis using reference-gene-based normalization.

PubMed

Wang, Xi; Gardiner, Erin J; Cairns, Murray J

2015-05-01

Normalization of high-throughput molecular expression profiles secures differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches. While the same general principles apply to microRNA (miRNA) normalization, there is mounting evidence that global shifts in their expression patterns occur in specific circumstances, which pose a challenge for normalizing miRNA expression data. As an alternative to global normalization, which has the propensity to flatten large trends, normalization against constitutively expressed reference genes presents an advantage through their relative independence. Here we investigated the performance of reference-gene-based (RGB) normalization for differential miRNA expression analysis of microarray expression data, and compared the results with other normalization methods, including: quantile, variance stabilization, robust spline, simple scaling, rank invariant, and Loess regression. The comparative analyses were executed using miRNA expression in tissue samples derived from subjects with schizophrenia and non-psychiatric controls. We proposed a consistency criterion for evaluating methods by examining the overlapping of differentially expressed miRNAs detected using different partitions of the whole data. Based on this criterion, we found that RGB normalization generally outperformed global normalization methods. Thus we recommend the application of RGB normalization for miRNA expression data sets, and believe that this will yield a more consistent and useful readout of differentially expressed miRNAs, particularly in biological conditions characterized by large shifts in miRNA expression.

EMAGE mouse embryo spatial gene expression database: 2010 update

PubMed Central

Richardson, Lorna; Venkataraman, Shanmugasundaram; Stevenson, Peter; Yang, Yiya; Burton, Nicholas; Rao, Jianguo; Fisher, Malcolm; Baldock, Richard A.; Davidson, Duncan R.; Christiansen, Jeffrey H.

2010-01-01

EMAGE (http://www.emouseatlas.org/emage) is a freely available online database of in situ gene expression patterns in the developing mouse embryo. Gene expression domains from raw images are extracted and integrated spatially into a set of standard 3D virtual mouse embryos at different stages of development, which allows data interrogation by spatial methods. An anatomy ontology is also used to describe sites of expression, which allows data to be queried using text-based methods. Here, we describe recent enhancements to EMAGE including: the release of a completely re-designed website, which offers integration of many different search functions in HTML web pages, improved user feedback and the ability to find similar expression patterns at the click of a button; back-end refactoring from an object oriented to relational architecture, allowing associated SQL access; and the provision of further access by standard formatted URLs and a Java API. We have also increased data coverage by sourcing from a greater selection of journals and developed automated methods for spatial data annotation that are being applied to spatially incorporate the genome-wide (∼19 000 gene) ‘EURExpress’ dataset into EMAGE. PMID:19767607

Generation of a foveomacular transcriptome

PubMed Central

Bernstein, Steven; Wong, Paul W.

2014-01-01

Purpose Organizing molecular biologic data is a growing challenge since the rate of data accumulation is steadily increasing. Information relevant to a particular biologic query can be difficult to extract from the comprehensive databases currently available. We present a data collection and organization model designed to ameliorate these problems and applied it to generate an expressed sequence tag (EST)–based foveomacular transcriptome. Methods Using Perl, MySQL, EST libraries, screening, and human foveomacular gene expression as a model system, we generated a foveomacular transcriptome database enriched for molecularly relevant data. Results Using foveomacula as a gene expression model tissue, we identified and organized 6,056 genes expressed in that tissue. Of those identified genes, 3,480 had not been previously described as expressed in the foveomacula. Internal experimental controls as well as comparison of our data set to published data sets suggest we do not yet have a complete description of the foveomacula transcriptome. Conclusions We present an organizational method designed to amplify the utility of data pertinent to a specific research interest. Our method is generic enough to be applicable to a variety of conditions yet focused enough to allow for specialized study. PMID:24991187

Generation of novel pharmacogenomic candidates in the response to methotrexate in juvenile idiopathic arthritis: correlation between gene expression and genotype

PubMed Central

Moncrieffe, Halima; Hinks, Anne; Ursu, Simona; Kassoumeri, Laura; Etheridge, Angela; Hubank, Mike; Martin, Paul; Weiler, Tracey; Glass, David N; Thompson, Susan D.; Thomson, Wendy; Wedderburn, Lucy R

2010-01-01

Objectives Little is known about mechanisms of efficacy of methotrexate (MTX) in childhood arthritis, or genetic influences upon response to MTX. The aims of this study were to use gene expression profiling to identify novel pathways/genes altered by MTX and then investigate these genes for genotype associations with response to MTX treatment. Methods Gene expression profiling before and after MTX treatment was performed on 11 children with juvenile idiopathic arthritis (JIA) treated with MTX, in whom response at 6 months of treatment was defined. Genes showing the most differential gene expression after treatment were selected for SNP genotyping. Genotype frequencies were compared between non-responders and responders (ACR-Ped70). An independent cohort was available for validation. Results Gene expression profiling before and after MTX treatment revealed 1222 differentially expressed probes sets (fold change >1.7, p< 0.05) and 1065 when restricted to full responder cases only. Six highly differentially expressed genes were analysed for genetic association to response to MTX. Three SNPs in the SLC16A7 gene showed significant association with MTX response. One SNP showed validated association in an independent cohort. Conclusions This study is the first, to our knowledge, to evaluate gene expression profiles in children with JIA before and after MTX, and to analyse genetic variation in differentially expressed genes. We have identified a gene which may contribute to genetic variability in MTX response in JIA, and established as proof of principle that genes which are differentially expressed at mRNA level after drug administration may also be good candidates for genetic analysis. PMID:20827233

Comprehensive Evaluation of the Contribution of X Chromosome Genes to Platinum Sensitivity

PubMed Central

Gamazon, Eric R.; Im, Hae Kyung; O’Donnell, Peter H.; Ziliak, Dana; Stark, Amy L.; Cox, Nancy J.; Dolan, M. Eileen; Huang, Rong Stephanie

2011-01-01

Utilizing a genome-wide gene expression dataset generated from Affymetrix GeneChip® Human Exon 1.0ST array, we comprehensively surveyed the role of 322 X chromosome gene expression traits on cellular sensitivity to cisplatin and carboplatin. We identified 31 and 17 X chromosome genes whose expression levels are significantly correlated (after multiple testing correction) with sensitivity to carboplatin and cisplatin, respectively, in the combined HapMap CEU and YRI populations (false discovery rate, FDR<0.05). Of those, 14 overlap for both cisplatin and carboplatin. Employing an independent gene expression quantification method, the Illumina Sentrix Human-6 Expression BeadChip, measured on the same HapMap cell lines, we found that 4 and 2 of these genes are significantly associated with carboplatin and cisplatin sensitivity respectively in both analyses. Two genes, CTPS2 and DLG3, were identified by both genome-wide gene expression analyses as correlated with cellular sensitivity to both platinating agents. The expression of DLG3 gene was also found to correlate with cellular sensitivity to platinating agents in NCI60 cancer cell lines. In addition, we evaluated the role of X chromosome gene expression to the observed differences in sensitivity to the platinums between CEU and YRI derived cell lines. Of the 34 distinct genes significantly correlated with either carboplatin or cisplatin sensitivity, 14 are differentially expressed (defined as p<0.05) between CEU and YRI. Thus, sex chromosome genes play a role in cellular sensitivity to platinating agents and differences in the expression level of these genes are an important source of variation that should be included in comprehensive pharmacogenomic studies. PMID:21252287

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

PubMed

Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

2015-12-01

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

PubMed

Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Pulsed Irradiation Improves Target Selectivity of Infrared Laser-Evoked Gene Operator for Single-Cell Gene Induction in the Nematode C. elegans

PubMed Central

Suzuki, Motoshi; Toyoda, Naoya; Takagi, Shin

2014-01-01

Methods for turning on/off gene expression at the experimenter’s discretion would be useful for various biological studies. Recently, we reported on a novel microscope system utilizing an infrared laser-evoked gene operator (IR-LEGO) designed for inducing heat shock response efficiently in targeted single cells in living organisms without cell damage, thereby driving expression of a transgene under the control of a heat shock promoter. Although the original IR-LEGO can be successfully used for gene induction, several limitations hinder its wider application. Here, using the nematode Caenorhabditis elegans (C. elegans) as a subject, we have made improvements in IR-LEGO. For better spatial control of heating, a pulsed irradiation method using an optical chopper was introduced. As a result, single cells of C. elegans embryos as early as the 2-cell stage and single neurons in ganglia can be induced to express genes selectively. In addition, the introduction of site-specific recombination systems to IR-LEGO enables the induction of gene expression controlled by constitutive and cell type-specific promoters. The strategies adopted here will be useful for future applications of IR-LEGO to other organisms. PMID:24465705

Method for increasing thermostability in cellulase ennzymes

DOEpatents

Adney, W.S.; Thomas, S.R.; Baker, J.O.; Himmel, M.E.; Chou, Y.C.

1998-01-27

The gene encoding Acidothermus cellulolyticus E1 endoglucanase is cloned and expressed in Pichia pastoris. A new modified E1 endoglucanase enzyme comprising the catalytic domain of the full size E1 enzyme demonstrates enhanced thermostability and is produced by two methods. The first method of producing the new modified E1 is proteolytic cleavage to remove the cellulose binding domain and linker peptide of the full size E1. The second method of producing the new modified E1 is genetic truncation of the gene encoding the full size E1 so that the catalytic domain is expressed in the expression product. 8 figs.

Electric pulses used in electrochemotherapy and electrogene therapy do not significantly change the expression profile of genes involved in the development of cancer in malignant melanoma cells

PubMed Central

2009-01-01

Background Electroporation is a versatile method for in vitro or in vivo delivery of different molecules into cells. However, no study so far has analysed the effects of electric pulses used in electrochemotherapy (ECT pulses) or electric pulses used in electrogene therapy (EGT pulses) on malignant cells. We studied the effect of ECT and EGT pulses on human malignant melanoma cells in vitro in order to understand and predict the possible effect of electric pulses on gene expression and their possible effect on cell behaviour. Methods We used microarrays with 2698 different oligonucleotides to obtain the expression profile of genes involved in apoptosis and cancer development in a malignant melanoma cell line (SK-MEL28) exposed to ECT pulses and EGT pulses. Results Cells exposed to ECT pulses showed a 68.8% average survival rate, while cells exposed to EGT pulses showed a 31.4% average survival rate. Only seven common genes were found differentially expressed in cells 16 h after exposure to ECT and EGT pulses. We found that ECT and EGT pulses induce an HSP70 stress response mechanism, repress histone protein H4, a major protein involved in chromatin assembly, and down-regulate components involved in protein synthesis. Conclusion Our results show that electroporation does not significantly change the expression profile of major tumour suppressor genes or oncogenes of the cell cycle. Moreover, electroporation also does not changes the expression of genes involved in the stability of DNA, supporting current evidence that electroporation is a safe method that does not promote tumorigenesis. However, in spite of being considered an isothermal method, it does to some extent induce stress, which resulted in the expression of the environmental stress response mechanism, HSP70. PMID:19709437

Patterns of expression of position-dependent integrated transgenes in mouse embryo.

PubMed Central

Bonnerot, C; Grimber, G; Briand, P; Nicolas, J F

1990-01-01

The abilities to introduce foreign DNA into the genome of mice and to visualize gene expression at the single-cell level underlie a method for defining individual elements of a genetic program. We describe the use of an Escherichia coli lacZ reporter gene fused to the promoter of the gene for hypoxanthine phosphoribosyl transferase that is expressed in all tissues. Most transgenic mice (six of seven) obtained with this construct express the lacZ gene from the hypoxanthine phosphoribosyltransferase promoter. Unexpectedly, however, the expression is temporally and spatially regulated. Each transgenic line is characterized by a specific, highly reproducible pattern of lacZ expression. These results show that, for expression, the integrated construct must be complemented by elements of the genome. These elements exert dominant developmental control on the hypoxanthine phosphoribosyltransferase promoter. The expression patterns in some transgenic mice conform to a typological marker and in others to a subtle combination of typology and topography. These observations define discrete heterogeneities of cell types and of certain structures, particularly in the nervous system and in the mesoderm. This system opens opportunities for developmental studies by providing cellular, molecular, and genetic markers of cell types, cell states, and cells from developmental compartments. Finally this method illustrates that genes transduced or transposed to a different position in the genome acquire different spatiotemporal specificities, a result that has implications for evolution. Images PMID:1696727

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

PubMed Central

Moretti, Stefano; van Leeuwen, Danitsja; Gmuender, Hans; Bonassi, Stefano; van Delft, Joost; Kleinjans, Jos; Patrone, Fioravante; Merlo, Domenico Franco

2008-01-01

Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways. PMID:18764936

Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach

PubMed Central

Shi, Xingjie; Zhao, Qing; Huang, Jian; Xie, Yang; Ma, Shuangge

2015-01-01

Motivation: Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation. Results: A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method. Availability and implementation: R code is available at http://works.bepress.com/shuangge/49/ Contact: shuangge.ma@yale.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26342102

Unstable Expression of Commonly Used Reference Genes in Rat Pancreatic Islets Early after Isolation Affects Results of Gene Expression Studies.

PubMed

Kosinová, Lucie; Cahová, Monika; Fábryová, Eva; Týcová, Irena; Koblas, Tomáš; Leontovyč, Ivan; Saudek, František; Kříž, Jan

2016-01-01

The use of RT-qPCR provides a powerful tool for gene expression studies; however, the proper interpretation of the obtained data is crucially dependent on accurate normalization based on stable reference genes. Recently, strong evidence has been shown indicating that the expression of many commonly used reference genes may vary significantly due to diverse experimental conditions. The isolation of pancreatic islets is a complicated procedure which creates severe mechanical and metabolic stress leading possibly to cellular damage and alteration of gene expression. Despite of this, freshly isolated islets frequently serve as a control in various gene expression and intervention studies. The aim of our study was to determine expression of 16 candidate reference genes and one gene of interest (F3) in isolated rat pancreatic islets during short-term cultivation in order to find a suitable endogenous control for gene expression studies. We compared the expression stability of the most commonly used reference genes and evaluated the reliability of relative and absolute quantification using RT-qPCR during 0-120 hrs after isolation. In freshly isolated islets, the expression of all tested genes was markedly depressed and it increased several times throughout the first 48 hrs of cultivation. We observed significant variability among samples at 0 and 24 hrs but substantial stabilization from 48 hrs onwards. During the first 48 hrs, relative quantification failed to reflect the real changes in respective mRNA concentrations while in the interval 48-120 hrs, the relative expression generally paralleled the results determined by absolute quantification. Thus, our data call into question the suitability of relative quantification for gene expression analysis in pancreatic islets during the first 48 hrs of cultivation, as the results may be significantly affected by unstable expression of reference genes. However, this method could provide reliable information from 48 hrs onwards.

Targeting neuroendocrine differentiation for prostate cancer radiosensitization

DTIC Science & Technology

2017-12-01

Secondary HRP-conjugated antibodies were purchased fromGEHealthcare UK Ltd. (Buckinghamshire, UK). 2.5. RNA isolation and quantitative real- time PCR (qRT...gene expression data using real- time quantitative PCR and the 2(-Delta Delta C(T))Method,Methods 25 (2001) 402–408. [47] T.K. Kelly, T.B. Miranda, G...relative gene expression data using real- time quantitative PCR and the 2(−Delta Delta C(T)) method, Methods 25 (2001) 402–408. [37] J. Ren, L. Wen, X

Implementation of spectral clustering with partitioning around medoids (PAM) algorithm on microarray data of carcinoma

NASA Astrophysics Data System (ADS)

Cahyaningrum, Rosalia D.; Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Technology of microarray became one of the imperative tools in life science to observe the gene expression levels, one of which is the expression of the genes of people with carcinoma. Carcinoma is a cancer that forms in the epithelial tissue. These data can be analyzed such as the identification expressions hereditary gene and also build classifications that can be used to improve diagnosis of carcinoma. Microarray data usually served in large dimension that most methods require large computing time to do the grouping. Therefore, this study uses spectral clustering method which allows to work with any object for reduces dimension. Spectral clustering method is a method based on spectral decomposition of the matrix which is represented in the form of a graph. After the data dimensions are reduced, then the data are partitioned. One of the famous partition method is Partitioning Around Medoids (PAM) which is minimize the objective function with exchanges all the non-medoid points into medoid point iteratively until converge. Objectivity of this research is to implement methods spectral clustering and partitioning algorithm PAM to obtain groups of 7457 genes with carcinoma based on the similarity value. The result in this study is two groups of genes with carcinoma.

Mutual information estimation reveals global associations between stimuli and biological processes

PubMed Central

Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun

2009-01-01

Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155

Relationship between gene expression and GC-content in mammals: statistical significance and biological relevance.

PubMed

Sémon, Marie; Mouchiroud, Dominique; Duret, Laurent

2005-02-01

Mammalian chromosomes are characterized by large-scale variations of DNA base composition (the so-called isochores). In contradiction with previous studies, Lercher et al. (Hum. Mol. Genet., 12, 2411, 2003) recently reported a strong correlation between gene expression breadth and GC-content, suggesting that there might be a selective pressure favoring the concentration of housekeeping genes in GC-rich isochores. We reassessed this issue by examining in human and mouse the correlation between gene expression and GC-content, using different measures of gene expression (EST, SAGE and microarray) and different measures of GC-content. We show that correlations between GC-content and expression are very weak, and may vary according to the method used to measure expression. Such weak correlations have a very low predictive value. The strong correlations reported by Lercher et al. (2003) are because of the fact that they measured variables over neighboring genes windows. We show here that using gene windows artificially enhances the correlation. The assertion that the expression of a given gene depends on the GC-content of the region where it is located is therefore not supported by the data.

Systemic bioinformatics analysis of skeletal muscle gene expression profiles of sepsis

PubMed Central

Yang, Fang; Wang, Yumei

2018-01-01

Sepsis is a type of systemic inflammatory response syndrome with high morbidity and mortality. Skeletal muscle dysfunction is one of the major complications of sepsis that may also influence the outcome of sepsis. The aim of the present study was to explore and identify potential mechanisms and therapeutic targets of sepsis. Systemic bioinformatics analysis of skeletal muscle gene expression profiles from the Gene Expression Omnibus was performed. Differentially expressed genes (DEGs) in samples from patients with sepsis and control samples were screened out using the limma package. Differential co-expression and coregulation (DCE and DCR, respectively) analysis was performed based on the Differential Co-expression Analysis package to identify differences in gene co-expression and coregulation patterns between the control and sepsis groups. Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways of DEGs were identified using the Database for Annotation, Visualization and Integrated Discovery, and inflammatory, cancer and skeletal muscle development-associated biological processes and pathways were identified. DCE and DCR analysis revealed several potential therapeutic targets for sepsis, including genes and transcription factors. The results of the present study may provide a basis for the development of novel therapeutic targets and treatment methods for sepsis. PMID:29805480

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

PubMed Central

Di, Yanming; Schafer, Daniel W.; Wilhelm, Larry J.; Fox, Samuel E.; Sullivan, Christopher M.; Curzon, Aron D.; Carrington, James C.; Mockler, Todd C.; Chang, Jeff H.

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts. PMID:21998647

Validation of internal controls for gene expression analysis in the intestine of rats infected with Hymenolepis diminuta.

PubMed

Hoque, Tafazzal; Bhogal, Meetu; Boghal, Meetu; Webb, Rodney A

2007-12-01

The non-invasive parasitic cestode Hymenolepis diminuta induces hypertrophy, hyperplasia and other changes in cell activity in the intestine of rats which are indicated in the expression of mRNA. We have investigated various house-keeping genes (GAPDH, beta-actin, 18S and HPRT) and other internal controls (total RNA/unit biomass, total RNA/unit length of intestine) to validate gene expression in the rat intestine after cestode infection and drug-induced neuromodulation. Variation in GAPDH, beta-actin, 18S and HPRT expression was observed in rat jejunal tissue according to treatment. Total RNA/unit length of intestine was found to be the most suitable internal control for normalizing target gene mRNA expression in both infected and/or drug-induced rat intestine. This normalization method may be applied to studies of gene expression levels in intestinal tissue where hypertrophy, hyperplasia, rapid growth and cell differentiation generally occur.

New methods for tightly regulated gene expression and highly efficient chromosomal integration of cloned genes for Methanosarcina species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guss, Adam M.; Rother, Michael; Zhang, Jun Kai

A highly efficient method for chromosomal integration of cloned DNA into Methanosarcina spp. was developed utilizing the site-specific recombination system from the Streptomyces phage φC31. Host strains expressing the φC31 integrase gene and carrying an appropriate recombination site can be transformed with non-replicating plasmids carrying the complementary recombination site at efficiencies similar to those obtained with self-replicating vectors. We have also constructed a series of hybrid promoters that combine the highly expressed M. barkeri P mcrB promoter with binding sites for the tetracycline-responsive, bacterial TetR protein. These promoters are tightly regulated by the presence or absence of tetracycline in strainsmore » that express the tetR gene. The hybrid promoters can be used in genetic experiments to test gene essentiality by placing a gene of interest under their control. Thus, growth of strains with tetR -regulated essential genes becomes tetracycline-dependent. A series of plasmid vectors that utilize the site-specific recombination system for construction of reporter gene fusions and for tetracycline regulated expression of cloned genes are reported. These vectors were used to test the efficiency of translation at a variety of start codons. Fusions using an ATG start site were the most active, whereas those using GTG and TTG were approximately one half or one fourth as active, respectively. The CTG fusion was 95% less active than the ATG fusion.« less

New methods for tightly regulated gene expression and highly efficient chromosomal integration of cloned genes for Methanosarcina species

DOE PAGES

Guss, Adam M.; Rother, Michael; Zhang, Jun Kai; ...

2008-01-01

A highly efficient method for chromosomal integration of cloned DNA into Methanosarcina spp. was developed utilizing the site-specific recombination system from the Streptomyces phage φC31. Host strains expressing the φC31 integrase gene and carrying an appropriate recombination site can be transformed with non-replicating plasmids carrying the complementary recombination site at efficiencies similar to those obtained with self-replicating vectors. We have also constructed a series of hybrid promoters that combine the highly expressed M. barkeri P mcrB promoter with binding sites for the tetracycline-responsive, bacterial TetR protein. These promoters are tightly regulated by the presence or absence of tetracycline in strainsmore » that express the tetR gene. The hybrid promoters can be used in genetic experiments to test gene essentiality by placing a gene of interest under their control. Thus, growth of strains with tetR -regulated essential genes becomes tetracycline-dependent. A series of plasmid vectors that utilize the site-specific recombination system for construction of reporter gene fusions and for tetracycline regulated expression of cloned genes are reported. These vectors were used to test the efficiency of translation at a variety of start codons. Fusions using an ATG start site were the most active, whereas those using GTG and TTG were approximately one half or one fourth as active, respectively. The CTG fusion was 95% less active than the ATG fusion.« less

A gene profiling deconvolution approach to estimating immune cell composition from complex tissues.

PubMed

Chen, Shu-Hwa; Kuo, Wen-Yu; Su, Sheng-Yao; Chung, Wei-Chun; Ho, Jen-Ming; Lu, Henry Horng-Shing; Lin, Chung-Yen

2018-05-08

A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a .

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays.

PubMed

McLachlan, G J; Bean, R W; Jones, L Ben-Tovim

2006-07-01

An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

PubMed

Cui, Peng; Zhong, Tingyan; Wang, Zhuo; Wang, Tao; Zhao, Hongyu; Liu, Chenglin; Lu, Hui

2018-06-01

Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene identification algorithms have been developed, large numbers of false positives and low coverage are still major problems in this field. In this study we constructed a novel computational framework for circadian gene identification using deep neural networks (DNN) - a deep learning algorithm which can represent the raw form of data patterns without imposing assumptions on the expression distribution. Firstly, we transformed time-course gene expression data into categorical-state data to denote the changing trend of gene expression. Two distinct expression patterns emerged after clustering of the state data for circadian genes from our manually created learning dataset. DNN was then applied to discriminate the aperiodic genes and the two subtypes of periodic genes. In order to assess the performance of DNN, four commonly used machine learning methods including k-nearest neighbors, logistic regression, naïve Bayes, and support vector machines were used for comparison. The results show that the DNN model achieves the best balanced precision and recall. Next, we conducted large scale circadian gene detection using the trained DNN model for the remaining transcription profiles. Comparing with JTK_CYCLE and a study performed by Möller-Levet et al. (doi: https://doi.org/10.1073/pnas.1217154110), we identified 1132 novel periodic genes. Through the functional analysis of these novel circadian genes, we found that the GTPase superfamily exhibits distinct circadian expression patterns and may provide a molecular switch of circadian control of the functioning of the immune system in human blood. Our study provides novel insights into both the circadian gene identification field and the study of complex circadian-driven biological control. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. Copyright © 2017. Published by Elsevier B.V.

A nonparametric mean-variance smoothing method to assess Arabidopsis cold stress transcriptional regulator CBF2 overexpression microarray data.

PubMed

Hu, Pingsha; Maiti, Tapabrata

2011-01-01

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request.

A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data

PubMed Central

Hu, Pingsha; Maiti, Tapabrata

2011-01-01

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request. PMID:21611181

Comparative analysis of gene expression level by quantitative real-time PCR has limited application in objects with different morphology.

PubMed

Demidenko, Natalia V; Penin, Aleksey A

2012-01-01

qRT-PCR is a generally acknowledged method for gene expression analysis due to its precision and reproducibility. However, it is well known that the accuracy of qRT-PCR data varies greatly depending on the experimental design and data analysis. Recently, a set of guidelines has been proposed that aims to improve the reliability of qRT-PCR. However, there are additional factors that have not been taken into consideration in these guidelines that can seriously affect the data obtained using this method. In this study, we report the influence that object morphology can have on qRT-PCR data. We have used a number of Arabidopsis thaliana mutants with altered floral morphology as models for this study. These mutants have been well characterised (including in terms of gene expression levels and patterns) by other techniques. This allows us to compare the results from the qRT-PCR with the results inferred from other methods. We demonstrate that the comparison of gene expression levels in objects that differ greatly in their morphology can lead to erroneous results.

Allopatric integrations selectively change host transcriptomes, leading to varied expression efficiencies of exotic genes in Myxococcus xanthus.

PubMed

Zhu, Li-Ping; Yue, Xin-Jing; Han, Kui; Li, Zhi-Feng; Zheng, Lian-Shuai; Yi, Xiu-Nan; Wang, Hai-Long; Zhang, You-Ming; Li, Yue-Zhong

2015-07-22

Exotic genes, especially clustered multiple-genes for a complex pathway, are normally integrated into chromosome for heterologous expression. The influences of insertion sites on heterologous expression and allotropic expressions of exotic genes on host remain mostly unclear. We compared the integration and expression efficiencies of single and multiple exotic genes that were inserted into Myxococcus xanthus genome by transposition and attB-site-directed recombination. While the site-directed integration had a rather stable chloramphenicol acetyl transferase (CAT) activity, the transposition produced varied CAT enzyme activities. We attempted to integrate the 56-kb gene cluster for the biosynthesis of antitumor polyketides epothilones into M. xanthus genome by site-direction but failed, which was determined to be due to the insertion size limitation at the attB site. The transposition technique produced many recombinants with varied production capabilities of epothilones, which, however, were not paralleled to the transcriptional characteristics of the local sites where the genes were integrated. Comparative transcriptomics analysis demonstrated that the allopatric integrations caused selective changes of host transcriptomes, leading to varied expressions of epothilone genes in different mutants. With the increase of insertion fragment size, transposition is a more practicable integration method for the expression of exotic genes. Allopatric integrations selectively change host transcriptomes, which lead to varied expression efficiencies of exotic genes.

Standardized Scalp Massage Results in Increased Hair Thickness by Inducing Stretching Forces to Dermal Papilla Cells in the Subcutaneous Tissue

PubMed Central

Kobayashi, Kazuhiro; Hama, Takanori; Murakami, Kasumi; Ogawa, Rei

2016-01-01

Objective: In this study, we evaluated the effect of scalp massage on hair in Japanese males and the effect of stretching forces on human dermal papilla cells in vitro. Methods: Nine healthy men received 4 minutes of standardized scalp massage per day for 24 weeks using a scalp massage device. Total hair number, hair thickness, and hair growth rate were evaluated. The mechanical effect of scalp massage on subcutaneous tissue was analyzed using a finite element method. To evaluate the effect of mechanical forces, human dermal papilla cells were cultured using a 72-hour stretching cycle. Gene expression change was analyzed using DNA microarray analyses. In addition, expression of hair cycle-related genes including IL6, NOGGIN, BMP4, and SMAD4 were evaluated using real-time reverse transcription-polymerase chain reaction. Results: Standardized scalp massage resulted in increased hair thickness 24 weeks after initiation of massage (0.085 ± 0.003 mm vs 0.092 ± 0.001 mm). Finite element method showed that scalp massage caused z-direction displacement and von Mises stress on subcutaneous tissue. In vitro, DNA microarray showed gene expression change significantly compared with nonstretching human dermal papilla cells. A total of 2655 genes were upregulated and 2823 genes were downregulated. Real-time reverse transcription-polymerase chain reaction demonstrated increased expression of hair cycle–related genes such as NOGGIN, BMP4, SMAD4, and IL6ST and decrease in hair loss–related genes such as IL6. Conclusions: Stretching forces result in changes in gene expression in human dermal papilla cells. Standardized scalp massage is a way to transmit mechanical stress to human dermal papilla cells in subcutaneous tissue. Hair thickness was shown to increase with standardized scalp massage. PMID:26904154

Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli

PubMed Central

Li, Meng-Yao; Song, Xiong; Wang, Feng; Xiong, Ai-Sheng

2016-01-01

Parsley, one of the most important vegetables in the Apiaceae family, is widely used in the food, medicinal, and cosmetic industries. Recent studies on parsley mainly focus on its chemical composition, and further research involving the analysis of the plant's gene functions and expressions is required. qPCR is a powerful method for detecting very low quantities of target transcript levels and is widely used to study gene expression. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, four software, namely geNorm, NormFinder, BestKeeper, and RefFinder were used to evaluate the expression stabilities of eight candidate reference genes of parsley (GAPDH, ACTIN, eIF-4α, SAND, UBC, TIP41, EF-1α, and TUB) under various conditions, including abiotic stresses (heat, cold, salt, and drought) and hormone stimuli treatments (GA, SA, MeJA, and ABA). Results showed that EF-1α and TUB were the most stable genes for abiotic stresses, whereas EF-1α, GAPDH, and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1α and TUB were the most stable reference genes among all tested samples, and UBC was the least stable one. Expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study can guide the selection of suitable reference genes in gene expression in parsley. PMID:27746803

Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli.

PubMed

Li, Meng-Yao; Song, Xiong; Wang, Feng; Xiong, Ai-Sheng

2016-01-01

Parsley, one of the most important vegetables in the Apiaceae family, is widely used in the food, medicinal, and cosmetic industries. Recent studies on parsley mainly focus on its chemical composition, and further research involving the analysis of the plant's gene functions and expressions is required. qPCR is a powerful method for detecting very low quantities of target transcript levels and is widely used to study gene expression. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, four software, namely geNorm, NormFinder, BestKeeper, and RefFinder were used to evaluate the expression stabilities of eight candidate reference genes of parsley ( GAPDH, ACTIN, eIF-4 α, SAND, UBC, TIP41, EF-1 α, and TUB ) under various conditions, including abiotic stresses (heat, cold, salt, and drought) and hormone stimuli treatments (GA, SA, MeJA, and ABA). Results showed that EF-1 α and TUB were the most stable genes for abiotic stresses, whereas EF-1 α, GAPDH , and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1 α and TUB were the most stable reference genes among all tested samples, and UBC was the least stable one. Expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study can guide the selection of suitable reference genes in gene expression in parsley.

A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data.

PubMed

Larriba, Yolanda; Rueda, Cristina; Fernández, Miguel A; Peddada, Shyamal D

2018-01-01

Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html.

A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data

PubMed Central

Larriba, Yolanda; Rueda, Cristina; Fernández, Miguel A.; Peddada, Shyamal D.

2018-01-01

Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html PMID:29456555

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

PubMed Central

Qian, Liwei; Zheng, Haoran; Zhou, Hong; Qin, Ruibin; Li, Jinlong

2013-01-01

The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of–the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction. PMID:23516469

Uses of antimicrobial genes from microbial genome

DOEpatents

Sorek, Rotem; Rubin, Edward M.

2013-08-20

We describe a method for mining microbial genomes to discover antimicrobial genes and proteins having broad spectrum of activity. Also described are antimicrobial genes and their expression products from various microbial genomes that were found using this method. The products of such genes can be used as antimicrobial agents or as tools for molecular biology.

DNA ARRAYS TO MONITOR GENE EXPRESSION IN RAT BLOOD AND UTERUS FOLLOWING 17BETA-ESTRADIOL EXPOSURE: BIOMONITORING ENVIRONMENTAL EFFECTS USING SURROGATE TISSUES

EPA Science Inventory

We propose that gene expression changes in accessible tissues such as blood often reflect those in inaccessible tissues, thus offering a convenient biomonitoring method to provide insight into the effects of environmental toxicants on such tissues. In this pilot study, gene expre...

MiRNA-TF-gene network analysis through ranking of biomolecules for multi-informative uterine leiomyoma dataset.

PubMed

Mallik, Saurav; Maulik, Ujjwal

2015-10-01

Gene ranking is an important problem in bioinformatics. Here, we propose a new framework for ranking biomolecules (viz., miRNAs, transcription-factors/TFs and genes) in a multi-informative uterine leiomyoma dataset having both gene expression and methylation data using (statistical) eigenvector centrality based approach. At first, genes that are both differentially expressed and methylated, are identified using Limma statistical test. A network, comprising these genes, corresponding TFs from TRANSFAC and ITFP databases, and targeter miRNAs from miRWalk database, is then built. The biomolecules are then ranked based on eigenvector centrality. Our proposed method provides better average accuracy in hub gene and non-hub gene classifications than other methods. Furthermore, pre-ranked Gene set enrichment analysis is applied on the pathway database as well as GO-term databases of Molecular Signatures Database with providing a pre-ranked gene-list based on different centrality values for comparing among the ranking methods. Finally, top novel potential gene-markers for the uterine leiomyoma are provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Promoter sequence of 3-phosphoglycerate kinase gene 1 of lactic acid-producing fungus rhizopus oryzae and a method of expressing a gene of interest in fungal species

DOEpatents

Gao, Johnway [Richland, WA; Skeen, Rodney S [Pendleton, OR

2002-10-15

The present invention provides the promoter clone discovery of phosphoglycerate kinase gene 1 of a lactic acid-producing filamentous fungal strain, Rhizopus oryzae. The isolated promoter can constitutively regulate gene expression under various carbohydrate conditions. In addition, the present invention also provides a design of an integration vector for the transformation of a foreign gene in Rhizopus oryzae.

Promoter sequence of 3-phosphoglycerate kinase gene 2 of lactic acid-producing fungus rhizopus oryzae and a method of expressing a gene of interest in fungal species

DOEpatents

Gao, Johnway [Richland, WA; Skeen, Rodney S [Pendleton, OR

2003-03-04

The present invention provides the promoter clone discovery of phosphoglycerate kinase gene 2 of a lactic acid-producing filamentous fungal strain, Rhizopus oryzae. The isolated promoter can constitutively regulate gene expression under various carbohydrate conditions. In addition, the present invention also provides a design of an integration vector for the transformation of a foreign gene in Rhizopus oryzae.

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

PubMed

Xu, Kelin; Jin, Li; Xiong, Momiao

2017-05-18

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples. The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

Gene for ataxia-telangiectasia complementation group D (ATDC)

DOEpatents

Murnane, J.P.; Painter, R.B.; Kapp, L.N.; Yu, L.C.

1995-03-07

Disclosed herein is a new gene, an AT gene for complementation group D, the ATDC gene and fragments thereof. Nucleic acid probes for the gene are provided as well as proteins encoded by the gene, cDNA therefrom, preferably a 3 kilobase (kb) cDNA, and recombinant nucleic acid molecules for expression of the proteins. Further disclosed are methods to detect mutations in the gene, preferably methods employing the polymerase chain reaction (PCR). Also disclosed are methods to detect AT genes from other AT complementation groups. 30 figs.

Ontology based molecular signatures for immune cell types via gene expression analysis

PubMed Central

2013-01-01

Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

Infrared laser-mediated local gene induction in medaka, zebrafish and Arabidopsis thaliana.

PubMed

Deguchi, Tomonori; Itoh, Mariko; Urawa, Hiroko; Matsumoto, Tomohiro; Nakayama, Sohei; Kawasaki, Takashi; Kitano, Takeshi; Oda, Shoji; Mitani, Hiroshi; Takahashi, Taku; Todo, Takeshi; Sato, Junichi; Okada, Kiyotaka; Hatta, Kohei; Yuba, Shunsuke; Kamei, Yasuhiro

2009-12-01

Heat shock promoters are powerful tools for the precise control of exogenous gene induction in living organisms. In addition to the temporal control of gene expression, the analysis of gene function can also require spatial restriction. Recently, we reported a new method for in vivo, single-cell gene induction using an infrared laser-evoked gene operator (IR-LEGO) system in living nematodes (Caenorhabditis elegans). It was demonstrated that infrared (IR) irradiation could induce gene expression in single cells without incurring cellular damage. Here, we report the application of IR-LEGO to the small fish, medaka (Japanese killifish; Oryzias latipes) and zebrafish (Danio rerio), and a higher plant (Arabidopsis thaliana). Using easily observable reporter genes, we successfully induced gene expression in various tissues in these living organisms. IR-LEGO has the potential to be a useful tool in extensive research fields for cell/tissue marking or targeted gene expression in local tissues of small fish and plants.

Direct isolation of differentially expressed genes from a specific chromosome region of common wheat: application of the amplified fragment length polymorphism-based mRNA fingerprinting (AMF) method in combination with a deletion line of wheat.

PubMed

Kojima, T; Habu, Y; Iida, S; Ogihara, Y

2000-05-01

The amplified restriction fragment length polymorphism (AFLP)-based mRNA fingerprinting (AMF) method makes it possible systematically and conveniently to identify differentially expressed cDNAs with high reproducibility. We have applied the AMF method to the cloning of the Q gene of common wheat, which is located on the long arm of chromosome 5A and pleiotropically controls the spike morphology and the threshing character of seeds. Using the AMF method, we compared the fingerprints of mRNA samples extracted from the young spikes of Triticum aestivum cv. Chinese Spring (CS) carrying the Q gene to those of a chromosome deletion line of CS, namely, q5, which lacks 15% of 5AL including the Q gene. Approximately 12,200 fragments were produced after PCR with 256 primer combinations. Of these, 92 fragments were differentially expressed between CS and q5. Northern and Southern analyses showed that 16 fragments gave specific or relatively stronger transcript signals in CS, and these clones were present in single copy or in low copy numbers in the wheat genome. Four clones were genetically mapped to the region deleted in q5. Subsequently, one clone, pTaQ22, was mapped at the same locus as the Q gene, indicating that pTaQ22 corresponds to the Q gene or is tightly linked to it. DNA sequence data showed that pTaQ22 had no homology to any known genes, thus suggesting a novel function for this gene in flower morphogenesis. This AMF method might provide a straightforward method for isolating genes in the hexaploid background of common wheat.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

PubMed

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.

Determination of differential gene expression profiles in superficial and deeper zones of mature rat articular cartilage using RNA sequencing of laser microdissected tissue specimens.

PubMed

Mori, Yoshifumi; Chung, Ung-Il; Tanaka, Sakae; Saito, Taku

2014-01-01

Superficial zone (SFZ) cells, which are morphologically and functionally distinct from chondrocytes in deeper zones, play important roles in the maintenance of articular cartilage. Here, we established an easy and reliable method for performance of laser microdissection (LMD) on cryosections of mature rat articular cartilage using an adhesive membrane. We further examined gene expression profiles in the SFZ and the deeper zones of articular cartilage by performing RNA sequencing (RNA-seq). We validated sample collection methods, RNA amplification and the RNA-seq data using real-time RT-PCR. The combined data provide comprehensive information regarding genes specifically expressed in the SFZ or deeper zones, as well as a useful protocol for expression analysis of microsamples of hard tissues.

Brain selective transgene expression in zebrafish using an NRSE derived motif

PubMed Central

Bergeron, Sadie A.; Hannan, Markus C.; Codore, Hiba; Fero, Kandice; Li, Grace H.; Moak, Zachary; Yokogawa, Tohei; Burgess, Harold A.

2012-01-01

Transgenic technologies enable the manipulation and observation of circuits controlling behavior by permitting expression of genetically encoded reporter genes in neurons. Frequently though, neuronal expression is accompanied by transgene expression in non-neuronal tissues, which may preclude key experimental manipulations, including assessment of the contribution of neurons to behavior by ablation. To better restrict transgene expression to the nervous system in zebrafish larvae, we have used DNA sequences derived from the neuron-restrictive silencing element (NRSE). We find that one such sequence, REx2, when used in conjunction with several basal promoters, robustly suppresses transgene expression in non-neuronal tissues. Both in transient transgenic experiments and in stable enhancer trap lines, suppression is achieved without compromising expression within the nervous system. Furthermore, in REx2 enhancer trap lines non-neuronal expression can be de-repressed by knocking down expression of the NRSE binding protein RE1-silencing transcription factor (Rest). In one line, we show that the resulting pattern of reporter gene expression coincides with that of the adjacent endogenous gene, hapln3. We demonstrate that three common basal promoters are susceptible to the effects of the REx2 element, suggesting that this method may be useful for confining expression from many other promoters to the nervous system. This technique enables neural specific targeting of reporter genes and thus will facilitate the use of transgenic methods to manipulate circuit function in freely behaving larvae. PMID:23293587

Gene expression analysis of immunostained endothelial cells isolated from formaldehyde-fixated paraffin embedded tumors using laser capture microdissection--a technical report.

PubMed

Kaneko, Tomoatsu; Okiji, Takashi; Kaneko, Reika; Suda, Hideaki; Nör, Jacques E

2009-12-01

Laser capture microdissection (LCM) allows microscopic procurement of specific cell types from tissue sections that can then be used for gene expression analysis. In conventional LCM, frozen tissues stained with hematoxylin are normally used to the molecular analysis. Recent studies suggested that it is possible to carry out gene expression analysis of formaldehyde-fixated paraffin embedded (FFPE) tissues that were stained with hematoxylin. However, it is still unclear if quantitative gene expression analyses can be performed from LCM cells from FFPE tissues that were subjected to immunostaining to enhance identification of target cells. In this proof-of-principle study, we analyzed by reverse transcription-PCR (RT-PCR) and real time PCR the expression of genes in factor VIII immunostained human endothelial cells that were dissected from FFPE tissues by LCM. We observed that immunostaining should be performed at 4 degrees C to preserve the mRNA from the cells. The expression of Bcl-2 in the endothelial cells was evaluated by RT-PCR and by real time PCR. Glyceraldehyde-3-phosphate dehydrogenase and 18S were used as house keeping genes for RT-PCR and real time PCR, respectively. This report unveils a method for quantitative gene expression analysis in cells that were identified by immunostaining and retrieved by LCM from FFPE tissues. This method is ideally suited for the analysis of relatively rare cell types within a tissue, and should improve on our ability to perform differential diagnosis of pathologies as compared to conventional LCM.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Isolated yeast promoter sequence and a method of regulated heterologous expression

DOEpatents

Gao, Johnway; Skeen, Rodney S.; Hooker, Brian S.; Anderson, Daniel B.

2005-05-31

The present invention provides the promoter clone discovery of a glucoamylase gene of a starch utilizing yeast strain Schwanniomyces castellii. The isolated glucoamylase promoter is an inducible promoter, which can regulate strong gene expression in starch culture medium.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

PubMed

Wen, Qing; Kim, Chang-Sik; Hamilton, Peter W; Zhang, Shu-Dong

2016-05-11

Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective. We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin. In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

Brief Report: A mass spectrometry assay to simultaneously analyze ROS1 and RET fusion gene expression in non-small cell lung cancer

PubMed Central

Wijesinghe, Priyanga; Bepler, Gerold

2014-01-01

Introduction ROS1 and RET gene fusions were recently discovered in non-small cell lung cancer (NSCLC) as potential therapeutic targets with small molecule kinase inhibitors. The conventional screening methods of these fusions are time consuming and require samples of high quality and quantity. Here, we describe a novel and efficient method by coupling the power of multiplexing PCR and the sensitivity of mass spectrometry. Methods The multiplex mass spectrometry platform simultaneously tests samples for the expression of nine ROS1 and six RET fusion genes. The assay incorporates detection of wild-type exon junctions immediately upstream and downstream of the fusion junction to exclude false negative results. To flag false positives, the system also comprises two independent assays for each fusion gene junction. Results The characteristic mass spectrometric peaks of the gene fusions were obtained using engineered plasmid constructs. Specific assays targeting the wild-type gene exon junctions were validated using cDNA from lung tissue of healthy individuals. The system was further validated using cDNA derived from NSCLC cell lines that express endogenous fusion genes. The expressed ROS1-SLC34A2 and CCDC6-RET gene fusions from the NSCLC cell lines HCC78 and LC-2/ad, respectively, were accurately detected by the novel assay. The assay is extremely sensitive, capable of detecting an event in test specimens containing 0.5% positive tumors. Conclusion The novel multiplexed assay is robustly capable of detecting 15 different clinically relevant RET and ROS1 fusion variants. The benefits of this detection method include exceptionally low sample input, high cost efficiency, flexibility, and rapid turnover. PMID:25384172

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards.

PubMed

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Laegreid, Astrid

2007-10-18

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

PubMed Central

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Lægreid, Astrid

2007-01-01

Background The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. Results We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. Conclusion The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish. PMID:17949480

Automatic Control of Gene Expression in Mammalian Cells.

PubMed

Fracassi, Chiara; Postiglione, Lorena; Fiore, Gianfranco; di Bernardo, Diego

2016-04-15

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

Identification and function analysis of contrary genes in Dupuytren's contracture.

PubMed

Ji, Xianglu; Tian, Feng; Tian, Lijie

2015-07-01

The present study aimed to analyze the expression of genes involved in Dupuytren's contracture (DC), using bioinformatic methods. The profile of GSE21221 was downloaded from the gene expression ominibus, which included six samples, derived from fibroblasts and six healthy control samples, derived from carpal-tunnel fibroblasts. A Distributed Intrusion Detection System was used in order to identify differentially expressed genes. The term contrary genes is proposed. Contrary genes were the genes that exhibited opposite expression patterns in the positive and negative groups, and likely exhibited opposite functions. These were identified using Coexpress software. Gene ontology (GO) function analysis was conducted for the contrary genes. A network of GO terms was constructed using the reduce and visualize gene ontology database. Significantly expressed genes (801) and contrary genes (98) were screened. A significant association was observed between Chitinase-3-like protein 1 and ten genes in the positive gene set. Positive regulation of transcription and the activation of nuclear factor-κB (NF-κB)-inducing kinase activity exhibited the highest degree values in the network of GO terms. In the present study, the expression of genes involved in the development of DC was analyzed, and the concept of contrary genes proposed. The genes identified in the present study are involved in the positive regulation of transcription and activation of NF-κB-inducing kinase activity. The contrary genes and GO terms identified in the present study may potentially be used for DC diagnosis and treatment.

Regulatory network involving miRNAs and genes in serous ovarian carcinoma

PubMed Central

Zhao, Haiyan; Xu, Hao; Xue, Luchen

2017-01-01

Serous ovarian carcinoma (SOC) is one of the most life-threatening types of gynecological malignancy, but the pathogenesis of SOC remains unknown. Previous studies have indicated that differentially expressed genes and microRNAs (miRNAs) serve important functions in SOC. However, genes and miRNAs are identified in a disperse form, and limited information is known about the regulatory association between miRNAs and genes in SOC. In the present study, three regulatory networks were hierarchically constructed, including a differentially-expressed network, a related network and a global network to reveal associations between each factor. In each network, there were three types of factors, which were genes, miRNAs and transcription factors that interact with each other. Focus was placed on the differentially-expressed network, in which all genes and miRNAs were differentially expressed and therefore may have affected the development of SOC. Following the comparison and analysis between the three networks, a number of signaling pathways which demonstrated differentially expressed elements were highlighted. Subsequently, the upstream and downstream elements of differentially expressed miRNAs and genes were listed, and a number of key elements (differentially expressed miRNAs, genes and TFs predicted using the P-match method) were analyzed. The differentially expressed network partially illuminated the pathogenesis of SOC. It was hypothesized that if there was no differential expression of miRNAs and genes, SOC may be prevented and treatment may be identified. The present study provided a theoretical foundation for gene therapy for SOC. PMID:29113276

RNA sample preparation applied to gene expression profiling for the horse biological passport.

PubMed

Bailly-Chouriberry, Ludovic; Baudoin, Florent; Cormant, Florence; Glavieux, Yohan; Loup, Benoit; Garcia, Patrice; Popot, Marie-Agnès; Bonnaire, Yves

2017-09-01

The improvement of doping control is an ongoing race. Techniques to fight doping are usually based on the direct detection of drugs or their metabolites by analytical methods such as chromatography hyphenated to mass spectrometry after ad hoc sample preparation. Nowadays, omic methods constitute an attractive development and advances have been achieved particularly by application of molecular biology tools for detection of anabolic androgenic steroids (AAS), erythropoiesis-stimulating agent (ESA), or to control human growth hormone misuses. These interesting results across different animal species have suggested that modification of gene expression offers promising new methods of improving the window of detection of banned substances by targeting their effects on blood cell gene expression. In this context, the present study describes the possibility of using a modified version of the dedicated Human IVD (in vitro Diagnostics) PAXgene® Blood RNA Kit for horse gene expression analysis in blood collected on PAXgene® tubes applied to the horse biological passport. The commercial kit was only approved for human blood samples and has required an optimization of specific technical requirements for equine blood samples. Improvements and recommendations were achieved for sample collection, storage and RNA extraction procedure. Following these developments, RNA yield and quality were demonstrated to be suitable for downstream gene expression analysis by qPCR techniques. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

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.

Stable Reference Gene Selection for RT-qPCR Analysis in Nonviruliferous and Viruliferous Frankliniella occidentalis.

PubMed

Yang, Chunxiao; Li, Hui; Pan, Huipeng; Ma, Yabin; Zhang, Deyong; Liu, Yong; Zhang, Zhanhong; Zheng, Changying; Chu, Dong

2015-01-01

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for measuring and evaluating gene expression during variable biological processes. To facilitate gene expression studies, normalization of genes of interest relative to stable reference genes is crucial. The western flower thrips Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), the main vector of tomato spotted wilt virus (TSWV), is a destructive invasive species. In this study, the expression profiles of 11 candidate reference genes from nonviruliferous and viruliferous F. occidentalis were investigated. Five distinct algorithms, geNorm, NormFinder, BestKeeper, the ΔCt method, and RefFinder, were used to determine the performance of these genes. geNorm, NormFinder, BestKeeper, and RefFinder identified heat shock protein 70 (HSP70), heat shock protein 60 (HSP60), elongation factor 1 α, and ribosomal protein l32 (RPL32) as the most stable reference genes, and the ΔCt method identified HSP60, HSP70, RPL32, and heat shock protein 90 as the most stable reference genes. Additionally, two reference genes were sufficient for reliable normalization in nonviruliferous and viruliferous F. occidentalis. This work provides a foundation for investigating the molecular mechanisms of TSWV and F. occidentalis interactions.

Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

PubMed

Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

2014-01-01

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.

Baculovirus-mediated gene transfer in butterfly wings in vivo: an efficient expression system with an anti-gp64 antibody.

PubMed

Dhungel, Bidur; Ohno, Yoshikazu; Matayoshi, Rie; Otaki, Joji M

2013-03-25

Candidate genes for color pattern formation in butterfly wings have been known based on gene expression patterns since the 1990s, but their functions remain elusive due to a lack of a functional assay. Several methods of transferring and expressing a foreign gene in butterfly wings have been reported, but they have suffered from low success rates or low expression levels. Here, we developed a simple, practical method to efficiently deliver and express a foreign gene using baculovirus-mediated gene transfer in butterfly wings in vivo. A recombinant baculovirus containing a gene for green fluorescent protein (GFP) was injected into pupae of the blue pansy butterfly Junonia orithya (Nymphalidae). GFP fluorescence was detected in the pupal wings and other body parts of the injected individuals three to five days post-injection at various degrees of fluorescence. We obtained a high GFP expression rate at relatively high virus titers, but it was associated with pupal death before color pattern formation in wings. To reduce the high mortality rate caused by the baculovirus treatment, we administered an anti-gp64 antibody, which was raised against baculovirus coat protein gp64, to infected pupae after the baculovirus injection. This treatment greatly reduced the mortality rate of the infected pupae. GFP fluorescence was observed in pupal and adult wings and other body parts of the antibody-treated individuals at various degrees of fluorescence. Importantly, we obtained completely developed wings with a normal color pattern, in which fluorescent signals originated directly from scales or the basal membrane after the removal of scales. GFP fluorescence in wing tissues spatially coincided with anti-GFP antibody staining, confirming that the fluorescent signals originated from the expressed GFP molecules. Our baculovirus-mediated gene transfer system with an anti-gp64 antibody is reasonably efficient, and it can be an invaluable tool to transfer, express, and functionally examine foreign genes in butterfly wings and also in other non-model insect systems.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Dynamic Analysis of Gene Expression in Rice Superior and Inferior Grains by RNA-Seq

PubMed Central

Sun, Hongzheng; Peng, Ting; Zhao, Yafan; Du, Yanxiu; Zhang, Jing; Li, Junzhou; Xin, Zeyu; Zhao, Quanzhi

2015-01-01

Poor grain filling of inferior grains located on lower secondary panicle branch causes great drop in rice yield and quality. Dynamic gene expression patterns between superior and inferior grains were examined from the view of the whole transcriptome by using RNA-Seq method. In total, 19,442 genes were detected during rice grain development. Genes involved in starch synthesis, grain storage and grain development were interrogated in particular in superior and inferior grains. Of the genes involved in sucrose to starch transformation process, most were expressed at lower level in inferior grains at early filling stage compared to that of superior grains. But at late filling stage, the expression of those genes was higher in inferior grains and lower in superior grains. The same trends were observed in the expression of grain storage protein genes. While, evidence that genes involved in cell cycle showed higher expression in inferior grains during whole period of grain filling indicated that cell proliferation was active till the late filling stage. In conclusion, delayed expression of most starch synthesis genes in inferior grains and low capacity of sink organ might be two important factors causing low filling rate of inferior grain at early filling stage, and shortage of carbohydrate supply was a limiting factor at late filling stage. PMID:26355995

Validation of the β-amy1 transcription profiling assay and selection of reference genes suited for a RT-qPCR assay in developing barley caryopsis.

PubMed

Ovesná, Jaroslava; Kučera, Ladislav; Vaculová, Kateřina; Štrymplová, Kamila; Svobodová, Ilona; Milella, Luigi

2012-01-01

Reverse transcription coupled with real-time quantitative PCR (RT-qPCR) is a frequently used method for gene expression profiling. Reference genes (RGs) are commonly employed to normalize gene expression data. A limited information exist on the gene expression and profiling in developing barley caryopsis. Expression stability was assessed by measuring the cycle threshold (Ct) range and applying both the GeNorm (pair-wise comparison of geometric means) and Normfinder (model-based approach) principles for the calculation. Here, we have identified a set of four RGs suitable for studying gene expression in the developing barley caryopsis. These encode the proteins GAPDH, HSP90, HSP70 and ubiquitin. We found a correlation between the frequency of occurrence of a transcript in silico and its suitability as an RG. This set of RGs was tested by comparing the normalized level of β-amylase (β-amy1) transcript with directly measured quantities of the BMY1 gene product in the developing barley caryopsis. This panel of genes could be used for other gene expression studies, as well as to optimize β-amy1 analysis for study of the impact of β-amy1 expression upon barley end-use quality.

3D confocal reconstruction of gene expression in mouse.

PubMed

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.

Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

PubMed Central

Schena, M; Shalon, D; Heller, R; Chai, A; Brown, P O; Davis, R W

1996-01-01

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery. Images Fig. 1 Fig. 2 Fig. 3 PMID:8855227

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods.

PubMed

Wang, Liming; Zhu, L; Luan, R; Wang, L; Fu, J; Wang, X; Sui, L

2016-10-10

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM.

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods

PubMed Central

Wang, Liming; Zhu, L.; Luan, R.; Wang, L.; Fu, J.; Wang, X.; Sui, L.

2016-01-01

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM. PMID:27737314

Comparison of quantitative real-time polymerase chain reaction with NanoString® methodology using adipose and liver tissues from rats fed seaweed.

PubMed

Bentley-Hewitt, Kerry L; Hedderley, Duncan I; Monro, John; Martell, Sheridan; Smith, Hannah; Mishra, Suman

2016-05-25

Experimental methods are constantly being improved by new technology. Recently a new technology, NanoString®, has been introduced to the market for the analysis of gene expression. Our experiments used adipose and liver samples collected from a rat feeding trial to explore gene expression changes resulting from a diet of 7.5% seaweed. Both quantitative real-time polymerase chain reaction (qPCR) and NanoString methods were employed to look at expression of genes related to fat and glucose metabolism and this paper compares results from both methods. We conclude that NanoString offers a valuable alternative to qPCR and our data suggest that results are more accurate because of the reduced sample handling and direct quantification of gene copy number without the need for enzymatic amplification. However, we have highlighted a potential challenge for both methods, which needs to be addressed when designing primers or probes. We suggest a literature search for known splice variants of a particular gene to be completed so that primers or probes can be designed that do not span exons which may be affected by alternative gene sequences. Copyright © 2016 Elsevier B.V. All rights reserved.

Missing value imputation for gene expression data by tailored nearest neighbors.

PubMed

Faisal, Shahla; Tutz, Gerhard

2017-04-25

High dimensional data like gene expression and RNA-sequences often contain missing values. The subsequent analysis and results based on these incomplete data can suffer strongly from the presence of these missing values. Several approaches to imputation of missing values in gene expression data have been developed but the task is difficult due to the high dimensionality (number of genes) of the data. Here an imputation procedure is proposed that uses weighted nearest neighbors. Instead of using nearest neighbors defined by a distance that includes all genes the distance is computed for genes that are apt to contribute to the accuracy of imputed values. The method aims at avoiding the curse of dimensionality, which typically occurs if local methods as nearest neighbors are applied in high dimensional settings. The proposed weighted nearest neighbors algorithm is compared to existing missing value imputation techniques like mean imputation, KNNimpute and the recently proposed imputation by random forests. We use RNA-sequence and microarray data from studies on human cancer to compare the performance of the methods. The results from simulations as well as real studies show that the weighted distance procedure can successfully handle missing values for high dimensional data structures where the number of predictors is larger than the number of samples. The method typically outperforms the considered competitors.

Toll like receptors gene expression of human keratinocytes cultured of severe burn injury.

PubMed

Cornick, Sarita Mac; Noronha, Silvana Aparecida Alves Corrêa de; Noronha, Samuel Marcos Ribeiro de; Cezillo, Marcus V B; Ferreira, Lydia Masako; Gragnani, Alfredo

2014-01-01

To evaluate the expression profile of genes related to Toll Like Receptors (TLR) pathways of human Primary Epidermal keratinocytes of patients with severe burns. After obtaining viable fragments of skin with and without burning, culture hKEP was initiated by the enzymatic method using Dispase (Sigma-Aldrich). These cells were treated with Trizol(r) (Life Technologies) for extraction of total RNA. This was quantified and analyzed for purity for obtaining cDNA for the analysis of gene expression using specific TLR pathways PCR Arrays plates (SA Biosciences). After the analysis of gene expression we found that 21% of these genes were differentially expressed, of which 100% were repressed or hyporegulated. Among these, the following genes (fold decrease): HSPA1A (-58), HRAS (-36), MAP2K3 (-23), TOLLIP (-23), RELA (-18), FOS (-16), and TLR1 (-6.0). This study contributes to the understanding of the molecular mechanisms related to TLR pathways and underlying wound infection caused by the burn. Furthermore, it may provide new strategies to restore normal expression of these genes and thereby change the healing process and improve clinical outcome.

Arctigenin from Arctium lappa inhibits interleukin-2 and interferon gene expression in primary human T lymphocytes.

PubMed

Tsai, Wei-Jern; Chang, Chu-Ting; Wang, Guei-Jane; Lee, Tzong-Huei; Chang, Shwu-Fen; Lu, Shao-Chun; Kuo, Yuh-Chi

2011-03-25

Arctium lappa (Niubang), a Chinese herbal medicine, is used to treat tissue inflammation. This study investigates the effects of arctigenin (AC), isolated from A. lappa, on anti-CD3/CD28 Ab-stimulated cell proliferation and cytokine gene expression in primary human T lymphocytes. Cell proliferation was determined with enzyme immunoassays and the tritiated thymidine uptake method. Cytokine production and gene expression were analyzed with reverse transcription-polymerase chain reaction. AC inhibited primary human T lymphocytes proliferation activated by anti-CD3/CD28 Ab. Cell viability test indicated that the inhibitory effects of AC on primary human T lymphocyte proliferation were not due to direct cytotoxicity. AC suppressed interleukin-2 (IL-2) and interferon-γ (IFN-γ) production in a concentration-dependent manner. Furthermore, AC decreased the IL-2 and IFN-γ gene expression in primary human T lymphocytes induced by anti-CD3/CD28 Ab. Reporter gene analyses revealed that AC decreased NF-AT-mediated reporter gene expression. AC inhibited T lymphocyte proliferation and decreased the gene expression of IL-2, IFN-γ and NF-AT.

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.

PubMed

Zhang, Wensheng; Edwards, Andrea; Fan, Wei; Zhu, Dongxiao; Zhang, Kun

2010-06-22

Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods. Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (SVD-based Pattern Pairing and Chart Splitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W118 of M. drosophila. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering. svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.

Silver nanoparticles administered to chicken affect VEGFA and FGF2 gene expression in breast muscle and heart

NASA Astrophysics Data System (ADS)

Hotowy, Anna; Sawosz, Ewa; Pineda, Lane; Sawosz, Filip; Grodzik, Marta; Chwalibog, André

2012-07-01

Nanoparticles of colloidal silver (AgNano) can influence gene expression. Concerning trials of AgNano application in poultry nutrition, it is useful to reveal whether they affect the expression of genes crucial for bird development. AgNano were administered to broiler chickens as a water solution in two concentrations (10 and 20 ppm). After dissection of the birds, breast muscles and hearts were collected. Gene expression of FGF2 and VEGFA on the mRNA and protein levels were evaluated using quantitative polymerase chain reaction and enzyme-linked immunosorbent assay methods. The results for gene expression in the breast muscle revealed changes on the mRNA level ( FGF2 was up-regulated, P < 0.05) but not on the protein level. In the heart, 20 ppm of silver nanoparticles in drinking water increased the expression of VEGFA ( P < 0.05), at the same time decreasing FGF2 expression both on the transcriptional and translational levels. Changes in the expression of these genes may lead to histological changes, but this needs to be proven using histological and immunohistochemical examination of tissues. In general, we showed that AgNano application in poultry feeding influences the expression of FGF2 and VEGFA genes on the mRNA and protein levels in growing chicken.

Anterior-posterior regionalized gene expression in the Ciona notochord

PubMed Central

Veeman, Michael

2014-01-01

Background In the simple ascidian chordate Ciona the signaling pathways and gene regulatory networks giving rise to initial notochord induction are largely understood and the mechanisms of notochord morphogenesis are being systematically elucidated. The notochord has generally been thought of as a non-compartmentalized or regionalized organ that is not finely patterned at the level of gene expression. Quantitative imaging methods have recently shown, however, that notochord cell size, shape and behavior vary consistently along the anterior-posterior (AP) axis. Results Here we screen candidate genes by whole mount in situ hybridization for potential AP asymmetry. We identify 4 genes that show non-uniform expression in the notochord. Ezrin/radixin/moesin (ERM) is expressed more strongly in the secondary notochord lineage than the primary. CTGF is expressed stochastically in a subset of notochord cells. A novel calmodulin-like gene (BCamL) is expressed more strongly at both the anterior and posterior tips of the notochord. A TGF-β ortholog is expressed in a gradient from posterior to anterior. The asymmetries in ERM, BCamL and TGF-β expression are evident even before the notochord cells have intercalated into a single-file column. Conclusions We conclude that the Ciona notochord is not a homogeneous tissue but instead shows distinct patterns of regionalized gene expression. PMID:24288133

Anterior-posterior regionalized gene expression in the Ciona notochord.

PubMed

Reeves, Wendy; Thayer, Rachel; Veeman, Michael

2014-04-01

In the simple ascidian chordate Ciona, the signaling pathways and gene regulatory networks giving rise to initial notochord induction are largely understood and the mechanisms of notochord morphogenesis are being systematically elucidated. The notochord has generally been thought of as a non-compartmentalized or regionalized organ that is not finely patterned at the level of gene expression. Quantitative imaging methods have recently shown, however, that notochord cell size, shape, and behavior vary consistently along the anterior-posterior (AP) axis. Here we screen candidate genes by whole mount in situ hybridization for potential AP asymmetry. We identify 4 genes that show non-uniform expression in the notochord. Ezrin/radixin/moesin (ERM) is expressed more strongly in the secondary notochord lineage than the primary. CTGF is expressed stochastically in a subset of notochord cells. A novel calmodulin-like gene (BCamL) is expressed more strongly at both the anterior and posterior tips of the notochord. A TGF-β ortholog is expressed in a gradient from posterior to anterior. The asymmetries in ERM, BCamL, and TGF-β expression are evident even before the notochord cells have intercalated into a single-file column. We conclude that the Ciona notochord is not a homogeneous tissue but instead shows distinct patterns of regionalized gene expression. Copyright © 2013 Wiley Periodicals, Inc.

Differential co-expression analysis of a microarray gene expression profiles of pulmonary adenocarcinoma.

PubMed

Fu, Shijie; Pan, Xufeng; Fang, Wentao

2014-08-01

Lung cancer severely reduces the quality of life worldwide and causes high socioeconomic burdens. However, key genes leading to the generation of pulmonary adenocarcinoma remain elusive despite intensive research efforts. The present study aimed to identify the potential associations between transcription factors (TFs) and differentially co‑expressed genes (DCGs) in the regulation of transcription in pulmonary adenocarcinoma. Gene expression profiles of pulmonary adenocarcinoma were downloaded from the Gene Expression Omnibus, and gene expression was analyzed using a computational method. A total of 37,094 differentially co‑expressed links (DCLs) and 251 DCGs were identified, which were significantly enriched in 10 pathways. The construction of the regulatory network and the analysis of the regulatory impact factors revealed eight crucial TFs in the regulatory network. These TFs regulated the expression of DCGs by promoting or inhibiting their expression. In addition, certain TFs and target genes associated with DCGs did not appear in the DCLs, which indicated that those TFs could be synergistic with other factors. This is likely to provide novel insights for research into pulmonary adenocarcinoma. In conclusion, the present study may enhance the understanding of disease mechanisms and lead to an improved diagnosis of lung cancer. However, further studies are required to confirm these observations.

Validation of reference genes for quantitative RT-PCR studies of gene expression in perennial ryegrass (Lolium perenne L.)

PubMed Central

2010-01-01

Background Perennial ryegrass (Lolium perenne L.) is an important pasture and turf crop. Biotechniques such as gene expression studies are being employed to improve traits in this temperate grass. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is among the best methods available for determining changes in gene expression. Before analysis of target gene expression, it is essential to select an appropriate normalisation strategy to control for non-specific variation between samples. Reference genes that have stable expression at different biological and physiological states can be effectively used for normalisation; however, their expression stability must be validated before use. Results Existing Serial Analysis of Gene Expression data were queried to identify six moderately expressed genes that had relatively stable gene expression throughout the year. These six candidate reference genes (eukaryotic elongation factor 1 alpha, eEF1A; TAT-binding protein homolog 1, TBP-1; eukaryotic translation initiation factor 4 alpha, eIF4A; YT521-B-like protein family protein, YT521-B; histone 3, H3; ubiquitin-conjugating enzyme, E2) were validated for qRT-PCR normalisation in 442 diverse perennial ryegrass (Lolium perenne L.) samples sourced from field- and laboratory-grown plants under a wide range of experimental conditions. Eukaryotic EF1A is encoded by members of a multigene family exhibiting differential expression and necessitated the expression analysis of different eEF1A encoding genes; a highly expressed eEF1A (h), a moderately, but stably expressed eEF1A (s), and combined expression of multigene eEF1A (m). NormFinder identified eEF1A (s) and YT521-B as the best combination of two genes for normalisation of gene expression data in perennial ryegrass following different defoliation management in the field. Conclusions This study is unique in the magnitude of samples tested with the inclusion of numerous field-grown samples, helping pave the way to conduct gene expression studies in perennial biomass crops under field-conditions. From our study several stably expressed reference genes have been validated. This provides useful candidates for reference gene selection in perennial ryegrass under conditions other than those tested here. PMID:20089196

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

PubMed

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Dynamic CRM occupancy reflects a temporal map of developmental progression.

PubMed

Wilczyński, Bartek; Furlong, Eileen E M

2010-06-22

Development is driven by tightly coordinated spatio-temporal patterns of gene expression, which are initiated through the action of transcription factors (TFs) binding to cis-regulatory modules (CRMs). Although many studies have investigated how spatial patterns arise, precise temporal control of gene expression is less well understood. Here, we show that dynamic changes in the timing of CRM occupancy is a prevalent feature common to all TFs examined in a developmental ChIP time course to date. CRMs exhibit complex binding patterns that cannot be explained by the sequence motifs or expression of the TFs themselves. The temporal changes in TF binding are highly correlated with dynamic patterns of target gene expression, which in turn reflect transitions in cellular function during different stages of development. Thus, it is not only the timing of a TF's expression, but also its temporal occupancy in refined time windows, which determines temporal gene expression. Systematic measurement of dynamic CRM occupancy may therefore serve as a powerful method to decode dynamic changes in gene expression driving developmental progression.

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.

PubMed

Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro

2015-12-01

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. Copyright © 2015. Published by Elsevier Ltd.

A laser pointer driven microheater for precise local heating and conditional gene regulation in vivo. Microheater driven gene regulation in zebrafish.

PubMed

Placinta, Mike; Shen, Meng-Chieh; Achermann, Marc; Karlstrom, Rolf O

2009-12-30

Tissue heating has been employed to study a variety of biological processes, including the study of genes that control embryonic development. Conditional regulation of gene expression is a particularly powerful approach for understanding gene function. One popular method for mis-expressing a gene of interest employs heat-inducible heat shock protein (hsp) promoters. Global heat shock of hsp-promoter-containing transgenic animals induces gene expression throughout all tissues, but does not allow for spatial control. Local heating allows for spatial control of hsp-promoter-driven transgenes, but methods for local heating are cumbersome and variably effective. We describe a simple, highly controllable, and versatile apparatus for heating biological tissue and other materials on the micron-scale. This microheater employs micron-scale fiber optics and uses an inexpensive laser-pointer as a power source. Optical fibers can be pulled on a standard electrode puller to produce tips of varying sizes that can then be used to reliably heat 20-100 mum targets. We demonstrate precise spatiotemporal control of hsp70l:GFP transgene expression in a variety of tissue types in zebrafish embryos and larvae. We also show how this system can be employed as part of a new method for lineage tracing that would greatly facilitate the study of organogenesis and tissue regulation at any time in the life cycle. This versatile and simple local heater has broad utility for the study of gene function and for lineage tracing. This system could be used to control hsp-driven gene expression in any organism simply by bringing the fiber optic tip in contact with the tissue of interest. Beyond these uses for the study of gene function, this device has wide-ranging utility in materials science and could easily be adapted for therapeutic purposes in humans.

Discretization provides a conceptually simple tool to build expression networks.

PubMed

Vass, J Keith; Higham, Desmond J; Mudaliar, Manikhandan A V; Mao, Xuerong; Crowther, Daniel J

2011-04-18

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes "microarray" data using known relationships which are successfully identified by our method.We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic--identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.

Bayesian approach to transforming public gene expression repositories into disease diagnosis databases.

PubMed

Huang, Haiyan; Liu, Chun-Chi; Zhou, Xianghong Jasmine

2010-04-13

The rapid accumulation of gene expression data has offered unprecedented opportunities to study human diseases. The National Center for Biotechnology Information Gene Expression Omnibus is currently the largest database that systematically documents the genome-wide molecular basis of diseases. However, thus far, this resource has been far from fully utilized. This paper describes the first study to transform public gene expression repositories into an automated disease diagnosis database. Particularly, we have developed a systematic framework, including a two-stage Bayesian learning approach, to achieve the diagnosis of one or multiple diseases for a query expression profile along a hierarchical disease taxonomy. Our approach, including standardizing cross-platform gene expression data and heterogeneous disease annotations, allows analyzing both sources of information in a unified probabilistic system. A high level of overall diagnostic accuracy was shown by cross validation. It was also demonstrated that the power of our method can increase significantly with the continued growth of public gene expression repositories. Finally, we showed how our disease diagnosis system can be used to characterize complex phenotypes and to construct a disease-drug connectivity map.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Monoallelic Gene Expression in Mammals.

PubMed

Chess, Andrew

2016-11-23

Monoallelic expression not due to cis-regulatory sequence polymorphism poses an intriguing problem in epigenetics because it requires the unequal treatment of two segments of DNA that are present in the same nucleus and that can indeed have absolutely identical sequences. Here, I focus on a few recent developments in the field of monoallelic expression that are of particular interest and raise interesting questions for future work. One development is regarding analyses of imprinted genes, in which recent work suggests the possibility that intriguing networks of imprinted genes exist and are important for genetic and physiological studies. Another issue that has been raised in recent years by a number of publications is the question of how skewed allelic expression should be for it to be designated as monoallelic expression and, further, what methods are appropriate or inappropriate for analyzing genomic data to examine allele-specific expression. Perhaps the most exciting recent development in mammalian monoallelic expression is a clever and carefully executed analysis of genetic diversity of autosomal genes subject to random monoallelic expression (RMAE), which provides compelling evidence for distinct evolutionary forces acting on random monoallelically expressed genes.

Gene expression profiles in peripheral blood mononuclear cells of Chinese nickel refinery workers with high exposures to nickel and control subjects

PubMed Central

Arita, Adriana; Muñoz, Alexandra; Chervona, Yana; Niu, Jingping; Qu, Qingshan; Zhao, Najuan; Ruan, Ye; Kiok, Kathrin; Kluz, Thomas; Sun, Hong; Clancy, Hailey A.; Shamy, Magdy; Costa, Max

2012-01-01

Background Occupational exposure to nickel (Ni) is associated with an increased risk of lung and nasal cancers. Ni compounds exhibit weak mutagenic activity, alter the cell’s epigenetic homeostasis, and activate signaling pathways. However, changes in gene expression associated with Ni exposure have only been investigated in vitro. This study was conducted in a Chinese population to determine whether occupational exposure to Ni was associated with differential gene expression profiles in the peripheral blood mononuclear cells (PBMCs) of Ni-refinery workers when compared to referents. Methods Eight Ni-refinery workers and ten referents were selected. PBMC RNA was extracted and gene expression profiling was performed using Affymetrix exon arrays. Differentially expressed genes between both groups were identified in a global analysis. Results There were a total of 2756 differentially expressed genes (DEG) in the Ni-refinery workers relative to the control subjects (FDR adjusted p<0.05) with 770 up-regulated genes and 1986 down-regulated genes. DNA repair and epigenetic genes were significantly overrepresented (p< 0.0002) among the DEG. Of 31 DNA repair genes, 29 were repressed in the high exposure group and two were overexpressed. Of the 16 epigenetic genes 12 were repressed in the high exposure group and 4 were overexpressed. Conclusions The results of this study indicate that occupational exposure to Ni is associated with alterations in gene expression profiles in PBMCs of subjects. Impact Gene expression may be useful in identifying patterns of deregulation that precede clinical identification of Ni-induced cancers. PMID:23195993

Genomics of Mature and Immature Olfactory Sensory Neurons

PubMed Central

Nickell, Melissa D.; Breheny, Patrick; Stromberg, Arnold J.; McClintock, Timothy S.

2014-01-01

The continuous replacement of neurons in the olfactory epithelium provides an advantageous model for investigating neuronal differentiation and maturation. By calculating the relative enrichment of every mRNA detected in samples of mature mouse olfactory sensory neurons (OSNs), immature OSNs, and the residual population of neighboring cell types, and then comparing these ratios against the known expression patterns of >300 genes, enrichment criteria that accurately predicted the OSN expression patterns of nearly all genes were determined. We identified 847 immature OSN-specific and 691 mature OSN-specific genes. The control of gene expression by chromatin modification and transcription factors, and neurite growth, protein transport, RNA processing, cholesterol biosynthesis, and apoptosis via death domain receptors, were overrepresented biological processes in immature OSNs. Ion transport (ion channels), presynaptic functions, and cilia-specific processes were overrepresented in mature OSNs. Processes overrepresented among the genes expressed by all OSNs were protein and ion transport, ER overload response, protein catabolism, and the electron transport chain. To more accurately represent gradations in mRNA abundance and identify all genes expressed in each cell type, classification methods were used to produce probabilities of expression in each cell type for every gene. These probabilities, which identified 9,300 genes expressed in OSNs, were 96% accurate at identifying genes expressed in OSNs and 86% accurate at discriminating genes specific to mature and immature OSNs. This OSN gene database not only predicts the genes responsible for the major biological processes active in OSNs, but also identifies thousands of never before studied genes that support OSN phenotypes. PMID:22252456

Purification and differentiation of human adipose-derived stem cells by membrane filtration and membrane migration methods

PubMed Central

Lin, Hong Reng; Heish, Chao-Wen; Liu, Cheng-Hui; Muduli, Saradaprasan; Li, Hsing-Fen; Higuchi, Akon; Kumar, S. Suresh; Alarfaj, Abdullah A.; Munusamy, Murugan A.; Hsu, Shih-Tien; Chen, Da-Chung; Benelli, Giovanni; Murugan, Kadarkarai; Cheng, Nai-Chen; Wang, Han-Chow; Wu, Gwo-Jang

2017-01-01

Human adipose-derived stem cells (hADSCs) are easily isolated from fat tissue without ethical concerns, but differ in purity, pluripotency, differentiation ability, and stem cell marker expression, depending on the isolation method. We isolated hADSCs from a primary fat tissue solution using: (1) conventional culture, (2) a membrane filtration method, (3) a membrane migration method where the primary cell solution was permeated through membranes, adhered hADSCs were cultured, and hADSCs migrated out from the membranes. Expression of mesenchymal stem cell markers and pluripotency genes, and osteogenic differentiation were compared for hADSCs isolated by different methods using nylon mesh filter membranes with pore sizes ranging from 11 to 80 μm. hADSCs isolated by the membrane migration method had the highest MSC surface marker expression and efficient differentiation into osteoblasts. Osteogenic differentiation ability of hADSCs and MSC surface marker expression were correlated, but osteogenic differentiation ability and pluripotent gene expression were not. PMID:28071738

Domain selection combined with improved cloning strategy for high throughput expression of higher eukaryotic proteins

PubMed Central

Chen, Yunjia; Qiu, Shihong; Luan, Chi-Hao; Luo, Ming

2007-01-01

Background Expression of higher eukaryotic genes as soluble, stable recombinant proteins is still a bottleneck step in biochemical and structural studies of novel proteins today. Correct identification of stable domains/fragments within the open reading frame (ORF), combined with proper cloning strategies, can greatly enhance the success rate when higher eukaryotic proteins are expressed as these domains/fragments. Furthermore, a HTP cloning pipeline incorporated with bioinformatics domain/fragment selection methods will be beneficial to studies of structure and function genomics/proteomics. Results With bioinformatics tools, we developed a domain/domain boundary prediction (DDBP) method, which was trained by available experimental data. Combined with an improved cloning strategy, DDBP had been applied to 57 proteins from C. elegans. Expression and purification results showed there was a 10-fold increase in terms of obtaining purified proteins. Based on the DDBP method, the improved GATEWAY cloning strategy and a robotic platform, we constructed a high throughput (HTP) cloning pipeline, including PCR primer design, PCR, BP reaction, transformation, plating, colony picking and entry clones extraction, which have been successfully applied to 90 C. elegans genes, 88 Brucella genes, and 188 human genes. More than 97% of the targeted genes were obtained as entry clones. This pipeline has a modular design and can adopt different operations for a variety of cloning/expression strategies. Conclusion The DDBP method and improved cloning strategy were satisfactory. The cloning pipeline, combined with our recombinant protein HTP expression pipeline and the crystal screening robots, constitutes a complete platform for structure genomics/proteomics. This platform will increase the success rate of purification and crystallization dramatically and promote the further advancement of structure genomics/proteomics. PMID:17663785

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions.

PubMed

Zhou, Weichen; Ma, Yanyun; Zhang, Jun; Hu, Jingyi; Zhang, Menghan; Wang, Yi; Li, Yi; Wu, Lijun; Pan, Yida; Zhang, Yitong; Zhang, Xiaonan; Zhang, Xinxin; Zhang, Zhanqing; Zhang, Jiming; Li, Hai; Lu, Lungen; Jin, Li; Wang, Jiucun; Yuan, Zhenghong; Liu, Jie

2017-11-01

Liver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus-infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)-infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV-DNA) in large-scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions. We analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine-learning methods including Random Forest, K-nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model. Significant genes related to clinical parameters were found enriching in the immune system, interferon-stimulated, regulation of cytokine production, anti-apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77-0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65-0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible. This is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations

PubMed Central

Logsdon, Benjamin A.; Mezey, Jason

2010-01-01

Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data. PMID:21152011

Identification and validation of reference genes for qRT-PCR studies of the obligate aphid pathogenic fungus Pandora neoaphidis during different developmental stages.

PubMed

Zhang, Shutao; Chen, Chun; Xie, Tingna; Ye, Sudan

2017-01-01

The selection of stable reference genes is a critical step for the accurate quantification of gene expression. To identify and validate the reference genes in Pandora neoaphidis-an obligate aphid pathogenic fungus-the expression of 13classical candidate reference genes were evaluated by quantitative real-time reverse transcriptase polymerase chain reaction(qPCR) at four developmental stages (conidia, conidia with germ tubes, short hyphae and elongated hyphae). Four statistical algorithms, including geNorm, NormFinder, BestKeeper and Delta Ct method were used to rank putative reference genes according to their expression stability and indicate the best reference gene or combination of reference genes for accurate normalization. The analysis of comprehensive ranking revealed that ACT1and 18Swas the most stably expressed genes throughout the developmental stages. To further validate the suitability of the reference genes identified in this study, the expression of cell division control protein 25 (CDC25) and Chitinase 1(CHI1) genes were used to further confirm the validated candidate reference genes. Our study presented the first systematic study of reference gene(s) selection for P. neoaphidis study and provided guidelines to obtain more accurate qPCR results for future developmental efforts.

Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage

PubMed Central

2015-01-01

Background Transgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then re-established during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities. Methods This study applied the recently proposed principal component analysis (PCA)-based unsupervised feature extraction to previously reported and publically available gene expression/promoter methylation profiles of rat primordial germ cells, between E13 and E16 of the F3 generation vinclozolin lineage that are expected to exhibit TGE abnormalities, to identify multiple genes that exhibited aberrant gene expression/promoter methylation during development. Results The biological feasibility of the identified genes were tested via enrichment analyses of various biological concepts including pathway analysis, gene ontology terms and protein-protein interactions. All validations suggested superiority of the proposed method over three conventional and popular supervised methods that employed t test, limma and significance analysis of microarrays, respectively. The identified genes were globally related to tumors, the prostate, kidney, testis and the immune system and were previously reported to be related to various diseases caused by TGE. Conclusions Among the genes reported by PCA-based unsupervised feature extraction, we propose that chemokine signaling pathways and leucine rich repeat proteins are key factors that initiate transgenerational epigenetic-mediated diseases, because multiple genes included in these two categories were identified in this study. PMID:26677731

Characterizing differential gene expression in polyploid grasses lacking a reference transcriptome

USDA-ARS?s Scientific Manuscript database

Basal transcriptome characterization and differential gene expression in response to varying conditions are often addressed through next generation sequencing (NGS) and data analysis techniques. While these strategies are commonly used, there are countless tools, pipelines, data analysis methods an...

Classification of intramural metastases and lymph node metastases of esophageal cancer from gene expression based on boosting and projective adaptive resonance theory.

PubMed

Takahashi, Hiro; Aoyagi, Kazuhiko; Nakanishi, Yukihiro; Sasaki, Hiroki; Yoshida, Teruhiko; Honda, Hiroyuki

2006-07-01

Esophageal cancer is a well-known cancer with poorer prognosis than other cancers. An optimal and individualized treatment protocol based on accurate diagnosis is urgently needed to improve the treatment of cancer patients. For this purpose, it is important to develop a sophisticated algorithm that can manage a large amount of data, such as gene expression data from DNA microarrays, for optimal and individualized diagnosis. Marker gene selection is essential in the analysis of gene expression data. We have already developed a combination method of the use of the projective adaptive resonance theory and that of a boosted fuzzy classifier with the SWEEP operator denoted PART-BFCS. This method is superior to other methods, and has four features, namely fast calculation, accurate prediction, reliable prediction, and rule extraction. In this study, we applied this method to analyze microarray data obtained from esophageal cancer patients. A combination method of PART-BFCS and the U-test was also investigated. It was necessary to use a specific type of BFCS, namely, BFCS-1,2, because the esophageal cancer data were very complexity. PART-BFCS and PART-BFCS with the U-test models showed higher performances than two conventional methods, namely, k-nearest neighbor (kNN) and weighted voting (WV). The genes including CDK6 could be found by our methods and excellent IF-THEN rules could be extracted. The genes selected in this study have a high potential as new diagnosis markers for esophageal cancer. These results indicate that the new methods can be used in marker gene selection for the diagnosis of cancer patients.

eQTL Mapping Using RNA-seq Data

PubMed Central

Hu, Yijuan

2012-01-01

As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping. PMID:23667399

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

PubMed Central

Dinalankara, Wikum; Bravo, Héctor Corrada

2015-01-01

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings. PMID:26078586

A cross-species analysis method to analyze animal models' similarity to human's disease state

PubMed Central

2012-01-01

Background Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. Results We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. Conclusions We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology. PMID:23282076

A cross-species analysis method to analyze animal models' similarity to human's disease state.

PubMed

Yu, Shuhao; Zheng, Lulu; Li, Yun; Li, Chunyan; Ma, Chenchen; Li, Yixue; Li, Xuan; Hao, Pei

2012-01-01

Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.

Maximizing RNA yield from archival renal tumors and optimizing gene expression analysis.

PubMed

Glenn, Sean T; Head, Karen L; Teh, Bin T; Gross, Kenneth W; Kim, Hyung L

2010-01-01

Formalin-fixed, paraffin-embedded tissues are widely available for gene expression analysis using TaqMan PCR. Five methods, including 4 commercial kits, for recovering RNA from paraffin-embedded renal tumor tissue were compared. The MasterPure kit from Epicentre produced the highest RNA yield. However, the difference in RNA yield between the kit from Epicenter and Invitrogen's TRIzol method was not significant. Using the top 3 RNA isolation methods, the manufacturers' protocols were modified to include an overnight Proteinase K digestion. Overnight protein digestion resulted in a significant increase in RNA yield. To optimize the reverse transcription reaction, conventional reverse transcription with random oligonucleotide primers was compared to reverse transcription using primers specific for genes of interest. Reverse transcription using gene-specific primers significantly increased the quantity of cDNA detectable by TaqMan PCR. Therefore, expression profiling of formalin-fixed, paraffin-embedded tissue using TaqMan qPCR can be optimized by using the MasterPure RNA isolation kit modified to include an overnight Proteinase K digestion and gene-specific primers during the reverse transcription.

Establishing references for gene expression analyses by RT-qPCR in Theobroma cacao tissues.

PubMed

Pinheiro, T T; Litholdo, C G; Sereno, M L; Leal, G A; Albuquerque, P S B; Figueira, A

2011-11-17

Lack of continuous progress in Theobroma cacao (Malvaceae) breeding, especially associated with seed quality traits, requires more efficient selection methods based on genomic information. Reverse transcript quantitative PCR (RT-qPCR) has become the method of choice for gene expression analysis, but relative expression analysis requires various reference genes, which must be stable across various biological conditions. We sought suitable reference genes for various tissues of cacao, especially developing seeds. Ten potential reference genes were analyzed for stability at various stages of embryo development, leaves, stems, roots, flowers, and pod epicarp; seven of them were also evaluated in shoot tips treated either with hormones (salicylate; ethefon; methyl-jasmonate) or after inoculation with the fungus Moniliophthora perniciosa (Marasmiaceae sensu lato). For developing embryos, the three most stable genes were actin (ACT), polyubiquitin (PUB), and ribosomal protein L35 (Rpl35). In the analyses of various tissues, the most stable genes were malate dehydrogenase (MDH), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and acyl-carrier protein B (ACP B). GAPDH, MDH and tubulin (TUB) were the most appropriate for normalization when shoot apexes were treated with hormones, while ACT, TUB and Rpl35 were the most appropriate after inoculation with M. perniciosa. We conclude that for each plant system and biological or ontogenetical condition, there is a need to define suitable reference genes. This is the first report to define reference genes for expression studies in cacao.

Differentially expressed genes of Coptotermes formosanus (Isoptera: Rhinotermitidae) challenged by chemical insecticides.

PubMed

Zhang, Yi; Zhao, Yuanyuan; Qiu, Xuehong; Han, Richou

2013-08-01

Coptotermes formosanus Shiraki (Isoptera: Rhinotermitidae) termites are harmful social insects to wood constructions. The current control methods heavily depend on the chemical insecticides with increasing resistance. Analysis of the differentially expressed genes mediated by chemical insecticides will contribute to the understanding of the termite resistance to chemicals and to the establishment of alternative control measures. In the present article, a full-length cDNA library was constructed from the termites induced by a mixture of commonly used insecticides (0.01% sulfluramid and 0.01% triflumuron) for 24 h, by using the RNA ligase-mediated Rapid Amplification cDNA End method. Fifty-eight differentially expressed clones were obtained by polymerase chain reaction and confirmed by dot-blot hybridization. Forty-six known sequences were obtained, which clustered into 33 unique sequences grouped in 6 contigs and 27 singlets. Sixty-seven percent (22) of the sequences had counterpart genes from other organisms, whereas 33% (11) were undescribed. A Gene Ontology analysis classified 33 unique sequences into different functional categories. In general, most of the differential expression genes were involved in binding and catalytic activity.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

PubMed Central

Liu, Zhi-Ping

2015-01-01

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented. PMID:25937810

Rapid and tunable method to temporally control gene editing based on conditional Cas9 stabilization. | Office of Cancer Genomics

Cancer.gov

The CRISPR/Cas9 system is a powerful tool for studying gene function. Here, we describe a method that allows temporal control of CRISPR/Cas9 activity based on conditional Cas9 destabilization. We demonstrate that fusing an FKBP12-derived destabilizing domain to Cas9 (DD-Cas9) enables conditional Cas9 expression and temporal control of gene editing in the presence of an FKBP12 synthetic ligand. This system can be easily adapted to co-express, from the same promoter, DD-Cas9 with any other gene of interest without co-modulation of the latter.

A Novel ‘Gene Insertion/Marker Out’ (GIMO) Method for Transgene Expression and Gene Complementation in Rodent Malaria Parasites

PubMed Central

Sajid, Mohammed; Chevalley-Maurel, Séverine; Ramesar, Jai; Klop, Onny; Franke-Fayard, Blandine M. D.; Janse, Chris J.; Khan, Shahid M.

2011-01-01

Research on the biology of malaria parasites has greatly benefited from the application of reverse genetic technologies, in particular through the analysis of gene deletion mutants and studies on transgenic parasites that express heterologous or mutated proteins. However, transfection in Plasmodium is limited by the paucity of drug-selectable markers that hampers subsequent genetic modification of the same mutant. We report the development of a novel ‘gene insertion/marker out’ (GIMO) method for two rodent malaria parasites, which uses negative selection to rapidly generate transgenic mutants ready for subsequent modifications. We have created reference mother lines for both P. berghei ANKA and P. yoelii 17XNL that serve as recipient parasites for GIMO-transfection. Compared to existing protocols GIMO-transfection greatly simplifies and speeds up the generation of mutants expressing heterologous proteins, free of drug-resistance genes, and requires far fewer laboratory animals. In addition we demonstrate that GIMO-transfection is also a simple and fast method for genetic complementation of mutants with a gene deletion or mutation. The implementation of GIMO-transfection procedures should greatly enhance Plasmodium reverse-genetic research. PMID:22216235

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

PubMed Central

Müller, Christian; Schillert, Arne; Röthemeier, Caroline; Trégouët, David-Alexandre; Proust, Carole; Binder, Harald; Pfeiffer, Norbert; Beutel, Manfred; Lackner, Karl J.; Schnabel, Renate B.; Tiret, Laurence; Wild, Philipp S.; Blankenberg, Stefan

2016-01-01

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data. PMID:27272489

Design, Assembly, and Characterization of TALE-Based Transcriptional Activators and Repressors.

PubMed

Thakore, Pratiksha I; Gersbach, Charles A

2016-01-01

Transcription activator-like effectors (TALEs) are modular DNA-binding proteins that can be fused to a variety of effector domains to regulate the epigenome. Nucleotide recognition by TALE monomers follows a simple cipher, making this a powerful and versatile method to activate or repress gene expression. Described here are methods to design, assemble, and test TALE transcription factors (TALE-TFs) for control of endogenous gene expression. In this protocol, TALE arrays are constructed by Golden Gate cloning and tested for activity by transfection and quantitative RT-PCR. These methods for engineering TALE-TFs are useful for studies in reverse genetics and genomics, synthetic biology, and gene therapy.

Expression and functional activity of P-glycoprotein in passaged primary human nasal epithelial cell monolayers cultured by the air-liquid interface method for nasal drug transport study.

PubMed

Cho, Hyun-Jong; Choi, Min-Koo; Lin, Hongxia; Kim, Jung Sun; Chung, Suk-Jae; Shim, Chang-Koo; Kim, Dae-Duk

2011-03-01

P-glycoprotein (P-gp) is an efflux transporter encoded by the multidrug resistance gene (MDR1), which is also known as the human ABCB1 gene (ATP-binding cassette, subfamily-B). The objectives of this study were to investigate the expression of P-gp in passaged primary human nasal epithelial (HNE) cell monolayer, cultured by the air-liquid interface (ALI) method, and to evaluate its feasibility as an in-vitro model for cellular uptake and transport studies of P-gp substrates. Reverse transcriptase-polymerase chain reaction (RT-PCR) was performed to verify the expression of the MDR1 gene. Transport and cellular uptake studies with P-gp substrate (rhodamine123) and P-gp inhibitors (verapamil and cyclosporin A) were conducted to assess the functional activity of P-gp in HNE cell monolayers cultured by the ALI method. MDR1 gene expression in primary HNE cell monolayers cultured by ALI method was confirmed by RT-PCR. The apparent permeability coefficient (P(app) ) of the P-gp substrate (rhodamine123) in the basolateral to apical (B to A) direction was 6.9 times higher than that in the apical to basolateral (A to B) direction. B to A transport was saturated at high rhodamine123 concentration, and the treatment of P-gp inhibitors increased cellular uptake of rhodamine123 in a time- and concentration-dependent manner. These results support the MDR1 gene expression and the functional activity of P-gp in primary HNE cell monolayers cultured by the ALI method. © 2011 The Authors. JPP © 2011 Royal Pharmaceutical Society.

INDUCTION OF GENE EXPRESSION IN SHEEPSHEAD MINNOWS (CYPRINODON VARIEGATUS) TREATED WITH 17B-ESTRADIOL, DIETHYLSTILBESTROL, OR ETHINYLESTRADIOL: THE USE OF MRNA FINGERPRINTS AS AN INDICATOR OF GENE REGULATION

EPA Science Inventory

The recent interest in hormonally active environmental contaminants has sparked a drive to find sensitive methods to measure their effects on wildlife. A molecular-based assay has been developed to measure the induction of gene expression in sheepshead minnows (Cyprinodon variega...

Microarray Data Mining for Potential Selenium Targets in Chemoprevention of Prostate Cancer

PubMed Central

ZHANG, HAITAO; DONG, YAN; ZHAO, HONGJUAN; BROOKS, JAMES D.; HAWTHORN, LESLEYANN; NOWAK, NORMA; MARSHALL, JAMES R.; GAO, ALLEN C.; IP, CLEMENT

2008-01-01

Background A previous clinical trial showed that selenium supplementation significantly reduced the incidence of prostate cancer. We report here a bioinformatics approach to gain new insights into selenium molecular targets that might be relevant to prostate cancer chemoprevention. Materials and Methods We first performed data mining analysis to identify genes which are consistently dysregulated in prostate cancer using published datasets from gene expression profiling of clinical prostate specimens. We then devised a method to systematically analyze three selenium microarray datasets from the LNCaP human prostate cancer cells, and to match the analysis to the cohort of genes implicated in prostate carcinogenesis. Moreover, we compared the selenium datasets with two datasets obtained from expression profiling of androgen-stimulated LNCaP cells. Results We found that selenium reverses the expression of genes implicated in prostate carcinogenesis. In addition, we found that selenium could counteract the effect of androgen on the expression of a subset obtained from androgen-regulated genes. Conclusions The above information provides us with a treasure of new clues to investigate the mechanism of selenium chemoprevention of prostate cancer. Furthermore, these selenium target genes could also serve as biomarkers in future clinical trials to gauge the efficacy of selenium intervention. PMID:18548127

DOE Office of Scientific and Technical Information (OSTI.GOV)

Onodera, Yasuhito; Bissell, Mina

Disclosed are methods in which glucose metabolism is correlated to oncogenesis through certain specific pathways; inhibition of certain enzymes is shown to interfere with oncogenic signaling, and measurement of certain enzyme levels is correlated with patient survival. The present methods comprise measuring level of expression of at least one of the enzymes involved in glucose uptake or metabolism, wherein increased expression of the at least one of the enzymes relative to expression in a normal cell correlates with poor prognosis of disease in a patient. Preferably the genes whose expression level is measured include GLUT3, PFKP, GAPDH, ALDOC, LDHA andmore » GFPT2. Also disclosed are embodiments directed towards downregulating the expression of some genes in glucose uptake and metabolism.« less

Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method

PubMed Central

Takahashi, Hiro; Nemoto, Takeshi; Yoshida, Teruhiko; Honda, Hiroyuki; Hasegawa, Tadashi

2006-01-01

Background Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. Results Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting – the PART-BFCS method – showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method – MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3 – are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS. Conclusion The procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PART filtering method. PMID:16948864

Generation of a Tet-On Expression System to Study Transactivation Ability of Tax-2.

PubMed

Bignami, Fabio; Sozzi, Riccardo Alessio; Pilotti, Elisabetta

2017-01-01

HTLV Tax proteins (Tax-1 and Tax-2) are known to be able to transactivate several host cellular genes involved in complex molecular pathways. Here, we describe a stable and regulated high-level expression model based on Tet-On system, to study the capacity of Tax-2 to transactivate host genes. In particular, the Jurkat Tet-On cell line suitable for evaluating the ability of Tax-2 to stimulate transactivation of a specific host gene, CCL3L1 (C-C motif chemokine ligand 3 like 1 gene), was selected. Then, a plasmid expressing tax-2 gene under control of a tetracycline-response element was constructed. To avoid the production of a fusion protein between the report gene and the inserted gene, a bidirectional plasmid was designed. Maximum expression and fast response time were achieved by using nucleofection technology as transfection method. After developing an optimized protocol for efficiently transferring tax-2 gene in Jurkat Tet-On cellular model and exposing transfected cells to Dox (doxycycline, a tetracycline derivate), a kinetics of tax-2 expression through TaqMan Real-time PCR assay was determined.

Regulatory and evolutionary signatures of sex-biased genes on both the X chromosome and the autosomes.

PubMed

Shen, Jiangshan J; Wang, Ting-You; Yang, Wanling

2017-11-02

Sex is an important but understudied factor in the genetics of human diseases. Analyses using a combination of gene expression data, ENCODE data, and evolutionary data of sex-biased gene expression in human tissues can give insight into the regulatory and evolutionary forces acting on sex-biased genes. In this study, we analyzed the differentially expressed genes between males and females. On the X chromosome, we used a novel method and investigated the status of genes that escape X-chromosome inactivation (escape genes), taking into account the clonality of lymphoblastoid cell lines (LCLs). To investigate the regulation of sex-biased differentially expressed genes (sDEG), we conducted pathway and transcription factor enrichment analyses on the sDEGs, as well as analyses on the genomic distribution of sDEGs. Evolutionary analyses were also conducted on both sDEGs and escape genes. Genome-wide, we characterized differential gene expression between sexes in 462 RNA-seq samples and identified 587 sex-biased genes, or 3.2% of the genes surveyed. On the X chromosome, sDEGs were distributed in evolutionary strata in a similar pattern as escape genes. We found a trend of negative correlation between the gene expression breadth and nonsynonymous over synonymous mutation (dN/dS) ratios, showing a possible pleiotropic constraint on evolution of genes. Genome-wide, nine transcription factors were found enriched in binding to the regions surrounding the transcription start sites of female-biased genes. Many pathways and protein domains were enriched in sex-biased genes, some of which hint at sex-biased physiological processes. These findings lend insight into the regulatory and evolutionary forces shaping sex-biased gene expression and their involvement in the physiological and pathological processes in human health and diseases.

Finding genes discriminating smokers from non-smokers by applying a growing self-organizing clustering method to large airway epithelium cell microarray data.

PubMed

Shahdoust, Maryam; Hajizadeh, Ebrahim; Mozdarani, Hossein; Chehrei, Ali

2013-01-01

Cigarette smoking is the major risk factor for development of lung cancer. Identification of effects of tobacco on airway gene expression may provide insight into the causes. This research aimed to compare gene expression of large airway epithelium cells in normal smokers (n=13) and non-smokers (n=9) in order to find genes which discriminate the two groups and assess cigarette smoking effects on large airway epithelium cells. Genes discriminating smokers from non-smokers were identified by applying a neural network clustering method, growing self-organizing maps (GSOM), to microarray data according to class discrimination scores. An index was computed based on differentiation between each mean of gene expression in the two groups. This clustering approach provided the possibility of comparing thousands of genes simultaneously. The applied approach compared the mean of 7,129 genes in smokers and non-smokers simultaneously and classified the genes of large airway epithelium cells which had differently expressed in smokers comparing with non-smokers. Seven genes were identified which had the highest different expression in smokers compared with the non-smokers group: NQO1, H19, ALDH3A1, AKR1C1, ABHD2, GPX2 and ADH7. Most (NQO1, ALDH3A1, AKR1C1, H19 and GPX2) are known to be clinically notable in lung cancer studies. Furthermore, statistical discriminate analysis showed that these genes could classify samples in smokers and non-smokers correctly with 100% accuracy. With the performed GSOM map, other nodes with high average discriminate scores included genes with alterations strongly related to the lung cancer such as AKR1C3, CYP1B1, UCHL1 and AKR1B10. This clustering by comparing expression of thousands of genes at the same time revealed alteration in normal smokers. Most of the identified genes were strongly relevant to lung cancer in the existing literature. The genes may be utilized to identify smokers with increased risk for lung cancer. A large sample study is now recommended to determine relations between the genes ABHD2 and ADH7 and smoking.