APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

PubMed

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems

PubMed Central

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information. PMID:23346354

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)-A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes.

PubMed

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare . However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop plants with large and complex genomes.

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)—A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes

PubMed Central

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare. However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop plants with large and complex genomes. PMID:29250096

Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

PubMed

Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

2014-05-01

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation. © 2013 AlphaMed Press.

A method for quantitative analysis of standard and high-throughput qPCR expression data based on input sample quantity.

PubMed

Adamski, Mateusz G; Gumann, Patryk; Baird, Alison E

2014-01-01

Over the past decade rapid advances have occurred in the understanding of RNA expression and its regulation. Quantitative polymerase chain reactions (qPCR) have become the gold standard for quantifying gene expression. Microfluidic next generation, high throughput qPCR now permits the detection of transcript copy number in thousands of reactions simultaneously, dramatically increasing the sensitivity over standard qPCR. Here we present a gene expression analysis method applicable to both standard polymerase chain reactions (qPCR) and high throughput qPCR. This technique is adjusted to the input sample quantity (e.g., the number of cells) and is independent of control gene expression. It is efficiency-corrected and with the use of a universal reference sample (commercial complementary DNA (cDNA)) permits the normalization of results between different batches and between different instruments--regardless of potential differences in transcript amplification efficiency. Modifications of the input quantity method include (1) the achievement of absolute quantification and (2) a non-efficiency corrected analysis. When compared to other commonly used algorithms the input quantity method proved to be valid. This method is of particular value for clinical studies of whole blood and circulating leukocytes where cell counts are readily available.

Evaluation of high throughput gene expression platforms using a genomic biomarker signature for prediction of skin sensitization.

PubMed

Forreryd, Andy; Johansson, Henrik; Albrekt, Ann-Sofie; Lindstedt, Malin

2014-05-16

Allergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an in vitro alternative to animal testing for identification of skin sensitizers, classifying chemicals by evaluating transcriptional levels of a genomic biomarker signature. During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms--nCounter®, BioMark HD™ and OpenArray®--and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms. Gene expression data from all of the evaluated platforms could be used to classify most of the sensitizers from non-sensitizers in the GARD assay. Results also showed high data quality and acceptable reproducibility for all platforms but only medium to poor correlations of expression measurements across platforms. In addition, evaluated platforms were superior to the microarray platform in terms of cost efficiency, simplicity of protocols and sample throughput. We evaluated the performance of three non-array based platforms using a limited set of transcripts from the GARD biomarker signature. We demonstrated that it was possible to achieve acceptable discriminatory power in terms of separation between sensitizers and non-sensitizers in the GARD assay while reducing assay costs, simplify assay procedures and increase sample throughput by using an alternative platform, providing a first step towards the goal to prepare GARD for formal validation and adaption of the assay for industrial screening of potential sensitizers.

Development and validation of a 48-target analytical method for high-throughput monitoring of genetically modified organisms.

PubMed

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-05

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection.

Development and Validation of A 48-Target Analytical Method for High-throughput Monitoring of Genetically Modified Organisms

PubMed Central

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-01

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection. PMID:25556930

High-Throughput Gene Mapping in Caenorhabditis elegans

PubMed Central

Swan, Kathryn A.; Curtis, Damian E.; McKusick, Kathleen B.; Voinov, Alexander V.; Mapa, Felipa A.; Cancilla, Michael R.

2002-01-01

Positional cloning of mutations in model genetic systems is a powerful method for the identification of targets of medical and agricultural importance. To facilitate the high-throughput mapping of mutations in Caenorhabditis elegans, we have identified a further 9602 putative new single nucleotide polymorphisms (SNPs) between two C. elegans strains, Bristol N2 and the Hawaiian mapping strain CB4856, by sequencing inserts from a CB4856 genomic DNA library and using an informatics pipeline to compare sequences with the canonical N2 genomic sequence. When combined with data from other laboratories, our marker set of 17,189 SNPs provides even coverage of the complete worm genome. To date, we have confirmed >1099 evenly spaced SNPs (one every 91 ± 56 kb) across the six chromosomes and validated the utility of our SNP marker set and new fluorescence polarization-based genotyping methods for systematic and high-throughput identification of genes in C. elegans by cloning several proprietary genes. We illustrate our approach by recombination mapping and confirmation of the mutation in the cloned gene, dpy-18. [The sequence data described in this paper have been submitted to the NCBI dbSNP data library under accession nos. 4388625–4389689 and GenBank dbSTS under accession nos. 973810–974874. The following individuals and institutions kindly provided reagents, samples, or unpublished information as indicated in the paper: The C. elegans Sequencing Consortium and The Caenorhabditis Genetics Center.] PMID:12097347

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

A large-scale benchmark of gene prioritization methods.

PubMed

Guala, Dimitri; Sonnhammer, Erik L L

2017-04-21

In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

PubMed

Ryall, Karen A; Shin, Jimin; Yoo, Minjae; Hinz, Trista K; Kim, Jihye; Kang, Jaewoo; Heasley, Lynn E; Tan, Aik Choon

2015-12-01

Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. aikchoon.tan@ucdenver.edu. Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Genetically Validated Drug Targets in Leishmania: Current Knowledge and Future Prospects.

PubMed

Jones, Nathaniel G; Catta-Preta, Carolina M C; Lima, Ana Paula C A; Mottram, Jeremy C

2018-04-13

There has been a very limited number of high-throughput screening campaigns carried out with Leishmania drug targets. In part, this is due to the small number of suitable target genes that have been shown by genetic or chemical methods to be essential for the parasite. In this perspective, we discuss the state of genetic target validation in the field of Leishmania research and review the 200 Leishmania genes and 36 Trypanosoma cruzi genes for which gene deletion attempts have been made since the first published case in 1990. We define a quality score for the different genetic deletion techniques that can be used to identify potential drug targets. We also discuss how the advances in genome-scale gene disruption techniques have been used to assist target-based and phenotypic-based drug development in other parasitic protozoa and why Leishmania has lacked a similar approach so far. The prospects for this scale of work are considered in the context of the application of CRISPR/Cas9 gene editing as a useful tool in Leishmania.

OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer.

PubMed

Van Coillie, Samya; Liang, Lunxi; Zhang, Yao; Wang, Huanbin; Fang, Jing-Yuan; Xu, Jie

2016-04-05

High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics.

PubMed

Geeleher, Paul; Loboda, Andrey; Lenkala, Divya; Wang, Fan; LaCroix, Bonnie; Karovic, Sanja; Wang, Jacqueline; Nebozhyn, Michael; Chisamore, Michael; Hardwick, James; Maitland, Michael L; Huang, R Stephanie

2015-11-01

Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated. We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial. Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines.This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically. Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Systematically Differentiating Functions for Alternatively Spliced Isoforms through Integrating RNA-seq Data

PubMed Central

Menon, Rajasree; Wen, Yuchen; Omenn, Gilbert S.; Kretzler, Matthias; Guan, Yuanfang

2013-01-01

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires ‘ground-truth’ functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the ‘responsible’ isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the ‘responsible’ isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions. PMID:24244129

Identification of in vivo regulators of the Vibrio cholerae xds gene using a high-throughput genetic selection

PubMed Central

McDonough, EmilyKate; Lazinski, David W.; Camilli, Andrew

2014-01-01

Summary Vibrio cholerae, the causative agent of cholera, remains a threat to public health in areas with inadequate sanitation. As a waterborne pathogen, V. cholerae moves between two dissimilar environments, aquatic reservoirs and the intestinal tract of humans. Accordingly, this pathogen undergoes adaptive shifts in gene expression throughout the different stages of its lifecycle. One particular gene, xds, encodes a secreted exonuclease that was previously identified as being induced during infection. Here we sought to identify regulators responsible for the in vivo-specific induction of xds. A transcriptional fusion of xds to two consecutive antibiotic resistance genes was used to select transposon mutants that had inserted within or adjacent to regulatory genes and thereby caused increased expression of the xds fusion under non-inducing conditions. Large pools of selected insertion sites were sequenced in a high throughput manner using Tn-seq to identify potential mechanisms of xds regulation. Our selection identified the two-component system PhoB/R as the dominant activator of xds expression. In vitro validation confirmed that PhoB, a protein which is only active during phosphate limitation, was responsible for xds activation. Using xds expression as a biosensor of the extracellular phosphate level, we observed that the mouse small intestine is a phosphate-limited environment. PMID:24673931

High-throughput discovery of novel developmental phenotypes.

PubMed

Dickinson, Mary E; Flenniken, Ann M; Ji, Xiao; Teboul, Lydia; Wong, Michael D; White, Jacqueline K; Meehan, Terrence F; Weninger, Wolfgang J; Westerberg, Henrik; Adissu, Hibret; Baker, Candice N; Bower, Lynette; Brown, James M; Caddle, L Brianna; Chiani, Francesco; Clary, Dave; Cleak, James; Daly, Mark J; Denegre, James M; Doe, Brendan; Dolan, Mary E; Edie, Sarah M; Fuchs, Helmut; Gailus-Durner, Valerie; Galli, Antonella; Gambadoro, Alessia; Gallegos, Juan; Guo, Shiying; Horner, Neil R; Hsu, Chih-Wei; Johnson, Sara J; Kalaga, Sowmya; Keith, Lance C; Lanoue, Louise; Lawson, Thomas N; Lek, Monkol; Mark, Manuel; Marschall, Susan; Mason, Jeremy; McElwee, Melissa L; Newbigging, Susan; Nutter, Lauryl M J; Peterson, Kevin A; Ramirez-Solis, Ramiro; Rowland, Douglas J; Ryder, Edward; Samocha, Kaitlin E; Seavitt, John R; Selloum, Mohammed; Szoke-Kovacs, Zsombor; Tamura, Masaru; Trainor, Amanda G; Tudose, Ilinca; Wakana, Shigeharu; Warren, Jonathan; Wendling, Olivia; West, David B; Wong, Leeyean; Yoshiki, Atsushi; MacArthur, Daniel G; Tocchini-Valentini, Glauco P; Gao, Xiang; Flicek, Paul; Bradley, Allan; Skarnes, William C; Justice, Monica J; Parkinson, Helen E; Moore, Mark; Wells, Sara; Braun, Robert E; Svenson, Karen L; de Angelis, Martin Hrabe; Herault, Yann; Mohun, Tim; Mallon, Ann-Marie; Henkelman, R Mark; Brown, Steve D M; Adams, David J; Lloyd, K C Kent; McKerlie, Colin; Beaudet, Arthur L; Bućan, Maja; Murray, Stephen A

2016-09-22

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

High-throughput discovery of novel developmental phenotypes

PubMed Central

Dickinson, Mary E.; Flenniken, Ann M.; Ji, Xiao; Teboul, Lydia; Wong, Michael D.; White, Jacqueline K.; Meehan, Terrence F.; Weninger, Wolfgang J.; Westerberg, Henrik; Adissu, Hibret; Baker, Candice N.; Bower, Lynette; Brown, James M.; Caddle, L. Brianna; Chiani, Francesco; Clary, Dave; Cleak, James; Daly, Mark J.; Denegre, James M.; Doe, Brendan; Dolan, Mary E.; Edie, Sarah M.; Fuchs, Helmut; Gailus-Durner, Valerie; Galli, Antonella; Gambadoro, Alessia; Gallegos, Juan; Guo, Shiying; Horner, Neil R.; Hsu, Chih-wei; Johnson, Sara J.; Kalaga, Sowmya; Keith, Lance C.; Lanoue, Louise; Lawson, Thomas N.; Lek, Monkol; Mark, Manuel; Marschall, Susan; Mason, Jeremy; McElwee, Melissa L.; Newbigging, Susan; Nutter, Lauryl M.J.; Peterson, Kevin A.; Ramirez-Solis, Ramiro; Rowland, Douglas J.; Ryder, Edward; Samocha, Kaitlin E.; Seavitt, John R.; Selloum, Mohammed; Szoke-Kovacs, Zsombor; Tamura, Masaru; Trainor, Amanda G; Tudose, Ilinca; Wakana, Shigeharu; Warren, Jonathan; Wendling, Olivia; West, David B.; Wong, Leeyean; Yoshiki, Atsushi; MacArthur, Daniel G.; Tocchini-Valentini, Glauco P.; Gao, Xiang; Flicek, Paul; Bradley, Allan; Skarnes, William C.; Justice, Monica J.; Parkinson, Helen E.; Moore, Mark; Wells, Sara; Braun, Robert E.; Svenson, Karen L.; de Angelis, Martin Hrabe; Herault, Yann; Mohun, Tim; Mallon, Ann-Marie; Henkelman, R. Mark; Brown, Steve D.M.; Adams, David J.; Lloyd, K.C. Kent; McKerlie, Colin; Beaudet, Arthur L.; Bucan, Maja; Murray, Stephen A.

2016-01-01

Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 lethal genes during the production of the first 1751 unique gene knockouts. Using a standardised phenotyping platform that incorporates high-resolution 3D imaging, we identified novel phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes identified in our screen, thus providing a novel dataset that facilitates prioritization and validation of mutations identified in clinical sequencing efforts. PMID:27626380

cDNA Clones with Rare and Recurrent Mutations Found in Cancers | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at UT- MD Anderson Cancer Center has developed High-Throughput Mutagenesis and Molecular Barcoding (HiTMMoB)1,2 pipeline to construct mutant alleles open reading frame expression clones that are either recurrent or rare in cancers. These barcoded genes can be used for context-specific functional validation, detection of novel biomarkers (pathway activation) and targets (drug sensitivity).

From High-Throughput Microarray-Based Screening to Clinical Application: The Development of a Second Generation Multigene Test for Breast Cancer Prognosis

PubMed Central

Brase, Jan C.; Kronenwett, Ralf; Petry, Christoph; Denkert, Carsten; Schmidt, Marcus

2013-01-01

Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of a second generation multigene assay, the EndoPredict test, a prognostic multigene expression test for estrogen receptor (ER) positive, human epidermal growth factor receptor (HER2) negative (ER+/HER2−) breast cancer patients. The EndoPredict gene signature was initially established in a large high-throughput microarray-based screening study. The key steps for biomarker identification are discussed in detail, in comparison to the establishment of other multigene signatures. After biomarker selection, genes and algorithms were transferred to a diagnostic platform (reverse transcription quantitative PCR (RT-qPCR)) to allow for assaying formalin-fixed, paraffin-embedded (FFPE) samples. A comprehensive analytical validation was performed and a prospective proficiency testing study with seven pathological laboratories finally proved that EndoPredict can be reliably used in the decentralized setting. Three independent large clinical validation studies (n = 2,257) demonstrated that EndoPredict offers independent prognostic information beyond current clinicopathological parameters and clinical guidelines. The review article summarizes several important steps that should be considered for the development process of a second generation multigene test and offers a means for transferring a microarray signature from the research laboratory to clinical practice. PMID:27605191

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

PubMed Central

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

Large scale validation of an efficient CRISPR/Cas-based multi gene editing protocol in Escherichia coli.

PubMed

Zerbini, Francesca; Zanella, Ilaria; Fraccascia, Davide; König, Enrico; Irene, Carmela; Frattini, Luca F; Tomasi, Michele; Fantappiè, Laura; Ganfini, Luisa; Caproni, Elena; Parri, Matteo; Grandi, Alberto; Grandi, Guido

2017-04-24

The exploitation of the CRISPR/Cas9 machinery coupled to lambda (λ) recombinase-mediated homologous recombination (recombineering) is becoming the method of choice for genome editing in E. coli. First proposed by Jiang and co-workers, the strategy has been subsequently fine-tuned by several authors who demonstrated, by using few selected loci, that the efficiency of mutagenesis (number of mutant colonies over total number of colonies analyzed) can be extremely high (up to 100%). However, from published data it is difficult to appreciate the robustness of the technology, defined as the number of successfully mutated loci over the total number of targeted loci. This information is particularly relevant in high-throughput genome editing, where repetition of experiments to rescue missing mutants would be impractical. This work describes a "brute force" validation activity, which culminated in the definition of a robust, simple and rapid protocol for single or multiple gene deletions. We first set up our own version of the CRISPR/Cas9 protocol and then we evaluated the mutagenesis efficiency by changing different parameters including sequence of guide RNAs, length and concentration of donor DNAs, and use of single stranded and double stranded donor DNAs. We then validated the optimized conditions targeting 78 "dispensable" genes. This work led to the definition of a protocol, featuring the use of double stranded synthetic donor DNAs, which guarantees mutagenesis efficiencies consistently higher than 10% and a robustness of 100%. The procedure can be applied also for simultaneous gene deletions. This work defines for the first time the robustness of a CRISPR/Cas9-based protocol based on a large sample size. Since the technical solutions here proposed can be applied to other similar procedures, the data could be of general interest for the scientific community working on bacterial genome editing and, in particular, for those involved in synthetic biology projects requiring high throughput procedures.

Apple ring rot-responsive putative microRNAs revealed by high-throughput sequencing in Malus × domestica Borkh.

PubMed

Yu, Xin-Yi; Du, Bei-Bei; Gao, Zhi-Hong; Zhang, Shi-Jie; Tu, Xu-Tong; Chen, Xiao-Yun; Zhang, Zhen; Qu, Shen-Chun

2014-08-01

MicroRNAs (miRNAs) are small non-coding RNAs, which silence target mRNA via cleavage or translational inhibition to function in regulating gene expression. MiRNAs act as important regulators of plant development and stress response. For understanding the role of miRNAs responsive to apple ring rot stress, we identified disease-responsive miRNAs using high-throughput sequencing in Malus × domestica Borkh.. Four small RNA libraries were constructed from two control strains in M. domestica, crabapple (CKHu) and Fuji Naga-fu No. 6 (CKFu), and two disease stress strains, crabapple (DSHu) and Fuji Naga-fu No. 6 (DSFu). A total of 59 miRNA families were identified and five miRNAs might be responsive to apple ring rot infection and validated via qRT-PCR. Furthermore, we predicted 76 target genes which were regulated by conserved miRNAs potentially. Our study demonstrated that miRNAs was responsive to apple ring rot infection and may have important implications on apple disease resistance.

PCR Primers to Study the Diversity of Expressed Fungal Genes Encoding Lignocellulolytic Enzymes in Soils Using High-Throughput Sequencing

PubMed Central

Barbi, Florian; Bragalini, Claudia; Vallon, Laurent; Prudent, Elsa; Dubost, Audrey; Fraissinet-Tachet, Laurence; Marmeisse, Roland; Luis, Patricia

2014-01-01

Plant biomass degradation in soil is one of the key steps of carbon cycling in terrestrial ecosystems. Fungal saprotrophic communities play an essential role in this process by producing hydrolytic enzymes active on the main components of plant organic matter. Open questions in this field regard the diversity of the species involved, the major biochemical pathways implicated and how these are affected by external factors such as litter quality or climate changes. This can be tackled by environmental genomic approaches involving the systematic sequencing of key enzyme-coding gene families using soil-extracted RNA as material. Such an approach necessitates the design and evaluation of gene family-specific PCR primers producing sequence fragments compatible with high-throughput sequencing approaches. In the present study, we developed and evaluated PCR primers for the specific amplification of fungal CAZy Glycoside Hydrolase gene families GH5 (subfamily 5) and GH11 encoding endo-β-1,4-glucanases and endo-β-1,4-xylanases respectively as well as Basidiomycota class II peroxidases, corresponding to the CAZy Auxiliary Activity family 2 (AA2), active on lignin. These primers were experimentally validated using DNA extracted from a wide range of Ascomycota and Basidiomycota species including 27 with sequenced genomes. Along with the published primers for Glycoside Hydrolase GH7 encoding enzymes active on cellulose, the newly design primers were shown to be compatible with the Illumina MiSeq sequencing technology. Sequences obtained from RNA extracted from beech or spruce forest soils showed a high diversity and were uniformly distributed in gene trees featuring the global diversity of these gene families. This high-throughput sequencing approach using several degenerate primers constitutes a robust method, which allows the simultaneous characterization of the diversity of different fungal transcripts involved in plant organic matter degradation and may lead to the discovery of complex patterns in gene expression of soil fungal communities. PMID:25545363

Identification of Reference Genes for RT-qPCR Data Normalization in Cannabis sativa Stem Tissues.

PubMed

Mangeot-Peter, Lauralie; Legay, Sylvain; Hausman, Jean-Francois; Esposito, Sergio; Guerriero, Gea

2016-09-15

Gene expression profiling via quantitative real-time PCR is a robust technique widely used in the life sciences to compare gene expression patterns in, e.g., different tissues, growth conditions, or after specific treatments. In the field of plant science, real-time PCR is the gold standard to study the dynamics of gene expression and is used to validate the results generated with high throughput techniques, e.g., RNA-Seq. An accurate relative quantification of gene expression relies on the identification of appropriate reference genes, that need to be determined for each experimental set-up used and plant tissue studied. Here, we identify suitable reference genes for expression profiling in stems of textile hemp (Cannabis sativa L.), whose tissues (isolated bast fibres and core) are characterized by remarkable differences in cell wall composition. We additionally validate the reference genes by analysing the expression of putative candidates involved in the non-oxidative phase of the pentose phosphate pathway and in the first step of the shikimate pathway. The goal is to describe the possible regulation pattern of some genes involved in the provision of the precursors needed for lignin biosynthesis in the different hemp stem tissues. The results here shown are useful to design future studies focused on gene expression analyses in hemp.

An improved stable isotope N-terminal labeling approach with light/heavy TMPP to automate proteogenomics data validation: dN-TOP.

PubMed

Bertaccini, Diego; Vaca, Sebastian; Carapito, Christine; Arsène-Ploetze, Florence; Van Dorsselaer, Alain; Schaeffer-Reiss, Christine

2013-06-07

In silico gene prediction has proven to be prone to errors, especially regarding precise localization of start codons that spread in subsequent biological studies. Therefore, the high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and especially proteogenomics fields. The trimethoxyphenyl phosphonium (TMPP) labeling approach (N-TOP) is an efficient N-terminomic approach that allows the characterization of both N-terminal and internal peptides in a single experiment. Due to its permanent positive charge, TMPP labeling strongly affects MS/MS fragmentation resulting in unadapted scoring of TMPP-derivatized peptide spectra by classical search engines. This behavior has led to difficulties in validating TMPP-derivatized peptide identifications with usual score filtering and thus to low/underestimated numbers of identified N-termini. We present herein a new strategy (dN-TOP) that overwhelmed the previous limitation allowing a confident and automated N-terminal peptide validation thanks to a combined labeling with light and heavy TMPP reagents. We show how this double labeling allows increasing the number of validated N-terminal peptides. This strategy represents a considerable improvement to the well-established N-TOP method with an enhanced and accelerated data processing making it now fully compatible with high-throughput proteogenomics studies.

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

PubMed

Yang, Laurence; Tan, Justin; O'Brien, Edward J; Monk, Jonathan M; Kim, Donghyuk; Li, Howard J; Charusanti, Pep; Ebrahim, Ali; Lloyd, Colton J; Yurkovich, James T; Du, Bin; Dräger, Andreas; Thomas, Alex; Sun, Yuekai; Saunders, Michael A; Palsson, Bernhard O

2015-08-25

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

A paper-based microbial fuel cell array for rapid and high-throughput screening of electricity-producing bacteria.

PubMed

Choi, Gihoon; Hassett, Daniel J; Choi, Seokheun

2015-06-21

There is a large global effort to improve microbial fuel cell (MFC) techniques and advance their translational potential toward practical, real-world applications. Significant boosts in MFC performance can be achieved with the development of new techniques in synthetic biology that can regulate microbial metabolic pathways or control their gene expression. For these new directions, a high-throughput and rapid screening tool for microbial biopower production is needed. In this work, a 48-well, paper-based sensing platform was developed for the high-throughput and rapid characterization of the electricity-producing capability of microbes. 48 spatially distinct wells of a sensor array were prepared by patterning 48 hydrophilic reservoirs on paper with hydrophobic wax boundaries. This paper-based platform exploited the ability of paper to quickly wick fluid and promoted bacterial attachment to the anode pads, resulting in instant current generation upon loading of the bacterial inoculum. We validated the utility of our MFC array by studying how strategic genetic modifications impacted the electrochemical activity of various Pseudomonas aeruginosa mutant strains. Within just 20 minutes, we successfully determined the electricity generation capacity of eight isogenic mutants of P. aeruginosa. These efforts demonstrate that our MFC array displays highly comparable performance characteristics and identifies genes in P. aeruginosa that can trigger a higher power density.

Targeted next-generation sequencing in steroid-resistant nephrotic syndrome: mutations in multiple glomerular genes may influence disease severity.

PubMed

Bullich, Gemma; Trujillano, Daniel; Santín, Sheila; Ossowski, Stephan; Mendizábal, Santiago; Fraga, Gloria; Madrid, Álvaro; Ariceta, Gema; Ballarín, José; Torra, Roser; Estivill, Xavier; Ars, Elisabet

2015-09-01

Genetic diagnosis of steroid-resistant nephrotic syndrome (SRNS) using Sanger sequencing is complicated by the high genetic heterogeneity and phenotypic variability of this disease. We aimed to improve the genetic diagnosis of SRNS by simultaneously sequencing 26 glomerular genes using massive parallel sequencing and to study whether mutations in multiple genes increase disease severity. High-throughput mutation analysis was performed in 50 SRNS and/or focal segmental glomerulosclerosis (FSGS) patients, a validation cohort of 25 patients with known pathogenic mutations, and a discovery cohort of 25 uncharacterized patients with probable genetic etiology. In the validation cohort, we identified the 42 previously known pathogenic mutations across NPHS1, NPHS2, WT1, TRPC6, and INF2 genes. In the discovery cohort, disease-causing mutations in SRNS/FSGS genes were found in nine patients. We detected three patients with mutations in an SRNS/FSGS gene and COL4A3. Two of them were familial cases and presented a more severe phenotype than family members with mutation in only one gene. In conclusion, our results show that massive parallel sequencing is feasible and robust for genetic diagnosis of SRNS/FSGS. Our results indicate that patients carrying mutations in an SRNS/FSGS gene and also in COL4A3 gene have increased disease severity.

High Throughput Measurement of Locomotor Sensitization to Volatilized Cocaine in Drosophila melanogaster.

PubMed

Filošević, Ana; Al-Samarai, Sabina; Andretić Waldowski, Rozi

2018-01-01

Drosophila melanogaster can be used to identify genes with novel functional roles in neuronal plasticity induced by repeated consumption of addictive drugs. Behavioral sensitization is a relatively simple behavioral output of plastic changes that occur in the brain after repeated exposures to drugs of abuse. The development of screening procedures for genes that control behavioral sensitization has stalled due to a lack of high-throughput behavioral tests that can be used in genetically tractable organism, such as Drosophila . We have developed a new behavioral test, FlyBong, which combines delivery of volatilized cocaine (vCOC) to individually housed flies with objective quantification of their locomotor activity. There are two main advantages of FlyBong: it is high-throughput and it allows for comparisons of locomotor activity of individual flies before and after single or multiple exposures. At the population level, exposure to vCOC leads to transient and concentration-dependent increase in locomotor activity, representing sensitivity to an acute dose. A second exposure leads to further increase in locomotion, representing locomotor sensitization. We validate FlyBong by showing that locomotor sensitization at either the population or individual level is absent in the mutants for circadian genes period (per) , Clock (Clk) , and cycle (cyc) . The locomotor sensitization that is present in timeless (tim) and pigment dispersing factor (pdf) mutant flies is in large part not cocaine specific, but derived from increased sensitivity to warm air. Circadian genes are not only integral part of the neural mechanism that is required for development of locomotor sensitization, but in addition, they modulate the intensity of locomotor sensitization as a function of the time of day. Motor-activating effects of cocaine are sexually dimorphic and require a functional dopaminergic transporter. FlyBong is a new and improved method for inducing and measuring locomotor sensitization to cocaine in individual Drosophila . Because of its high-throughput nature, FlyBong can be used in genetic screens or in selection experiments aimed at the unbiased identification of functional genes involved in acute or chronic effects of volatilized psychoactive substances.

High Throughput Measurement of Locomotor Sensitization to Volatilized Cocaine in Drosophila melanogaster

PubMed Central

Filošević, Ana; Al-samarai, Sabina; Andretić Waldowski, Rozi

2018-01-01

Drosophila melanogaster can be used to identify genes with novel functional roles in neuronal plasticity induced by repeated consumption of addictive drugs. Behavioral sensitization is a relatively simple behavioral output of plastic changes that occur in the brain after repeated exposures to drugs of abuse. The development of screening procedures for genes that control behavioral sensitization has stalled due to a lack of high-throughput behavioral tests that can be used in genetically tractable organism, such as Drosophila. We have developed a new behavioral test, FlyBong, which combines delivery of volatilized cocaine (vCOC) to individually housed flies with objective quantification of their locomotor activity. There are two main advantages of FlyBong: it is high-throughput and it allows for comparisons of locomotor activity of individual flies before and after single or multiple exposures. At the population level, exposure to vCOC leads to transient and concentration-dependent increase in locomotor activity, representing sensitivity to an acute dose. A second exposure leads to further increase in locomotion, representing locomotor sensitization. We validate FlyBong by showing that locomotor sensitization at either the population or individual level is absent in the mutants for circadian genes period (per), Clock (Clk), and cycle (cyc). The locomotor sensitization that is present in timeless (tim) and pigment dispersing factor (pdf) mutant flies is in large part not cocaine specific, but derived from increased sensitivity to warm air. Circadian genes are not only integral part of the neural mechanism that is required for development of locomotor sensitization, but in addition, they modulate the intensity of locomotor sensitization as a function of the time of day. Motor-activating effects of cocaine are sexually dimorphic and require a functional dopaminergic transporter. FlyBong is a new and improved method for inducing and measuring locomotor sensitization to cocaine in individual Drosophila. Because of its high-throughput nature, FlyBong can be used in genetic screens or in selection experiments aimed at the unbiased identification of functional genes involved in acute or chronic effects of volatilized psychoactive substances. PMID:29459820

Inference of Gene Regulatory Networks Using Time-Series Data: A Survey

PubMed Central

Sima, Chao; Hua, Jianping; Jung, Sungwon

2009-01-01

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository. PMID:20190956

Emerging techniques for the discovery and validation of therapeutic targets for skeletal diseases.

PubMed

Cho, Christine H; Nuttall, Mark E

2002-12-01

Advances in genomics and proteomics have revolutionised the drug discovery process and target validation. Identification of novel therapeutic targets for chronic skeletal diseases is an extremely challenging process based on the difficulty of obtaining high-quality human diseased versus normal tissue samples. The quality of tissue and genomic information obtained from the sample is critical to identifying disease-related genes. Using a genomics-based approach, novel genes or genes with similar homology to existing genes can be identified from cDNA libraries generated from normal versus diseased tissue. High-quality cDNA libraries are prepared from uncontaminated homogeneous cell populations harvested from tissue sections of interest. Localised gene expression analysis and confirmation are obtained through in situ hybridisation or immunohistochemical studies. Cells overexpressing the recombinant protein are subsequently designed for primary cell-based high-throughput assays that are capable of screening large compound banks for potential hits. Afterwards, secondary functional assays are used to test promising compounds. The same overexpressing cells are used in the secondary assay to test protein activity and functionality as well as screen for small-molecule agonists or antagonists. Once a hit is generated, a structure-activity relationship of the compound is optimised for better oral bioavailability and pharmacokinetics allowing the compound to progress into development. Parallel efforts from proteomics, as well as genetics/transgenics, bioinformatics and combinatorial chemistry, and improvements in high-throughput automation technologies, allow the drug discovery process to meet the demands of the medicinal market. This review discusses and illustrates how different approaches are incorporated into the discovery and validation of novel targets and, consequently, the development of potentially therapeutic agents in the areas of osteoporosis and osteoarthritis. While current treatments exist in the form of hormone replacement therapy, antiresorptive and anabolic agents for osteoporosis, there are no disease-modifying therapies for the treatment of the most common human joint disease, osteoarthritis. A massive market potential for improved options with better safety and efficacy still remains. Therefore, the application of genomics and proteomics for both diseases should provide much needed novel therapeutic approaches to treating these major world health problems.

High Throughput, High Content Screening for Novel Pigmentation Regulators Using a Keratinocyte/Melanocyte Co-culture System

PubMed Central

Lee, Ju Hee; Chen, Hongxiang; Kolev, Vihren; Aull, Katherine H.; Jung, Inhee; Wang, Jun; Miyamoto, Shoko; Hosoi, Junichi; Mandinova, Anna; Fisher, David E.

2014-01-01

Skin pigmentation is a complex process including melanogenesis within melanocytes and melanin transfer to the keratinocytes. To develop a comprehensive screening method for novel pigmentation regulators, we used immortalized melanocytes and keratinocytes in co-culture to screen large numbers of compounds. High-throughput screening plates were subjected to digital automated microscopy to quantify the pigmentation via brightfield microscopy. Compounds with pigment suppression were secondarily tested for their effects on expression of MITF and several pigment regulatory genes, and further validated in terms of non-toxicity to keratinocytes/melanocytes and dose dependent activity. The results demonstrate a high-throughput, high-content screening approach, which is applicable to the analysis of large chemical libraries using a co-culture system. We identified candidate pigmentation inhibitors from 4,000 screened compounds including zoxazolamine, 3-methoxycatechol, and alpha-mangostin, which were also shown to modulate expression of MITF and several key pigmentation factors, and are worthy of further evaluation for potential translation to clinical use. PMID:24438532

High-throughput discovery of mutations in tef semi-dwarfing genes by next-generation sequencing analysis.

PubMed

Zhu, Qihui; Smith, Shavannor M; Ayele, Mulu; Yang, Lixing; Jogi, Ansuya; Chaluvadi, Srinivasa R; Bennetzen, Jeffrey L

2012-11-01

Tef (Eragrostis tef) is a major cereal crop in Ethiopia. Lodging is the primary constraint to increasing productivity in this allotetraploid species, accounting for losses of ∼15-45% in yield each year. As a first step toward identifying semi-dwarf varieties that might have improved lodging resistance, an ∼6× fosmid library was constructed and used to identify both homeologues of the dw3 semi-dwarfing gene of Sorghum bicolor. An EMS mutagenized population, consisting of ∼21,210 tef plants, was planted and leaf materials were collected into 23 superpools. Two dwarfing candidate genes, homeologues of dw3 of sorghum and rht1 of wheat, were sequenced directly from each superpool with 454 technology, and 120 candidate mutations were identified. Out of 10 candidates tested, six independent mutations were validated by Sanger sequencing, including two predicted detrimental mutations in both dw3 homeologues with a potential to improve lodging resistance in tef through further breeding. This study demonstrates that high-throughput sequencing can identify potentially valuable mutations in under-studied plant species like tef and has provided mutant lines that can now be combined and tested in breeding programs for improved lodging resistance.

Identification of in vivo regulators of the Vibrio cholerae xds gene using a high-throughput genetic selection.

PubMed

McDonough, Emilykate; Lazinski, David W; Camilli, Andrew

2014-04-01

Vibrio cholerae, the causative agent of cholera, remains a threat to public health in areas with inadequate sanitation. As a waterborne pathogen, V. cholerae moves between two dissimilar environments, aquatic reservoirs and the intestinal tract of humans. Accordingly, this pathogen undergoes adaptive shifts in gene expression throughout the different stages of its lifecycle. One particular gene, xds, encodes a secreted exonuclease that was previously identified as being induced during infection. Here we sought to identify regulators responsible for the in vivo-specific induction of xds. A transcriptional fusion of xds to two consecutive antibiotic resistance genes was used to select transposon mutants that had inserted within or adjacent to regulatory genes and thereby caused increased expression of the xds fusion under non-inducing conditions. Large pools of selected insertion sites were sequenced in a high throughput manner using Tn-seq to identify potential mechanisms of xds regulation. Our selection identified the two-component system PhoB/R as the dominant activator of xds expression. In vitro validation confirmed that PhoB, a protein which is only active during phosphate limitation, was responsible for xds activation. Using xds expression as a biosensor of the extracellular phosphate level, we observed that the mouse small intestine is a phosphate-limited environment. © 2014 John Wiley & Sons Ltd.

Rapid construction of a whole-genome transposon insertion collection for Shewanella oneidensis by Knockout Sudoku.

PubMed

Baym, Michael; Shaket, Lev; Anzai, Isao A; Adesina, Oluwakemi; Barstow, Buz

2016-11-10

Whole-genome knockout collections are invaluable for connecting gene sequence to function, yet traditionally, their construction has required an extraordinary technical effort. Here we report a method for the construction and purification of a curated whole-genome collection of single-gene transposon disruption mutants termed Knockout Sudoku. Using simple combinatorial pooling, a highly oversampled collection of mutants is condensed into a next-generation sequencing library in a single day, a 30- to 100-fold improvement over prior methods. The identities of the mutants in the collection are then solved by a probabilistic algorithm that uses internal self-consistency within the sequencing data set, followed by rapid algorithmically guided condensation to a minimal representative set of mutants, validation, and curation. Starting from a progenitor collection of 39,918 mutants, we compile a quality-controlled knockout collection of the electroactive microbe Shewanella oneidensis MR-1 containing representatives for 3,667 genes that is functionally validated by high-throughput kinetic measurements of quinone reduction.

High-throughput discovery of rare human nucleotide polymorphisms by Ecotilling

PubMed Central

Till, Bradley J.; Zerr, Troy; Bowers, Elisabeth; Greene, Elizabeth A.; Comai, Luca; Henikoff, Steven

2006-01-01

Human individuals differ from one another at only ∼0.1% of nucleotide positions, but these single nucleotide differences account for most heritable phenotypic variation. Large-scale efforts to discover and genotype human variation have been limited to common polymorphisms. However, these efforts overlook rare nucleotide changes that may contribute to phenotypic diversity and genetic disorders, including cancer. Thus, there is an increasing need for high-throughput methods to robustly detect rare nucleotide differences. Toward this end, we have adapted the mismatch discovery method known as Ecotilling for the discovery of human single nucleotide polymorphisms. To increase throughput and reduce costs, we developed a universal primer strategy and implemented algorithms for automated band detection. Ecotilling was validated by screening 90 human DNA samples for nucleotide changes in 5 gene targets and by comparing results to public resequencing data. To increase throughput for discovery of rare alleles, we pooled samples 8-fold and found Ecotilling to be efficient relative to resequencing, with a false negative rate of 5% and a false discovery rate of 4%. We identified 28 new rare alleles, including some that are predicted to damage protein function. The detection of rare damaging mutations has implications for models of human disease. PMID:16893952

High-Throughput Quantitative Lipidomics Analysis of Nonesterified Fatty Acids in Plasma by LC-MS.

PubMed

Christinat, Nicolas; Morin-Rivron, Delphine; Masoodi, Mojgan

2017-01-01

Nonesterified fatty acids are important biological molecules which have multiple functions such as energy storage, gene regulation, or cell signaling. Comprehensive profiling of nonesterified fatty acids in biofluids can facilitate studying and understanding their roles in biological systems. For these reasons, we have developed and validated a high-throughput, nontargeted lipidomics method coupling liquid chromatography to high-resolution mass spectrometry for quantitative analysis of nonesterified fatty acids. Sufficient chromatographic separation is achieved to separate positional isomers such as polyunsaturated and branched-chain species and quantify a wide range of nonesterified fatty acids in human plasma samples. However, this method is not limited only to these fatty acid species and offers the possibility to perform untargeted screening of additional nonesterified fatty acid species.

Identification of immunohistochemical markers for distinguishing lung adenocarcinoma from squamous cell carcinoma

PubMed Central

Zhan, Cheng; Yan, Li; Wang, Lin; Sun, Yang; Wang, Xingxing; Lin, Zongwu; Zhang, Yongxing; Wang, Qun

2015-01-01

Background Immunohistochemical staining has been widely used in distinguishing lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC), which is of vital importance for the diagnosis and treatment of lung cancer. Due to the lack of a comprehensive analysis of different lung cancer subtypes, there may still be undiscovered markers with higher diagnostic accuracy. Methods Herein first, we systematically analyzed high-throughput data obtained from The Cancer Genome Atlas (TCGA) database. Combining differently expressed gene screening and receiver operating characteristic (ROC) curve analysis, we attempted to identify the genes which might be suitable as immunohistochemical markers in distinguishing LUAD from LUSC. Then we detected the expression of six of these genes (MLPH, TMC5, SFTA3, DSG3, DSC3 and CALML3) in lung cancer sections using immunohistochemical staining. Results A number of genes were identified as candidate immunohistochemical markers with high sensitivity and specificity in distinguishing LUAD from LUSC. Then the staining results confirmed the potentials of the six genes (MLPH, TMC5, SFTA3, DSG3, DSC3 and CALML3) in distinguishing LUAD from LUSC, and their sensitivity and specificity were not less than many commonly used markers. Conclusions The results revealed that the six genes (MLPH, TMC5, SFTA3, DSG3, DSC3 and CALML3) might be suitable markers in distinguishing LUAD from LUSC, and also validated the feasibility of our methods for identification of candidate markers from high-throughput data. PMID:26380766

Arabidopsis Ensemble Reverse-Engineered Gene Regulatory Network Discloses Interconnected Transcription Factors in Oxidative Stress[W

PubMed Central

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-01-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. PMID:25549671

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

PubMed

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-12-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. © 2014 American Society of Plant Biologists. All rights reserved.

Comparative analysis and validation of the malachite green assay for the high throughput biochemical characterization of terpene synthases

PubMed Central

Vardakou, Maria; Salmon, Melissa; Faraldos, Juan A.; O’Maille, Paul E.

2014-01-01

Terpenes are the largest group of natural products with important and diverse biological roles, while of tremendous economic value as fragrances, flavours and pharmaceutical agents. Class-I terpene synthases (TPSs), the dominant type of TPS enzymes, catalyze the conversion of prenyl diphosphates to often structurally diverse bioactive terpene hydrocarbons, and inorganic pyrophosphate (PPi). To measure their kinetic properties, current bio-analytical methods typically rely on the direct detection of hydrocarbon products by radioactivity measurements or gas chromatography–mass spectrometry (GC–MS). In this study we employed an established, rapid colorimetric assay, the pyrophosphate/malachite green assay (MG), as an alternative means for the biochemical characterization of class I TPSs activity.•We describe the adaptation of the MG assay for turnover and catalytic efficiency measurements of TPSs.•We validate the method by direct comparison with established assays. The agreement of kcat/KM among methods makes this adaptation optimal for rapid evaluation of TPSs.•We demonstrate the application of the MG assay for the high-throughput screening of TPS gene libraries. PMID:26150952

Comparative analysis and validation of the malachite green assay for the high throughput biochemical characterization of terpene synthases.

PubMed

Vardakou, Maria; Salmon, Melissa; Faraldos, Juan A; O'Maille, Paul E

2014-01-01

Terpenes are the largest group of natural products with important and diverse biological roles, while of tremendous economic value as fragrances, flavours and pharmaceutical agents. Class-I terpene synthases (TPSs), the dominant type of TPS enzymes, catalyze the conversion of prenyl diphosphates to often structurally diverse bioactive terpene hydrocarbons, and inorganic pyrophosphate (PPi). To measure their kinetic properties, current bio-analytical methods typically rely on the direct detection of hydrocarbon products by radioactivity measurements or gas chromatography-mass spectrometry (GC-MS). In this study we employed an established, rapid colorimetric assay, the pyrophosphate/malachite green assay (MG), as an alternative means for the biochemical characterization of class I TPSs activity.•We describe the adaptation of the MG assay for turnover and catalytic efficiency measurements of TPSs.•We validate the method by direct comparison with established assays. The agreement of k cat/K M among methods makes this adaptation optimal for rapid evaluation of TPSs.•We demonstrate the application of the MG assay for the high-throughput screening of TPS gene libraries.

The draft genome sequence of cork oak

PubMed Central

Ramos, António Marcos; Usié, Ana; Barbosa, Pedro; Barros, Pedro M.; Capote, Tiago; Chaves, Inês; Simões, Fernanda; Abreu, Isabl; Carrasquinho, Isabel; Faro, Carlos; Guimarães, Joana B.; Mendonça, Diogo; Nóbrega, Filomena; Rodrigues, Leandra; Saibo, Nelson J. M.; Varela, Maria Carolina; Egas, Conceição; Matos, José; Miguel, Célia M.; Oliveira, M. Margarida; Ricardo, Cândido P.; Gonçalves, Sónia

2018-01-01

Cork oak (Quercus suber) is native to southwest Europe and northwest Africa where it plays a crucial environmental and economical role. To tackle the cork oak production and industrial challenges, advanced research is imperative but dependent on the availability of a sequenced genome. To address this, we produced the first draft version of the cork oak genome. We followed a de novo assembly strategy based on high-throughput sequence data, which generated a draft genome comprising 23,347 scaffolds and 953.3 Mb in size. A total of 79,752 genes and 83,814 transcripts were predicted, including 33,658 high-confidence genes. An InterPro signature assignment was detected for 69,218 transcripts, which represented 82.6% of the total. Validation studies demonstrated the genome assembly and annotation completeness and highlighted the usefulness of the draft genome for read mapping of high-throughput sequence data generated using different protocols. All data generated is available through the public databases where it was deposited, being therefore ready to use by the academic and industry communities working on cork oak and/or related species. PMID:29786699

The draft genome sequence of cork oak.

PubMed

Ramos, António Marcos; Usié, Ana; Barbosa, Pedro; Barros, Pedro M; Capote, Tiago; Chaves, Inês; Simões, Fernanda; Abreu, Isabl; Carrasquinho, Isabel; Faro, Carlos; Guimarães, Joana B; Mendonça, Diogo; Nóbrega, Filomena; Rodrigues, Leandra; Saibo, Nelson J M; Varela, Maria Carolina; Egas, Conceição; Matos, José; Miguel, Célia M; Oliveira, M Margarida; Ricardo, Cândido P; Gonçalves, Sónia

2018-05-22

Cork oak (Quercus suber) is native to southwest Europe and northwest Africa where it plays a crucial environmental and economical role. To tackle the cork oak production and industrial challenges, advanced research is imperative but dependent on the availability of a sequenced genome. To address this, we produced the first draft version of the cork oak genome. We followed a de novo assembly strategy based on high-throughput sequence data, which generated a draft genome comprising 23,347 scaffolds and 953.3 Mb in size. A total of 79,752 genes and 83,814 transcripts were predicted, including 33,658 high-confidence genes. An InterPro signature assignment was detected for 69,218 transcripts, which represented 82.6% of the total. Validation studies demonstrated the genome assembly and annotation completeness and highlighted the usefulness of the draft genome for read mapping of high-throughput sequence data generated using different protocols. All data generated is available through the public databases where it was deposited, being therefore ready to use by the academic and industry communities working on cork oak and/or related species.

Modelling Human Regulatory Variation in Mouse: Finding the Function in Genome-Wide Association Studies and Whole-Genome Sequencing

PubMed Central

Schmouth, Jean-François; Bonaguro, Russell J.; Corso-Diaz, Ximena; Simpson, Elizabeth M.

2012-01-01

An increasing body of literature from genome-wide association studies and human whole-genome sequencing highlights the identification of large numbers of candidate regulatory variants of potential therapeutic interest in numerous diseases. Our relatively poor understanding of the functions of non-coding genomic sequence, and the slow and laborious process of experimental validation of the functional significance of human regulatory variants, limits our ability to fully benefit from this information in our efforts to comprehend human disease. Humanized mouse models (HuMMs), in which human genes are introduced into the mouse, suggest an approach to this problem. In the past, HuMMs have been used successfully to study human disease variants; e.g., the complex genetic condition arising from Down syndrome, common monogenic disorders such as Huntington disease and β-thalassemia, and cancer susceptibility genes such as BRCA1. In this commentary, we highlight a novel method for high-throughput single-copy site-specific generation of HuMMs entitled High-throughput Human Genes on the X Chromosome (HuGX). This method can be applied to most human genes for which a bacterial artificial chromosome (BAC) construct can be derived and a mouse-null allele exists. This strategy comprises (1) the use of recombineering technology to create a human variant–harbouring BAC, (2) knock-in of this BAC into the mouse genome using Hprt docking technology, and (3) allele comparison by interspecies complementation. We demonstrate the throughput of the HuGX method by generating a series of seven different alleles for the human NR2E1 gene at Hprt. In future challenges, we consider the current limitations of experimental approaches and call for a concerted effort by the genetics community, for both human and mouse, to solve the challenge of the functional analysis of human regulatory variation. PMID:22396661

MicroRNA from Moringa oleifera: Identification by High Throughput Sequencing and Their Potential Contribution to Plant Medicinal Value.

PubMed

Pirrò, Stefano; Zanella, Letizia; Kenzo, Maurice; Montesano, Carla; Minutolo, Antonella; Potestà, Marina; Sobze, Martin Sanou; Canini, Antonella; Cirilli, Marco; Muleo, Rosario; Colizzi, Vittorio; Galgani, Andrea

2016-01-01

Moringa oleifera is a widespread plant with substantial nutritional and medicinal value. We postulated that microRNAs (miRNAs), which are endogenous, noncoding small RNAs regulating gene expression at the post-transcriptional level, might contribute to the medicinal properties of plants of this species after ingestion into human body, regulating human gene expression. However, the knowledge is scarce about miRNA in Moringa. Furthermore, in order to test the hypothesis on the pharmacological potential properties of miRNA, we conducted a high-throughput sequencing analysis using the Illumina platform. A total of 31,290,964 raw reads were produced from a library of small RNA isolated from M. oleifera seeds. We identified 94 conserved and two novel miRNAs that were validated by qRT-PCR assays. Results from qRT-PCR trials conducted on the expression of 20 Moringa miRNA showed that are conserved across multiple plant species as determined by their detection in tissue of other common crop plants. In silico analyses predicted target genes for the conserved miRNA that in turn allowed to relate the miRNAs to the regulation of physiological processes. Some of the predicted plant miRNAs have functional homology to their mammalian counterparts and regulated human genes when they were transfected into cell lines. To our knowledge, this is the first report of discovering M. oleifera miRNAs based on high-throughput sequencing and bioinformatics analysis and we provided new insight into a potential cross-species control of human gene expression. The widespread cultivation and consumption of M. oleifera, for nutritional and medicinal purposes, brings humans into close contact with products and extracts of this plant species. The potential for miRNA transfer should be evaluated as one possible mechanism of action to account for beneficial properties of this valuable species.

Next Generation MUT-MAP, a High-Sensitivity High-Throughput Microfluidics Chip-Based Mutation Analysis Panel

PubMed Central

Patel, Rajesh; Tsan, Alison; Sumiyoshi, Teiko; Fu, Ling; Desai, Rupal; Schoenbrunner, Nancy; Myers, Thomas W.; Bauer, Keith; Smith, Edward; Raja, Rajiv

2014-01-01

Molecular profiling of tumor tissue to detect alterations, such as oncogenic mutations, plays a vital role in determining treatment options in oncology. Hence, there is an increasing need for a robust and high-throughput technology to detect oncogenic hotspot mutations. Although commercial assays are available to detect genetic alterations in single genes, only a limited amount of tissue is often available from patients, requiring multiplexing to allow for simultaneous detection of mutations in many genes using low DNA input. Even though next-generation sequencing (NGS) platforms provide powerful tools for this purpose, they face challenges such as high cost, large DNA input requirement, complex data analysis, and long turnaround times, limiting their use in clinical settings. We report the development of the next generation mutation multi-analyte panel (MUT-MAP), a high-throughput microfluidic, panel for detecting 120 somatic mutations across eleven genes of therapeutic interest (AKT1, BRAF, EGFR, FGFR3, FLT3, HRAS, KIT, KRAS, MET, NRAS, and PIK3CA) using allele-specific PCR (AS-PCR) and Taqman technology. This mutation panel requires as little as 2 ng of high quality DNA from fresh frozen or 100 ng of DNA from formalin-fixed paraffin-embedded (FFPE) tissues. Mutation calls, including an automated data analysis process, have been implemented to run 88 samples per day. Validation of this platform using plasmids showed robust signal and low cross-reactivity in all of the newly added assays and mutation calls in cell line samples were found to be consistent with the Catalogue of Somatic Mutations in Cancer (COSMIC) database allowing for direct comparison of our platform to Sanger sequencing. High correlation with NGS when compared to the SuraSeq500 panel run on the Ion Torrent platform in a FFPE dilution experiment showed assay sensitivity down to 0.45%. This multiplexed mutation panel is a valuable tool for high-throughput biomarker discovery in personalized medicine and cancer drug development. PMID:24658394

High-resolution phylogenetic microbial community profiling

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

Singer, Esther; Bushnell, Brian; Coleman-Derr, Devin

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structuresmore » at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.« less

High-resolution phylogenetic microbial community profiling

DOE PAGES

Singer, Esther; Bushnell, Brian; Coleman-Derr, Devin; ...

2016-02-09

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structuresmore » at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.« less

Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

PubMed Central

Dutta, Noton K.; Bandyopadhyay, Nirmalya; Veeramani, Balaji; Lamichhane, Gyanu; Karakousis, Petros C.; Bader, Joel S.

2014-01-01

ABSTRACT Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes. PMID:24549847

A novel pooled-sample multiplex luminex assay for high-throughput measurement of relative telomere length.

PubMed

Jasmine, Farzana; Shinkle, Justin; Sabarinathan, Mekala; Ahsan, Habibul; Pierce, Brandon L; Kibriya, Muhammad G

2018-03-12

Relative telomere length (RTL) is a potential biomarker of aging and risk for chronic disease. Previously, we developed a probe-based RTL assay on Luminex platform, where probes for Telomere (T) and reference gene (R) for a given DNA sample were tested in a single well. Here, we describe a method of pooling multiple samples in one well to increase the throughput and cost-effectiveness. We used four different microbeads for the same T-probe and four different microbeads for the same R-probe. Each pair of probe sets were hybridized to DNA in separate plates and then pooled in a single plate for all the subsequent steps. We used DNA samples from 60 independent individuals and repeated in multiple batches to test the precision. The precision was good to excellent with Intraclass correlation coefficient (ICC) of 0.908 (95% CI 0.856-0.942). More than 67% of the variation in the RTL could be explained by sample-to-sample variation; less than 0.1% variation was due to batch-to-batch variation and 0.3% variation was explained by bead-to-bead variation. We increased the throughput of RTL Luminex assay from 60 to 240 samples per run. The new assay was validated against the original Luminex assay without pooling (r = 0.79, P = 1.44 × 10 -15 ). In an independent set of samples (n = 550), the new assay showed a negative correlation of RTL with age (r = -0.41), a result providing external validation for the method. We describe a novel high throughput pooled-sample multiplex Luminex assay for RTL with good to excellent precision suitable for large-scale studies. © 2018 Wiley Periodicals, Inc.

ADHD-associated dopamine transporter, latrophilin and neurofibromin share a dopamine-related locomotor signature in Drosophila

PubMed Central

van der Voet, M; Harich, B; Franke, B; Schenck, A

2016-01-01

Attention-deficit/hyperactivity disorder (ADHD) is a common, highly heritable neuropsychiatric disorder with hyperactivity as one of the hallmarks. Aberrant dopamine signaling is thought to be a major theme in ADHD, but how this relates to the vast majority of ADHD candidate genes is illusive. Here we report a Drosophila dopamine-related locomotor endophenotype that is shared by pan-neuronal knockdown of orthologs of the ADHD-associated genes Dopamine transporter (DAT1) and Latrophilin (LPHN3), and of a gene causing a monogenic disorder with frequent ADHD comorbidity: Neurofibromin (NF1). The locomotor signature was not found in control models and could be ameliorated by methylphenidate, validating its relevance to symptoms of the disorder. The Drosophila ADHD endophenotype can be further exploited in high throughput to characterize the growing number of candidate genes. It represents an equally useful outcome measure for testing chemical compounds to define novel treatment options. PMID:25962619

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

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

Coradetti, Samuel T.; Pinel, Dominic; Geiselman, Gina M.

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted functionmore » in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. Lastly, these results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.« less

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

PubMed Central

Geiselman, Gina M; Ito, Masakazu; Mondo, Stephen J; Reilly, Morgann C; Cheng, Ya-Fang; Bauer, Stefan; Grigoriev, Igor V; Gladden, John M; Simmons, Blake A; Brem, Rachel B

2018-01-01

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted function in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. These results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi. PMID:29521624

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

DOE PAGES

Coradetti, Samuel T.; Pinel, Dominic; Geiselman, Gina M.; ...

2018-03-09

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted functionmore » in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. Lastly, these results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.« less

An ion channel library for drug discovery and safety screening on automated platforms.

PubMed

Wible, Barbara A; Kuryshev, Yuri A; Smith, Stephen S; Liu, Zhiqi; Brown, Arthur M

2008-12-01

Ion channels represent the third largest class of targets in drug discovery after G-protein coupled receptors and kinases. In spite of this ranking, ion channels continue to be under exploited as drug targets compared with the other two groups for several reasons. First, with 400 ion channel genes and an even greater number of functional channels due to mixing and matching of individual subunits, a systematic collection of ion channel-expressing cell lines for drug discovery and safety screening has not been available. Second, the lack of high-throughput functional assays for ion channels has limited their use as drug targets. Now that automated electrophysiology has come of age and provided the technology to assay ion channels at medium to high throughput, we have addressed the need for a library of ion channel cell lines by constructing the Ion Channel Panel (ChanTest Corp., Cleveland, OH). From 400 ion channel genes, a collection of 82 of the most relevant human ion channels for drug discovery, safety, and human disease has been assembled.Each channel has been stably overexpressed in human embryonic kidney 293 or Chinese hamster ovary cells. Cell lines have been selected and validated on automated electrophysiology systems to facilitate cost-effective screening for safe and selective compounds at earlier stages in the drug development process. The screening and validation processes as well as the relative advantages of different screening platforms are discussed.

FlyPrimerBank: An Online Database for Drosophila melanogaster Gene Expression Analysis and Knockdown Evaluation of RNAi Reagents

PubMed Central

Hu, Yanhui; Sopko, Richelle; Foos, Marianna; Kelley, Colleen; Flockhart, Ian; Ammeux, Noemie; Wang, Xiaowei; Perkins, Lizabeth; Perrimon, Norbert; Mohr, Stephanie E.

2013-01-01

The evaluation of specific endogenous transcript levels is important for understanding transcriptional regulation. More specifically, it is useful for independent confirmation of results obtained by the use of microarray analysis or RNA-seq and for evaluating RNA interference (RNAi)-mediated gene knockdown. Designing specific and effective primers for high-quality, moderate-throughput evaluation of transcript levels, i.e., quantitative, real-time PCR (qPCR), is nontrivial. To meet community needs, predefined qPCR primer pairs for mammalian genes have been designed and sequences made available, e.g., via PrimerBank. In this work, we adapted and refined the algorithms used for the mammalian PrimerBank to design 45,417 primer pairs for 13,860 Drosophila melanogaster genes, with three or more primer pairs per gene. We experimentally validated primer pairs for ~300 randomly selected genes expressed in early Drosophila embryos, using SYBR Green-based qPCR and sequence analysis of products derived from conventional PCR. All relevant information, including primer sequences, isoform specificity, spatial transcript targeting, and any available validation results and/or user feedback, is available from an online database (www.flyrnai.org/flyprimerbank). At FlyPrimerBank, researchers can retrieve primer information for fly genes either one gene at a time or in batch mode. Importantly, we included the overlap of each predicted amplified sequence with RNAi reagents from several public resources, making it possible for researchers to choose primers suitable for knockdown evaluation of RNAi reagents (i.e., to avoid amplification of the RNAi reagent itself). We demonstrate the utility of this resource for validation of RNAi reagents in vivo. PMID:23893746

Illumina whole-genome complementary DNA-mediated annealing, selection, extension and ligation platform: assessing its performance in formalin-fixed, paraffin-embedded samples and identifying invasion pattern-related genes in oral squamous cell carcinoma.

PubMed

Loudig, Olivier; Brandwein-Gensler, Margaret; Kim, Ryung S; Lin, Juan; Isayeva, Tatyana; Liu, Christina; Segall, Jeffrey E; Kenny, Paraic A; Prystowsky, Michael B

2011-12-01

High-throughput gene expression profiling from formalin-fixed, paraffin-embedded tissues has become a reality, and several methods are now commercially available. The Illumina whole-genome complementary DNA-mediated annealing, selection, extension and ligation assay (Illumina, Inc) is a full-transcriptome version of the original 512-gene complementary DNA-mediated annealing, selection, extension and ligation assay, allowing high-throughput profiling of 24,526 annotated genes from degraded and formalin-fixed, paraffin-embedded RNA. This assay has the potential to allow identification of novel gene signatures associated with clinical outcome using banked archival pathology specimen resources. We tested the reproducibility of the whole-genome complementary DNA-mediated annealing, selection, extension and ligation assay and its sensitivity for detecting differentially expressed genes in RNA extracted from matched fresh and formalin-fixed, paraffin-embedded cells, after 1 and 13 months of storage, using the human breast cell lines MCF7 and MCF10A. Then, using tumor worst pattern of invasion as a classifier, 1 component of the "risk model," we selected 12 formalin-fixed, paraffin-embedded oral squamous cell carcinomas for whole-genome complementary DNA-mediated annealing, selection, extension and ligation assay analysis. We profiled 5 tumors with nonaggressive, nondispersed pattern of invasion, and 7 tumors with aggressive dispersed pattern of invasion and satellites scattered at least 1 mm apart. To minimize variability, the formalin-fixed, paraffin-embedded specimens were prepared from snap-frozen tissues, and RNA was obtained within 24 hours of fixation. One hundred four down-regulated genes and 72 up-regulated genes in tumors with aggressive dispersed pattern of invasion were identified. We performed quantitative reverse transcriptase polymerase chain reaction validation of 4 genes using Taqman assays and in situ protein detection of 1 gene by immunohistochemistry. Functional cluster analysis of genes up-regulated in tumors with aggressive pattern of invasion suggests presence of genes involved in cellular cytoarchitecture, some of which already associated with tumor invasion. Identification of these genes provides biologic rationale for our histologic classification, with regard to tumor invasion, and demonstrates that the whole-genome complementary DNA-mediated annealing, selection, extension and ligation assay is a powerful assay for profiling degraded RNA from archived specimens when combined with quantitative reverse transcriptase polymerase chain reaction validation. Copyright © 2011 Elsevier Inc. All rights reserved.

High-throughput sequencing of the T cell receptor β gene identifies aggressive early-stage mycosis fungoides.

PubMed

de Masson, Adele; O'Malley, John T; Elco, Christopher P; Garcia, Sarah S; Divito, Sherrie J; Lowry, Elizabeth L; Tawa, Marianne; Fisher, David C; Devlin, Phillip M; Teague, Jessica E; Leboeuf, Nicole R; Kirsch, Ilan R; Robins, Harlan; Clark, Rachael A; Kupper, Thomas S

2018-05-09

Mycosis fungoides (MF), the most common cutaneous T cell lymphoma (CTCL) is a malignancy of skin-tropic memory T cells. Most MF cases present as early stage (stage I A/B, limited to the skin), and these patients typically have a chronic, indolent clinical course. However, a small subset of early-stage cases develop progressive and fatal disease. Because outcomes can be so different, early identification of this high-risk population is an urgent unmet clinical need. We evaluated the use of next-generation high-throughput DNA sequencing of the T cell receptor β gene ( TCRB ) in lesional skin biopsies to predict progression and survival in a discovery cohort of 208 patients with CTCL (177 with MF) from a 15-year longitudinal observational clinical study. We compared these data to the results in an independent validation cohort of 101 CTCL patients (87 with MF). The tumor clone frequency (TCF) in lesional skin, measured by high-throughput sequencing of the TCRB gene, was an independent prognostic factor of both progression-free and overall survival in patients with CTCL and MF in particular. In early-stage patients, a TCF of >25% in the skin was a stronger predictor of progression than any other established prognostic factor (stage IB versus IA, presence of plaques, high blood lactate dehydrogenase concentration, large-cell transformation, or age). The TCF therefore may accurately predict disease progression in early-stage MF. Early identification of patients at high risk for progression could help identify candidates who may benefit from allogeneic hematopoietic stem cell transplantation before their disease becomes treatment-refractory. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

High-Throughput Genotyping of Single Nucleotide Polymorphisms in the Plasmodium falciparum dhfr Gene by Asymmetric PCR and Melt-Curve Analysis▿

PubMed Central

Cruz, Rochelle E.; Shokoples, Sandra E.; Manage, Dammika P.; Yanow, Stephanie K.

2010-01-01

Mutations within the Plasmodium falciparum dihydrofolate reductase gene (Pfdhfr) contribute to resistance to antimalarials such as sulfadoxine-pyrimethamine (SP). Of particular importance are the single nucleotide polymorphisms (SNPs) within codons 51, 59, 108, and 164 in the Pfdhfr gene that are associated with SP treatment failure. Given that traditional genotyping methods are time-consuming and laborious, we developed an assay that provides the rapid, high-throughput analysis of parasite DNA isolated from clinical samples. This assay is based on asymmetric real-time PCR and melt-curve analysis (MCA) performed on the LightCycler platform. Unlabeled probes specific to each SNP are included in the reaction mixture and hybridize differentially to the mutant and wild-type sequences within the amplicon, generating distinct melting curves. Since the probe is present throughout PCR and MCA, the assay proceeds seamlessly with no further addition of reagents. This assay was validated for analytical sensitivity and specificity using plasmids, purified genomic DNA from reference strains, and parasite cultures. For all four SNPs, correct genotypes were identified with 100 copies of the template. The performance of the assay was evaluated with a blind panel of clinical isolates from travelers with low-level parasitemia. The concordance between our assay and DNA sequencing ranged from 84 to 100% depending on the SNP. We also directly compared our MCA assay to a published TaqMan real-time PCR assay and identified major issues with the specificity of the TaqMan probes. Our assay provides a number of technical improvements that facilitate the high-throughput screening of patient samples to identify SP-resistant malaria. PMID:20631115

Development of a High-Throughput Resequencing Array for the Detection of Pathogenic Mutations in Osteogenesis Imperfecta

PubMed Central

Wang, Yao; Cui, Yazhou; Zhou, Xiaoyan; Han, Jinxiang

2015-01-01

Objective Osteogenesis imperfecta (OI) is a rare inherited skeletal disease, characterized by bone fragility and low bone density. The mutations in this disorder have been widely reported to be on various exonal hotspots of the candidate genes, including COL1A1, COL1A2, CRTAP, LEPRE1, and FKBP10, thus creating a great demand for precise genetic tests. However, large genome sizes make the process daunting and the analyses, inefficient and expensive. Therefore, we aimed at developing a fast, accurate, efficient, and cheaper sequencing platform for OI diagnosis; and to this end, use of an advanced array-based technique was proposed. Method A CustomSeq Affymetrix Resequencing Array was established for high-throughput sequencing of five genes simultaneously. Genomic DNA extraction from 13 OI patients and 85 normal controls and amplification using long-range PCR (LR-PCR) were followed by DNA fragmentation and chip hybridization, according to standard Affymetrix protocols. Hybridization signals were determined using GeneChip Sequence Analysis Software (GSEQ). To examine the feasibility, the outcome from new resequencing approach was validated by conventional capillary sequencing method. Result Overall call rates using resequencing array was 96–98% and the agreement between microarray and capillary sequencing was 99.99%. 11 out of 13 OI patients with pathogenic mutations were successfully detected by the chip analysis without adjustment, and one mutation could also be identified using manual visual inspection. Conclusion A high-throughput resequencing array was developed that detects the disease-associated mutations in OI, providing a potential tool to facilitate large-scale genetic screening for OI patients. Through this method, a novel mutation was also found. PMID:25742658

Transcriptome profiling and pathway analysis of hepatotoxicity induced by tris (2-ethylhexyl) trimellitate (TOTM) in mice.

PubMed

Chen, Xian-Hua; Ma, Li; Hu, Yi-Xiang; Wang, Dan-Xian; Fang, Li; Li, Xue-Lai; Zhao, Jin-Chuan; Yu, Hai-Rong; Ying, Hua-Zhong; Yu, Chen-Huan

2016-01-01

Tris (2-ethylhexyl) trimellitate (TOTM) is commonly used as an alternative plasticizer for medical devices. But very little information was available on its biological effects. In this study, we investigated toxicity effects of TOTM on hepatic differential gene expression analyzed by using high-throughput sequencing analysis for over-represented functions and phenotypically anchored to complementary histopathologic, and biochemical data in the liver of mice. Among 1668 candidate genes, 694 genes were up-regulated and 974 genes were down-regulated after TOTM exposure. Using Gene Ontology analysis, TOTM affected three processes: the cell cycle, metabolic process and oxidative activity. Furthermore, 11 key genes involved in the above processes were validated by real time PCR. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that these genes were involved in the cell cycle pathway, lipid metabolism and oxidative process. It revealed the transcriptome gene expression response to TOTM exposure in mouse, and these data could contribute to provide a clearer understanding of the molecular mechanisms of TOTM-induced hepatotoxicity in human. Copyright © 2015 Elsevier B.V. All rights reserved.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

PubMed

Filzen, Tracey M; Kutchukian, Peter S; Hermes, Jeffrey D; Li, Jing; Tudor, Matthew

2017-02-01

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets

PubMed Central

Kutchukian, Peter S.; Li, Jing; Tudor, Matthew

2017-01-01

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound’s high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data. PMID:28182661

Uncovering leaf rust responsive miRNAs in wheat (Triticum aestivum L.) using high-throughput sequencing and prediction of their targets through degradome analysis.

PubMed

Kumar, Dhananjay; Dutta, Summi; Singh, Dharmendra; Prabhu, Kumble Vinod; Kumar, Manish; Mukhopadhyay, Kunal

2017-01-01

Deep sequencing identified 497 conserved and 559 novel miRNAs in wheat, while degradome analysis revealed 701 targets genes. QRT-PCR demonstrated differential expression of miRNAs during stages of leaf rust progression. Bread wheat (Triticum aestivum L.) is an important cereal food crop feeding 30 % of the world population. Major threat to wheat production is the rust epidemics. This study was targeted towards identification and functional characterizations of micro(mi)RNAs and their target genes in wheat in response to leaf rust ingression. High-throughput sequencing was used for transcriptome-wide identification of miRNAs and their expression profiling in retort to leaf rust using mock and pathogen-inoculated resistant and susceptible near-isogenic wheat plants. A total of 1056 mature miRNAs were identified, of which 497 miRNAs were conserved and 559 miRNAs were novel. The pathogen-inoculated resistant plants manifested more miRNAs compared with the pathogen infected susceptible plants. The miRNA counts increased in susceptible isoline due to leaf rust, conversely, the counts decreased in the resistant isoline in response to pathogenesis illustrating precise spatial tuning of miRNAs during compatible and incompatible interaction. Stem-loop quantitative real-time PCR was used to profile 10 highly differentially expressed miRNAs obtained from high-throughput sequencing data. The spatio-temporal profiling validated the differential expression of miRNAs between the isolines as well as in retort to pathogen infection. Degradome analysis provided 701 predicted target genes associated with defense response, signal transduction, development, metabolism, and transcriptional regulation. The obtained results indicate that wheat isolines employ diverse arrays of miRNAs that modulate their target genes during compatible and incompatible interaction. Our findings contribute to increase knowledge on roles of microRNA in wheat-leaf rust interactions and could help in rust resistance breeding programs.

High-throughput real-time quantitative reverse transcription PCR.

PubMed

Bookout, Angie L; Cummins, Carolyn L; Mangelsdorf, David J; Pesola, Jean M; Kramer, Martha F

2006-02-01

Extensive detail on the application of the real-time quantitative polymerase chain reaction (QPCR) for the analysis of gene expression is provided in this unit. The protocols are designed for high-throughput, 384-well-format instruments, such as the Applied Biosystems 7900HT, but may be modified to suit any real-time PCR instrument. QPCR primer and probe design and validation are discussed, and three relative quantitation methods are described: the standard curve method, the efficiency-corrected DeltaCt method, and the comparative cycle time, or DeltaDeltaCt method. In addition, a method is provided for absolute quantification of RNA in unknown samples. RNA standards are subjected to RT-PCR in the same manner as the experimental samples, thus accounting for the reaction efficiencies of both procedures. This protocol describes the production and quantitation of synthetic RNA molecules for real-time and non-real-time RT-PCR applications.

MMTV insertional mutagenesis identifies genes, gene families and pathways involved in mammary cancer.

PubMed

Theodorou, Vassiliki; Kimm, Melanie A; Boer, Mandy; Wessels, Lodewyk; Theelen, Wendy; Jonkers, Jos; Hilkens, John

2007-06-01

We performed a high-throughput retroviral insertional mutagenesis screen in mouse mammary tumor virus (MMTV)-induced mammary tumors and identified 33 common insertion sites, of which 17 genes were previously not known to be associated with mammary cancer and 13 had not previously been linked to cancer in general. Although members of the Wnt and fibroblast growth factors (Fgf) families were frequently tagged, our exhaustive screening for MMTV insertion sites uncovered a new repertoire of candidate breast cancer oncogenes. We validated one of these genes, Rspo3, as an oncogene by overexpression in a p53-deficient mammary epithelial cell line. The human orthologs of the candidate oncogenes were frequently deregulated in human breast cancers and associated with several tumor parameters. Computational analysis of all MMTV-tagged genes uncovered specific gene families not previously associated with cancer and showed a significant overrepresentation of protein domains and signaling pathways mainly associated with development and growth factor signaling. Comparison of all tagged genes in MMTV and Moloney murine leukemia virus-induced malignancies showed that both viruses target mostly different genes that act predominantly in distinct pathways.

Phylogenomics of plant genomes: a methodology for genome-wide searches for orthologs in plants

PubMed Central

Conte, Matthieu G; Gaillard, Sylvain; Droc, Gaetan; Perin, Christophe

2008-01-01

Background Gene ortholog identification is now a major objective for mining the increasing amount of sequence data generated by complete or partial genome sequencing projects. Comparative and functional genomics urgently need a method for ortholog detection to reduce gene function inference and to aid in the identification of conserved or divergent genetic pathways between several species. As gene functions change during evolution, reconstructing the evolutionary history of genes should be a more accurate way to differentiate orthologs from paralogs. Phylogenomics takes into account phylogenetic information from high-throughput genome annotation and is the most straightforward way to infer orthologs. However, procedures for automatic detection of orthologs are still scarce and suffer from several limitations. Results We developed a procedure for ortholog prediction between Oryza sativa and Arabidopsis thaliana. Firstly, we established an efficient method to cluster A. thaliana and O. sativa full proteomes into gene families. Then, we developed an optimized phylogenomics pipeline for ortholog inference. We validated the full procedure using test sets of orthologs and paralogs to demonstrate that our method outperforms pairwise methods for ortholog predictions. Conclusion Our procedure achieved a high level of accuracy in predicting ortholog and paralog relationships. Phylogenomic predictions for all validated gene families in both species were easily achieved and we can conclude that our methodology outperforms similarly based methods. PMID:18426584

High-Throughput Sequencing Identifies MicroRNAs from Posterior Intestine of Loach (Misgurnus anguillicaudatus) and Their Response to Intestinal Air-Breathing Inhibition.

PubMed

Huang, Songqian; Cao, Xiaojuan; Tian, Xianchang; Wang, Weimin

2016-01-01

MicroRNAs (miRNAs) exert important roles in animal growth, immunity, and development, and regulate gene expression at the post-transcriptional level. Knowledges about the diversities of miRNAs and their roles in accessory air-breathing organs (ABOs) of fish remain unknown. In this work, we used high-throughput sequencing to identify known and novel miRNAs from the posterior intestine, an important ABO, in loach (Misgurnus anguillicaudatus) under normal and intestinal air-breathing inhibited conditions. A total of 204 known and 84 novel miRNAs were identified, while 47 miRNAs were differentially expressed between the two small RNA libraries (i.e. between the normal and intestinal air-breathing inhibited group). Potential miRNA target genes were predicted by combining our transcriptome data of the posterior intestine of the loach under the same conditions, and then annotated using COG, GO, KEGG, Swissprot and Nr databases. The regulatory networks of miRNAs and their target genes were analyzed. The abundances of nine known miRNAs were validated by qRT-PCR. The relative expression profiles of six known miRNAs and their eight corresponding target genes, and two novel potential miRNAs were also detected. Histological characteristics of the posterior intestines in both normal and air-breathing inhibited group were further analyzed. This study contributes to our understanding on the functions and molecular regulatory mechanisms of miRNAs in accessory air-breathing organs of fish.

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments.

PubMed

Zhou, Bailing; Zhao, Huiying; Yu, Jiafeng; Guo, Chengang; Dou, Xianghua; Song, Feng; Hu, Guodong; Cao, Zanxia; Qu, Yuanxu; Yang, Yuedong; Zhou, Yaoqi; Wang, Jihua

2018-01-04

Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments

PubMed Central

Zhao, Huiying; Yu, Jiafeng; Guo, Chengang; Dou, Xianghua; Song, Feng; Hu, Guodong; Cao, Zanxia; Qu, Yuanxu

2018-01-01

Abstract Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs. PMID:28985416

Perspectives on Validation of High-Throughput Assays Supporting 21st Century Toxicity Testing

EPA Science Inventory

In vitro high-throughput screening (HTS) assays are seeing increasing use in toxicity testing. HTS assays can simultaneously test many chemicals but have seen limited use in the regulatory arena, in part because of the need to undergo rigorous, time-consuming formal validation. ...

Development of an Efficient Genome Editing Method by CRISPR/Cas9 in a Fish Cell Line.

PubMed

Dehler, Carola E; Boudinot, Pierre; Martin, Samuel A M; Collet, Bertrand

2016-08-01

CRISPR/Cas9 system has been used widely in animals and plants to direct mutagenesis. To date, no such method exists for fish somatic cell lines. We describe an efficient procedure for genome editing in the Chinook salmon Oncorhynchus tshawytscha CHSE. This cell line was genetically modified to firstly overexpress a monomeric form of EGFP (cell line CHSE-E Geneticin resistant) and additionally to overexpress nCas9n, a nuclear version of Cas9 (cell line CHSE-EC, Hygromycin and Geneticin resistant). A pre-validated sgRNA was produced in vitro and used to transfect CHSE-EC cells. The EGFP gene was disrupted in 34.6 % of cells, as estimated by FACS and microscopy. The targeted locus was characterised by PCR amplification, cloning and sequencing of PCR products; inactivation of the EGFP gene by deletions in the expected site was validated in 25 % of clones. This method opens perspectives for functional genomic studies compatible with high-throughput screening.

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.

PubMed

Nabavi, Sheida

2016-08-15

With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

Laminar and dorsoventral molecular organization of the medial entorhinal cortex revealed by large-scale anatomical analysis of gene expression.

PubMed

Ramsden, Helen L; Sürmeli, Gülşen; McDonagh, Steven G; Nolan, Matthew F

2015-01-01

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations.

Laminar and Dorsoventral Molecular Organization of the Medial Entorhinal Cortex Revealed by Large-scale Anatomical Analysis of Gene Expression

PubMed Central

Ramsden, Helen L.; Sürmeli, Gülşen; McDonagh, Steven G.; Nolan, Matthew F.

2015-01-01

Neural circuits in the medial entorhinal cortex (MEC) encode an animal’s position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations. PMID:25615592

Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

PubMed Central

Yang, Wanneng; Guo, Zilong; Huang, Chenglong; Duan, Lingfeng; Chen, Guoxing; Jiang, Ni; Fang, Wei; Feng, Hui; Xie, Weibo; Lian, Xingming; Wang, Gongwei; Luo, Qingming; Zhang, Qifa; Liu, Qian; Xiong, Lizhong

2014-01-01

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits. PMID:25295980

Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis.

PubMed

Koo, Byoung-Mo; Kritikos, George; Farelli, Jeremiah D; Todor, Horia; Tong, Kenneth; Kimsey, Harvey; Wapinski, Ilan; Galardini, Marco; Cabal, Angelo; Peters, Jason M; Hachmann, Anna-Barbara; Rudner, David Z; Allen, Karen N; Typas, Athanasios; Gross, Carol A

2017-03-22

A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Comprehensive circular RNA expression profile in radiation-treated HeLa cells and analysis of radioresistance-related circRNAs.

PubMed

Yu, Duo; Li, Yunfeng; Ming, Zhihui; Wang, Hongyong; Dong, Zhuo; Qiu, Ling; Wang, Tiejun

2018-01-01

Cervical cancer is one of the most common cancers in women worldwide. Malignant tumors develop resistance mechanisms and are less sensitive to or do not respond to irradiation. With the development of high-throughput sequencing technologies, circular RNA (circRNA) has been identified in an increasing number of diseases, especially cancers. It has been reported that circRNA can compete with microRNAs (miRNAs) to change the stability or translation of target RNAs, thus regulating gene expression at the transcriptional level. However, the role of circRNAs in cervical cancer and the radioresistance mechanisms of HeLa cells are unknown. The objective of this study is to investigate the role of circRNAs in radioresistance in HeLa cells. High-throughput sequencing and bioinformatics analysis of irradiated and sham-irradiated HeLa cells. The reliability of high-throughput RNA sequencing was validated using quantitative real-time polymerase chain reaction. The most significant circRNA functions and pathways were selected by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A circRNA-miRNA-target gene interaction network was used to find circRNAs associated with radioresistance. Moreover, a protein-protein interaction network was constructed to identify radioresistance-related hub proteins. High-throughput sequencing allowed the identification of 16,893 circRNAs involved in the response of HeLa cells to radiation. Compared with the control group, there were 153 differentially expressed circRNAs, of which 76 were up-regulated and 77 were down-regulated. GO covered three domains: biological process (BP), cellular component (CC) and molecular function (MF). The terms assigned to the BP domain were peptidyl-tyrosine dephosphorylation and regulation of cell migration. The identified CC terms were cell-cell adherens junction, nucleoplasm and cytosol, and the identified MF terms were protein binding and protein tyrosine phosphatase activity. The top five KEGG pathways were MAPK signaling pathway, endocytosis, axon guidance, neurotrophin signaling pathway, and SNARE interactions in vesicular transport. The protein-protein interaction analysis indicated that 19 proteins might be hub proteins. CircRNAs may play a major role in the response to radiation. These findings may improve our understanding of the role of circRNAs in radioresistance in HeLa cells and allow the development of novel therapeutic approaches.

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.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.

PubMed

Ludovini, Vienna; Bianconi, Fortunato; Siggillino, Annamaria; Piobbico, Danilo; Vannucci, Jacopo; Metro, Giulio; Chiari, Rita; Bellezza, Guido; Puma, Francesco; Della Fazia, Maria Agnese; Servillo, Giuseppe; Crinò, Lucio

2016-05-24

Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

PubMed Central

Yi, Ming; Stephens, Robert M.

2008-01-01

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. PMID:18818771

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.

Identification of microRNAs in Caragana intermedia by high-throughput sequencing and expression analysis of 12 microRNAs and their targets under salt stress.

PubMed

Zhu, Jianfeng; Li, Wanfeng; Yang, Wenhua; Qi, Liwang; Han, Suying

2013-09-01

142 miRNAs were identified and 38 miRNA targets were predicted, 4 of which were validated, in C. intermedia . The expression of 12 miRNAs in salt-stressed leaves was assessed by qRT-PCR. MicroRNAs (miRNAs) are endogenous small RNAs that play important roles in various biological and metabolic processes in plants. Caragana intermedia is an important ecological and economic tree species prominent in the desert environment of west and northwest China. To date, no investigation into C. intermedia miRNAs has been reported. In this study, high-throughput sequencing of small RNAs and analysis of transcriptome data were performed to identify both conserved and novel miRNAs, and also their target mRNA genes in C. intermedia. Based on sequence similarity and hairpin structure prediction, 132 putative conserved miRNAs (12 of which were confirmed to form hairpin precursors) belonging to 31 known miRNA families were identified. Ten novel miRNAs (including the miRNA* sequences of three novel miRNAs) were also discovered. Furthermore, 36 potential target genes of 17 known miRNA families and 2 potential target genes of 1 novel miRNA were predicted; 4 of these were validated by 5' RACE. The expression of 12 miRNAs was validated in different tissues, and these and five target mRNAs were assessed by qRT-PCR after salt treatment. The expression levels of seven miRNAs (cin-miR157a, cin-miR159a, cin-miR165a, cin-miR167b, cin-miR172b, cin-miR390a and cin-miR396a) were upregulated, while cin-miR398a expression was downregulated after salt treatment. The targets of cin-miR157a, cin-miR165a, cin-miR172b and cin-miR396a were downregulated and showed an approximately negative correlation with their corresponding miRNAs under salt treatment. These results would help further understanding of miRNA regulation in response to abiotic stress in C. intermedia.

20180311 - Differential Gene Expression and Concentration-Response Modeling Workflow for High-Throughput Transcriptomic (HTTr) Data: Results From MCF7 Cells (SOT)

EPA Science Inventory

Increasing efficiency and declining cost of generating whole transcriptome profiles has made high-throughput transcriptomics a practical option for chemical bioactivity screening. The resulting data output provides information on the expression of thousands of genes and is amenab...

Differential Gene Expression and Concentration-Response Modeling Workflow for High-Throughput Transcriptomic (HTTr) Data: Results From MCF7 Cells

EPA Science Inventory

Increasing efficiency and declining cost of generating whole transcriptome profiles has made high-throughput transcriptomics a practical option for chemical bioactivity screening. The resulting data output provides information on the expression of thousands of genes and is amenab...

The genetic architecture of long QT syndrome: A critical reappraisal.

PubMed

Giudicessi, John R; Wilde, Arthur A M; Ackerman, Michael J

2018-03-30

Collectively, the completion of the Human Genome Project and subsequent development of high-throughput next-generation sequencing methodologies have revolutionized genomic research. However, the rapid sequencing and analysis of thousands upon thousands of human exomes and genomes has taught us that most genes, including those known to cause heritable cardiovascular disorders such as long QT syndrome, harbor an unexpected background rate of rare, and presumably innocuous, non-synonymous genetic variation. In this Review, we aim to reappraise the genetic architecture underlying both the acquired and congenital forms of long QT syndrome by examining how the clinical phenotype associated with and background genetic variation in long QT syndrome-susceptibility genes impacts the clinical validity of existing gene-disease associations and the variant classification and reporting strategies that serve as the foundation for diagnostic long QT syndrome genetic testing. Copyright © 2018 Elsevier Inc. All rights reserved.

A role for genetic susceptibility in sporadic focal segmental glomerulosclerosis

PubMed Central

Yu, Haiyang; Artomov, Mykyta; Brähler, Sebastian; Stander, M. Christine; Shamsan, Ghaidan; Sampson, Matthew G.; White, J. Michael; Kretzler, Matthias; Jain, Sanjay; Winkler, Cheryl A.; Mitra, Robi D.; Daly, Mark J.; Shaw, Andrey S.

2016-01-01

Focal segmental glomerulosclerosis (FSGS) is a syndrome that involves kidney podocyte dysfunction and causes chronic kidney disease. Multiple factors including chemical toxicity, inflammation, and infection underlie FSGS; however, highly penetrant disease genes have been identified in a small fraction of patients with a family history of FSGS. Variants of apolipoprotein L1 (APOL1) have been linked to FSGS in African Americans with HIV or hypertension, supporting the proposal that genetic factors enhance FSGS susceptibility. Here, we used sequencing to investigate whether genetics plays a role in the majority of FSGS cases that are identified as primary or sporadic FSGS and have no known cause. Given the limited number of biopsy-proven cases with ethnically matched controls, we devised an analytic strategy to identify and rank potential candidate genes and used an animal model for validation. Nine candidate FSGS susceptibility genes were identified in our patient cohort, and three were validated using a high-throughput mouse method that we developed. Specifically, we introduced a podocyte-specific, doxycycline-inducible transactivator into a murine embryonic stem cell line with an FSGS-susceptible genetic background that allows shRNA-mediated targeting of candidate genes in the adult kidney. Our analysis supports a broader role for genetic susceptibility of both sporadic and familial cases of FSGS and provides a tool to rapidly evaluate candidate FSGS-associated genes. PMID:26901816

High-Throughput Screening of a Luciferase Reporter of Gene Silencing on the Inactive X Chromosome.

PubMed

Keegan, Alissa; Plath, Kathrin; Damoiseaux, Robert

2018-01-01

Assays of luciferase gene activity are a sensitive and quantitative reporter system suited to high-throughput screening. We adapted a luciferase assay to a screening strategy for identifying factors that reactivate epigenetically silenced genes. This epigenetic luciferase reporter is subject to endogenous gene silencing mechanisms on the inactive X chromosome (Xi) in primary mouse cells and thus captures the multilayered nature of chromatin silencing in development. Here, we describe the optimization of an Xi-linked luciferase reactivation assay in 384-well format and adaptation of the assay for high-throughput siRNA and chemical screening. Xi-luciferase reactivation screening has applications in stem cell biology and cancer therapy. We have used the approach described here to identify chromatin-modifying proteins and to identify drug combinations that enhance the gene reactivation activity of the DNA demethylating drug 5-aza-2'-deoxycytidine.

New strategies in drug discovery.

PubMed

Ohlstein, Eliot H; Johnson, Anthony G; Elliott, John D; Romanic, Anne M

2006-01-01

Gene identification followed by determination of the expression of genes in a given disease and understanding of the function of the gene products is central to the drug discovery process. The ability to associate a specific gene with a disease can be attributed primarily to the extraordinary progress that has been made in the areas of gene sequencing and information technologies. Selection and validation of novel molecular targets have become of great importance in light of the abundance of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. Further, the successful translation of basic scientific discoveries into clinical experimental medicine and novel therapeutics is an increasing challenge. As such, a new paradigm for drug discovery has emerged. This process involves the integration of clinical, genetic, genomic, and molecular phenotype data partnered with cheminformatics. Central to this process, the data generated are managed, collated, and interpreted with the use of informatics. This review addresses the use of new technologies that have arisen to deal with this new paradigm.

Emory University: High-Throughput Protein-Protein Interaction Dataset for Lung Cancer-Associated Genes | Office of Cancer Genomics

Cancer.gov

To discover novel PPI signaling hubs for lung cancer, CTD2 Center at Emory utilized large-scale genomics datasets and literature to compile a set of lung cancer-associated genes. A library of expression vectors were generated for these genes and utilized for detecting pairwise PPIs with cell lysate-based TR-FRET assays in high-throughput screening format. Read the abstract.

A robust high-throughput fungal biosensor assay for the detection of estrogen activity.

PubMed

Zutz, Christoph; Wagener, Karen; Yankova, Desislava; Eder, Stefanie; Möstl, Erich; Drillich, Marc; Rychli, Kathrin; Wagner, Martin; Strauss, Joseph

2017-10-01

Estrogenic active compounds are present in a variety of sources and may alter biological functions in vertebrates. Therefore, it is crucial to develop innovative analytical systems that allow us to screen a broad spectrum of matrices and deliver fast and reliable results. We present the adaptation and validation of a fungal biosensor for the detection of estrogen activity in cow derived samples and tested the clinical applicability for pregnancy diagnosis in 140 mares and 120 cows. As biosensor we used a previously engineered genetically modified strain of the filamentous fungus Aspergillus nidulans, which contains the human estrogen receptor alpha and a reporter construct, in which β-galactosidase gene expression is controlled by an estrogen-responsive-element. The estrogen response of the fungal biosensor was validated with blood, urine, feces, milk and saliva. All matrices were screened for estrogenic activity prior to and after chemical extraction and the results were compared to an enzyme immunoassay (EIA). The biosensor showed consistent results in milk, urine and feces, which were comparable to those of the EIA. In contrast to the EIA, no sample pre-treatment by chemical extraction was needed. For 17β-estradiol, the biosensor showed a limit of detection of 1ng/L. The validation of the biosensor for pregnancy diagnosis revealed a specificity of 100% and a sensitivity of more than 97%. In conclusion, we developed and validated a highly robust fungal biosensor for detection of estrogen activity, which is highly sensitive and economic as it allows analyzing in high-throughput formats without the necessity for organic solvents. Copyright © 2017 Elsevier Inc. All rights reserved.

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.

PubMed

Sasagawa, Yohei; Danno, Hiroki; Takada, Hitomi; Ebisawa, Masashi; Tanaka, Kaori; Hayashi, Tetsutaro; Kurisaki, Akira; Nikaido, Itoshi

2018-03-09

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30-50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

Technical variables in high-throughput miRNA expression profiling: much work remains to be done.

PubMed

Nelson, Peter T; Wang, Wang-Xia; Wilfred, Bernard R; Tang, Guiliang

2008-11-01

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Evaluation of Sequencing Approaches for High-Throughput Transcriptomics - (BOSC)

EPA Science Inventory

Whole-genome in vitro transcriptomics has shown the capability to identify mechanisms of action and estimates of potency for chemical-mediated effects in a toxicological framework, but with limited throughput and high cost. The generation of high-throughput global gene expression...

Integrated automation for continuous high-throughput synthetic chromosome assembly and transformation to identify improved yeast strains for industrial production of peptide sweetener brazzein

USDA-ARS?s Scientific Manuscript database

Production and recycling of recombinant sweetener peptides in industrial biorefineries involves the evaluation of large numbers of genes and proteins. High-throughput integrated robotic molecular biology platforms that have the capacity to rapidly synthesize, clone, and express heterologous gene ope...

Novel Informatic Approaches to Analyze Gene Expression Data with the ToxCast 320 Chemical Library in Cultures of Primary Human Hepatocytes

EPA Science Inventory

Prevailing methodologies in the analysis of gene expression data often neglect to incorporate full concentration and time response due to limitations in throughput and sensitivity with traditional microarray approaches. We have developed a high throughput assay suite using primar...

Bibliometrics for Social Validation.

PubMed

Hicks, Daniel J

2016-01-01

This paper introduces a bibliometric, citation network-based method for assessing the social validation of novel research, and applies this method to the development of high-throughput toxicology research at the US Environmental Protection Agency. Social validation refers to the acceptance of novel research methods by a relevant scientific community; it is formally independent of the technical validation of methods, and is frequently studied in history, philosophy, and social studies of science using qualitative methods. The quantitative methods introduced here find that high-throughput toxicology methods are spread throughout a large and well-connected research community, which suggests high social validation. Further assessment of social validation involving mixed qualitative and quantitative methods are discussed in the conclusion.

Bibliometrics for Social Validation

PubMed Central

2016-01-01

This paper introduces a bibliometric, citation network-based method for assessing the social validation of novel research, and applies this method to the development of high-throughput toxicology research at the US Environmental Protection Agency. Social validation refers to the acceptance of novel research methods by a relevant scientific community; it is formally independent of the technical validation of methods, and is frequently studied in history, philosophy, and social studies of science using qualitative methods. The quantitative methods introduced here find that high-throughput toxicology methods are spread throughout a large and well-connected research community, which suggests high social validation. Further assessment of social validation involving mixed qualitative and quantitative methods are discussed in the conclusion. PMID:28005974

Overview Article: Identifying transcriptional cis-regulatory modules in animal genomes

PubMed Central

Suryamohan, Kushal; Halfon, Marc S.

2014-01-01

Gene expression is regulated through the activity of transcription factors and chromatin modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily-identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods has led to an explosion of both computational and empirical methods for CRM discovery in model and non-model organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against transcription factors or histone post-translational modifications, identification of nucleosome-depleted “open” chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted transcription factor binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. PMID:25704908

High-Throughput Sequencing of microRNAs in Glucocorticoid Sensitive Paediatric Inflammatory Bowel Disease Patients.

PubMed

De Iudicibus, Sara; Lucafò, Marianna; Vitulo, Nicola; Martelossi, Stefano; Zimbello, Rosanna; De Pascale, Fabio; Forcato, Claudio; Naviglio, Samuele; Di Silvestre, Alessia; Gerdol, Marco; Stocco, Gabriele; Valle, Giorgio; Ventura, Alessandro; Bramuzzo, Matteo; Decorti, Giuliana

2018-05-08

The aim of this research was the identification of novel pharmacogenomic biomarkers for better understanding the complex gene regulation mechanisms underpinning glucocorticoid (GC) action in paediatric inflammatory bowel disease (IBD). This goal was achieved by evaluating high-throughput microRNA (miRNA) profiles during GC treatment, integrated with the assessment of expression changes in GC receptor (GR) heterocomplex genes. Furthermore, we tested the hypothesis that differentially expressed miRNAs could be directly regulated by GCs through investigating the presence of GC responsive elements (GREs) in their gene promoters. Ten IBD paediatric patients responding to GCs were enrolled. Peripheral blood was obtained at diagnosis (T0) and after four weeks of steroid treatment (T4). MicroRNA profiles were analyzed using next generation sequencing, and selected significantly differentially expressed miRNAs were validated by quantitative reverse transcription-polymerase chain reaction. In detail, 18 miRNAs were differentially expressed from T0 to T4, 16 of which were upregulated and 2 of which were downregulated. Out of these, three miRNAs (miR-144, miR-142, and miR-96) could putatively recognize the 3’UTR of the GR gene and three miRNAs (miR-363, miR-96, miR-142) contained GREs sequences, thereby potentially enabling direct regulation by the GR. In conclusion, we identified miRNAs differently expressed during GC treatment and miRNAs which could be directly regulated by GCs in blood cells of young IBD patients. These results could represent a first step towards their translation as pharmacogenomic biomarkers.

Gene expression and splicing alterations analyzed by high throughput RNA sequencing of chronic lymphocytic leukemia specimens.

PubMed

Liao, Wei; Jordaan, Gwen; Nham, Phillipp; Phan, Ryan T; Pelegrini, Matteo; Sharma, Sanjai

2015-10-16

To determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed. Ten CLL specimens and five normal peripheral blood CD19+ B cells were analyzed by HTS RNA-seq. The library preparation was performed with Illumina TrueSeq RNA kit and analyzed by Illumina HiSeq 2000 sequencing system. An average of 48.5 million reads for B cells, and 50.6 million reads for CLL specimens were obtained with 10396 and 10448 assembled transcripts for normal B cells and primary CLL specimens respectively. With the Cuffdiff analysis, 2091 differentially expressed genes (DEG) between B cells and CLL specimens based on FPKM (fragments per kilobase of transcript per million reads and false discovery rate, FDR q < 0.05, fold change >2) were identified. Expression of selected DEGs (n = 32) with up regulated and down regulated expression in CLL from RNA-seq data were also analyzed by qRT-PCR in a test cohort of CLL specimens. Even though there was a variation in fold expression of DEG genes between RNA-seq and qRT-PCR; more than 90 % of analyzed genes were validated by qRT-PCR analysis. Analysis of RNA-seq data for splicing alterations in CLL and B cells was performed by Multivariate Analysis of Transcript Splicing (MATS analysis). Skipped exon was the most frequent splicing alteration in CLL specimens with 128 significant events (P-value <0.05, minimum inclusion level difference >0.1). The RNA-seq analysis of CLL specimens identifies novel DEG and alternatively spliced genes that are potential prognostic markers and therapeutic targets. High level of validation by qRT-PCR for a number of DEG genes supports the accuracy of this analysis. Global comparison of transcriptomes of B cells, IGVH non-mutated CLL (U-CLL) and mutated CLL specimens (M-CLL) with multidimensional scaling analysis was able to segregate CLL and B cell transcriptomes but the M-CLL and U-CLL transcriptomes were indistinguishable. The analysis of HTS RNA-seq data to identify alternative splicing events and other genetic abnormalities specific to CLL is an added advantage of RNA-seq that is not feasible with other genome wide analysis.

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.

QuASAR-MPRA: accurate allele-specific analysis for massively parallel reporter assays.

PubMed

Kalita, Cynthia A; Moyerbrailean, Gregory A; Brown, Christopher; Wen, Xiaoquan; Luca, Francesca; Pique-Regi, Roger

2018-03-01

The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays. http://github.com/piquelab/QuASAR/tree/master/mpra. fluca@wayne.edu or rpique@wayne.edu. Supplementary data are available online at Bioinformatics. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Identification of widespread adenosine nucleotide binding in Mycobacterium tuberculosis

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

Ansong, Charles; Ortega, Corrie; Payne, Samuel H.

The annotation of protein function is almost completely performed by in silico approaches. However, computational prediction of protein function is frequently incomplete and error prone. In Mycobacterium tuberculosis (Mtb), ~25% of all genes have no predicted function and are annotated as hypothetical proteins. This lack of functional information severely limits our understanding of Mtb pathogenicity. Current tools for experimental functional annotation are limited and often do not scale to entire protein families. Here, we report a generally applicable chemical biology platform to functionally annotate bacterial proteins by combining activity-based protein profiling (ABPP) and quantitative LC-MS-based proteomics. As an example ofmore » this approach for high-throughput protein functional validation and discovery, we experimentally annotate the families of ATP-binding proteins in Mtb. Our data experimentally validate prior in silico predictions of >250 ATPases and adenosine nucleotide-binding proteins, and reveal 73 hypothetical proteins as novel ATP-binding proteins. We identify adenosine cofactor interactions with many hypothetical proteins containing a diversity of unrelated sequences, providing a new and expanded view of adenosine nucleotide binding in Mtb. Furthermore, many of these hypothetical proteins are both unique to Mycobacteria and essential for infection, suggesting specialized functions in mycobacterial physiology and pathogenicity. Thus, we provide a generally applicable approach for high throughput protein function discovery and validation, and highlight several ways in which application of activity-based proteomics data can improve the quality of functional annotations to facilitate novel biological insights.« less

On the Achievable Throughput Over TVWS Sensor Networks

PubMed Central

Caleffi, Marcello; Cacciapuoti, Angela Sara

2016-01-01

In this letter, we study the throughput achievable by an unlicensed sensor network operating over TV white space spectrum in presence of coexistence interference. Through the letter, we first analytically derive the achievable throughput as a function of the channel ordering. Then, we show that the problem of deriving the maximum expected throughput through exhaustive search is computationally unfeasible. Finally, we derive a computational-efficient algorithm characterized by polynomial-time complexity to compute the channel set maximizing the expected throughput and, stemming from this, we derive a closed-form expression of the maximum expected throughput. Numerical simulations validate the theoretical analysis. PMID:27043565

DOSE RESPONSE FROM HIGH THROUGHPUT GENE EXPRESSION STUDIES AND THE INFLUENCE OF TIME AND CELL LINE ON INFERRED MODE OF ACTION BY ONTOLOGIC ENRICHMENT (SOT)

EPA Science Inventory

Gene expression with ontologic enrichment and connectivity mapping tools is widely used to infer modes of action (MOA) for therapeutic drugs. Despite progress in high-throughput (HT) genomic systems, strategies suitable to identify industrial chemical MOA are needed. The L1000 is...

Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

EPA Science Inventory

Microarray profiling of chemical-induced effects is being increasingly used in medium and high-throughput formats. In this study, we describe computational methods to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), ...

Quantitative High-Throughput Identification of Drugs as Modulators of Human Constitutive Androstane Receptor

PubMed Central

Lynch, Caitlin; Zhao, Jinghua; Huang, Ruili; Xiao, Jingwei; Li, Linhao; Heyward, Scott; Xia, Menghang; Wang, Hongbing

2015-01-01

The constitutive androstane receptor (CAR, NR1I3) plays a key role in governing the transcription of numerous hepatic genes that involve xenobiotic metabolism/clearance, energy homeostasis, and cell proliferation. Thus, identification of novel human CAR (hCAR) modulators may not only enhance early prediction of drug-drug interactions but also offer potentially novel therapeutics for diseases such as metabolic disorders and cancer. In this study, we have generated a double stable cell line expressing both hCAR and a CYP2B6-driven luciferase reporter for quantitative high-throughput screening (qHTS) of hCAR modulators. Approximately 2800 compounds from the NIH Chemical Genomics Center Pharmaceutical Collection were screened employing both the activation and deactivation modes of the qHTS. Activators (115) and deactivators (152) of hCAR were identified from the primary qHTS, among which 10 agonists and 10 antagonists were further validated in the physiologically relevant human primary hepatocytes for compound-mediated hCAR nuclear translocation and target gene expression. Collectively, our results reveal that hCAR modulators can be efficiently identified through this newly established qHTS assay. Profiling drug collections for hCAR activity would facilitate the prediction of metabolism-based drug-drug interactions, and may lead to the identification of potential novel therapeutics. PMID:25993555

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

PubMed

Hutz, Janna E; Nelson, Thomas; Wu, Hua; McAllister, Gregory; Moutsatsos, Ioannis; Jaeger, Savina A; Bandyopadhyay, Somnath; Nigsch, Florian; Cornett, Ben; Jenkins, Jeremy L; Selinger, Douglas W

2013-04-01

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

PubMed Central

Bii, Victor M.; Trobridge, Grant D.

2016-01-01

Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types. PMID:27792127

Structure-guided design of fluorescent S-adenosylmethionine analogs for a high-throughput screen to target SAM-I riboswitch RNAs.

PubMed

Hickey, Scott F; Hammond, Ming C

2014-03-20

Many classes of S-adenosylmethionine (SAM)-binding RNAs and proteins are of interest as potential drug targets in diverse therapeutic areas, from infectious diseases to cancer. In the former case, the SAM-I riboswitch is an attractive target because this structured RNA element is found only in bacterial mRNAs and regulates multiple genes in several human pathogens. Here, we describe the synthesis of stable and fluorescent analogs of SAM in which the fluorophore is introduced through a functionalizable linker to the ribose. A Cy5-labeled SAM analog was shown to bind several SAM-I riboswitches via in-line probing and fluorescence polarization assays, including one from Staphylococcus aureus that controls the expression of SAM synthetase in this organism. A fluorescent ligand displacement assay was developed and validated for high-throughput screening of compounds to target the SAM-I riboswitch class. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of microRNAs and their targets in Finger millet by high throughput sequencing.

PubMed

Usha, S; Jyothi, M N; Sharadamma, N; Dixit, Rekha; Devaraj, V R; Nagesh Babu, R

2015-12-15

MicroRNAs are short non-coding RNAs which play an important role in regulating gene expression by mRNA cleavage or by translational repression. The majority of identified miRNAs were evolutionarily conserved; however, others expressed in a species-specific manner. Finger millet is an important cereal crop; nonetheless, no practical information is available on microRNAs to date. In this study, we have identified 95 conserved microRNAs belonging to 39 families and 3 novel microRNAs by high throughput sequencing. For the identified conserved and novel miRNAs a total of 507 targets were predicted. 11 miRNAs were validated and tissue specificity was determined by stem loop RT-qPCR, Northern blot. GO analyses revealed targets of miRNA were involved in wide range of regulatory functions. This study implies large number of known and novel miRNAs found in Finger millet which may play important role in growth and development. Copyright © 2015 Elsevier B.V. All rights reserved.

Cancer biomarker discovery: the entropic hallmark.

PubMed

Berretta, Regina; Moscato, Pablo

2010-08-18

It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.

High-throughput screening for modulators of ACVR1 transcription: discovery of potential therapeutics for fibrodysplasia ossificans progressiva

PubMed Central

Cappato, Serena; Tonachini, Laura; Giacopelli, Francesca; Tirone, Mario; Galietta, Luis J. V.; Sormani, Martina; Giovenzana, Anna; Spinelli, Antonello E.; Canciani, Barbara; Brunelli, Silvia; Ravazzolo, Roberto

2016-01-01

ABSTRACT The ACVR1 gene encodes a type I receptor of bone morphogenetic proteins (BMPs). Activating mutations in ACVR1 are responsible for fibrodysplasia ossificans progressiva (FOP), a rare disease characterized by congenital toe malformation and progressive heterotopic endochondral ossification leading to severe and cumulative disability. Until now, no therapy has been available to prevent soft-tissue swelling (flare-ups) that trigger the ossification process. With the aim of finding a new therapeutic strategy for FOP, we developed a high-throughput screening (HTS) assay to identify inhibitors of ACVR1 gene expression among drugs already approved for the therapy of other diseases. The screening, based on an ACVR1 promoter assay, was followed by an in vitro and in vivo test to validate and characterize candidate molecules. Among compounds that modulate the ACVR1 promoter activity, we selected the one showing the highest inhibitory effect, dipyridamole, a drug that is currently used as a platelet anti-aggregant. The inhibitory effect was detectable on ACVR1 gene expression, on the whole Smad-dependent BMP signaling pathway, and on chondrogenic and osteogenic differentiation processes by in vitro cellular assays. Moreover, dipyridamole reduced the process of heterotopic bone formation in vivo. Our drug repositioning strategy has led to the identification of dipyridamole as a possible therapeutic tool for the treatment of FOP. Furthermore, our study has also defined a pipeline of assays that will be useful for the evaluation of other pharmacological inhibitors of heterotopic ossification. PMID:27125279

High-Throughput Sequencing and Copy Number Variation Detection Using Formalin Fixed Embedded Tissue in Metastatic Gastric Cancer

PubMed Central

Hong, Min Eui; Do, In-Gu; Kang, So Young; Ha, Sang Yun; Kim, Seung Tae; Park, Se Hoon; Kang, Won Ki; Choi, Min-Gew; Lee, Jun Ho; Sohn, Tae Sung; Bae, Jae Moon; Kim, Sung; Kim, Duk-Hwan; Kim, Kyoung-Mee

2014-01-01

In the era of targeted therapy, mutation profiling of cancer is a crucial aspect of making therapeutic decisions. To characterize cancer at a molecular level, the use of formalin-fixed paraffin-embedded tissue is important. We tested the Ion AmpliSeq Cancer Hotspot Panel v2 and nCounter Copy Number Variation Assay in 89 formalin-fixed paraffin-embedded gastric cancer samples to determine whether they are applicable in archival clinical samples for personalized targeted therapies. We validated the results with Sanger sequencing, real-time quantitative PCR, fluorescence in situ hybridization and immunohistochemistry. Frequently detected somatic mutations included TP53 (28.17%), APC (10.1%), PIK3CA (5.6%), KRAS (4.5%), SMO (3.4%), STK11 (3.4%), CDKN2A (3.4%) and SMAD4 (3.4%). Amplifications of HER2, CCNE1, MYC, KRAS and EGFR genes were observed in 8 (8.9%), 4 (4.5%), 2 (2.2%), 1 (1.1%) and 1 (1.1%) cases, respectively. In the cases with amplification, fluorescence in situ hybridization for HER2 verified gene amplification and immunohistochemistry for HER2, EGFR and CCNE1 verified the overexpression of proteins in tumor cells. In conclusion, we successfully performed semiconductor-based sequencing and nCounter copy number variation analyses in formalin-fixed paraffin-embedded gastric cancer samples. High-throughput screening in archival clinical samples enables faster, more accurate and cost-effective detection of hotspot mutations or amplification in genes. PMID:25372287

Re-engineering adenovirus vector systems to enable high-throughput analyses of gene function.

PubMed

Stanton, Richard J; McSharry, Brian P; Armstrong, Melanie; Tomasec, Peter; Wilkinson, Gavin W G

2008-12-01

With the enhanced capacity of bioinformatics to interrogate extensive banks of sequence data, more efficient technologies are needed to test gene function predictions. Replication-deficient recombinant adenovirus (Ad) vectors are widely used in expression analysis since they provide for extremely efficient expression of transgenes in a wide range of cell types. To facilitate rapid, high-throughput generation of recombinant viruses, we have re-engineered an adenovirus vector (designated AdZ) to allow single-step, directional gene insertion using recombineering technology. Recombineering allows for direct insertion into the Ad vector of PCR products, synthesized sequences, or oligonucleotides encoding shRNAs without requirement for a transfer vector Vectors were optimized for high-throughput applications by making them "self-excising" through incorporating the I-SceI homing endonuclease into the vector removing the need to linearize vectors prior to transfection into packaging cells. AdZ vectors allow genes to be expressed in their native form or with strep, V5, or GFP tags. Insertion of tetracycline operators downstream of the human cytomegalovirus major immediate early (HCMV MIE) promoter permits silencing of transgenes in helper cells expressing the tet repressor thus making the vector compatible with the cloning of toxic gene products. The AdZ vector system is robust, straightforward, and suited to both sporadic and high-throughput applications.

Potential Novel Antibiotics from HTS Targeting the Virulence-regulating Transcription Factor, VirF, from Shigella flexneri

PubMed Central

Emanuele, Anthony A.; Adams, Nancy E.; Chen, Yi-Chen; Maurelli, Anthony T.; Garcia, George A.

2014-01-01

VirF is an AraC-type transcriptional regulator responsible for activating the transcription of virulence genes required for the intracellular invasion and cell-to-cell spread of Shigella flexneri. Gene disruption studies have validated VirF as a potential target for an anti-virulence therapy to treat shigellosis by determining that VirF is necessary for virulence, but not required for bacterial viability. Using a bacteria-based, β-galactosidase reporter assay we completed a high-throughput screening (HTS) campaign monitoring VirF activity in the presence of over 140,000 small molecules. From our screening campaign we identified five lead compounds to pursue in tissue-culture-based invasion and cell-to-cell spread assays and toxicity screens. Our observations of activity in these models for infection have validated our approach of targeting virulence regulation and have allowed us to identify a promising chemical scaffold from our HTS for hit-to-lead development. Interestingly, differential effects on invasion versus cell-to-cell spread suggest that the compounds’ efficacies may depend, in part, on the specific promoter that VirF is recognizing. PMID:24549153

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

PubMed

Vallat, Laurent; Kemper, Corey A; Jung, Nicolas; Maumy-Bertrand, Myriam; Bertrand, Frédéric; Meyer, Nicolas; Pocheville, Arnaud; Fisher, John W; Gribben, John G; Bahram, Seiamak

2013-01-08

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.

Discovering semantic features in the literature: a foundation for building functional associations

PubMed Central

Chagoyen, Monica; Carmona-Saez, Pedro; Shatkay, Hagit; Carazo, Jose M; Pascual-Montano, Alberto

2006-01-01

Background Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. Results We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. Conclusion The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data. PMID:16438716

NCBI GEO: archive for high-throughput functional genomic data.

PubMed

Barrett, Tanya; Troup, Dennis B; Wilhite, Stephen E; Ledoux, Pierre; Rudnev, Dmitry; Evangelista, Carlos; Kim, Irene F; Soboleva, Alexandra; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Muertter, Rolf N; Edgar, Ron

2009-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest public repository for high-throughput gene expression data. Additionally, GEO hosts other categories of high-throughput functional genomic data, including those that examine genome copy number variations, chromatin structure, methylation status and transcription factor binding. These data are generated by the research community using high-throughput technologies like microarrays and, more recently, next-generation sequencing. The database has a flexible infrastructure that can capture fully annotated raw and processed data, enabling compliance with major community-derived scientific reporting standards such as 'Minimum Information About a Microarray Experiment' (MIAME). In addition to serving as a centralized data storage hub, GEO offers many tools and features that allow users to effectively explore, analyze and download expression data from both gene-centric and experiment-centric perspectives. This article summarizes the GEO repository structure, content and operating procedures, as well as recently introduced data mining features. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

Performance comparison of SNP detection tools with illumina exome sequencing data—an assessment using both family pedigree information and sample-matched SNP array data

PubMed Central

Yi, Ming; Zhao, Yongmei; Jia, Li; He, Mei; Kebebew, Electron; Stephens, Robert M.

2014-01-01

To apply exome-seq-derived variants in the clinical setting, there is an urgent need to identify the best variant caller(s) from a large collection of available options. We have used an Illumina exome-seq dataset as a benchmark, with two validation scenarios—family pedigree information and SNP array data for the same samples, permitting global high-throughput cross-validation, to evaluate the quality of SNP calls derived from several popular variant discovery tools from both the open-source and commercial communities using a set of designated quality metrics. To the best of our knowledge, this is the first large-scale performance comparison of exome-seq variant discovery tools using high-throughput validation with both Mendelian inheritance checking and SNP array data, which allows us to gain insights into the accuracy of SNP calling through such high-throughput validation in an unprecedented way, whereas the previously reported comparison studies have only assessed concordance of these tools without directly assessing the quality of the derived SNPs. More importantly, the main purpose of our study was to establish a reusable procedure that applies high-throughput validation to compare the quality of SNP discovery tools with a focus on exome-seq, which can be used to compare any forthcoming tool(s) of interest. PMID:24831545

High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing.

PubMed

Lagarde, Julien; Uszczynska-Ratajczak, Barbara; Carbonell, Silvia; Pérez-Lluch, Sílvia; Abad, Amaya; Davis, Carrie; Gingeras, Thomas R; Frankish, Adam; Harrow, Jennifer; Guigo, Roderic; Johnson, Rory

2017-12-01

Accurate annotation of genes and their transcripts is a foundation of genomics, but currently no annotation technique combines throughput and accuracy. As a result, reference gene collections remain incomplete-many gene models are fragmentary, and thousands more remain uncataloged, particularly for long noncoding RNAs (lncRNAs). To accelerate lncRNA annotation, the GENCODE consortium has developed RNA Capture Long Seq (CLS), which combines targeted RNA capture with third-generation long-read sequencing. Here we present an experimental reannotation of the GENCODE intergenic lncRNA populations in matched human and mouse tissues that resulted in novel transcript models for 3,574 and 561 gene loci, respectively. CLS approximately doubled the annotated complexity of targeted loci, outperforming existing short-read techniques. Full-length transcript models produced by CLS enabled us to definitively characterize the genomic features of lncRNAs, including promoter and gene structure, and protein-coding potential. Thus, CLS removes a long-standing bottleneck in transcriptome annotation and generates manual-quality full-length transcript models at high-throughput scales.

Transcriptome-wide single nucleotide polymorphisms (SNPs) for abalone (Haliotis midae): validation and application using GoldenGate medium-throughput genotyping assays.

PubMed

Bester-Van Der Merwe, Aletta; Blaauw, Sonja; Du Plessis, Jana; Roodt-Wilding, Rouvay

2013-09-23

Haliotis midae is one of the most valuable commercial abalone species in the world, but is highly vulnerable, due to exploitation, habitat destruction and predation. In order to preserve wild and cultured stocks, genetic management and improvement of the species has become crucial. Fundamental to this is the availability and employment of molecular markers, such as microsatellites and single nucleotide (SNPs). Transcriptome sequences generated through sequencing-by-synthesis technology were utilized for the in vitro and in silico identification of 505 putative SNPs from a total of 316 selected contigs. A subset of 234 SNPs were further validated and characterized in wild and cultured abalone using two Illumina GoldenGate genotyping assays. Combined with VeraCode technology, this genotyping platform yielded a 65%-69% conversion rate (percentage polymorphic markers) with a global genotyping success rate of 76%-85% and provided a viable means for validating SNP markers in a non-model species. The utility of 31 of the validated SNPs in population structure analysis was confirmed, while a large number of SNPs (174) were shown to be informative and are, thus, good candidates for linkage map construction. The non-synonymous SNPs (50) located in coding regions of genes that showed similarities with known proteins will also be useful for genetic applications, such as the marker-assisted selection of genes of relevance to abalone aquaculture.

Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes.

PubMed

Olson, Nathan D; Treangen, Todd J; Hill, Christopher M; Cepeda-Espinoza, Victoria; Ghurye, Jay; Koren, Sergey; Pop, Mihai

2017-08-07

Metagenomic samples are snapshots of complex ecosystems at work. They comprise hundreds of known and unknown species, contain multiple strain variants and vary greatly within and across environments. Many microbes found in microbial communities are not easily grown in culture making their DNA sequence our only clue into their evolutionary history and biological function. Metagenomic assembly is a computational process aimed at reconstructing genes and genomes from metagenomic mixtures. Current methods have made significant strides in reconstructing DNA segments comprising operons, tandem gene arrays and syntenic blocks. Shorter, higher-throughput sequencing technologies have become the de facto standard in the field. Sequencers are now able to generate billions of short reads in only a few days. Multiple metagenomic assembly strategies, pipelines and assemblers have appeared in recent years. Owing to the inherent complexity of metagenome assembly, regardless of the assembly algorithm and sequencing method, metagenome assemblies contain errors. Recent developments in assembly validation tools have played a pivotal role in improving metagenomics assemblers. Here, we survey recent progress in the field of metagenomic assembly, provide an overview of key approaches for genomic and metagenomic assembly validation and demonstrate the insights that can be derived from assemblies through the use of assembly validation strategies. We also discuss the potential for impact of long-read technologies in metagenomics. We conclude with a discussion of future challenges and opportunities in the field of metagenomic assembly and validation. © The Author 2017. Published by Oxford University Press.

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy.

PubMed

Allan, Kristina J; Mahoney, Douglas J; Baird, Stephen D; Lefebvre, Charles A; Stojdl, David F

2018-04-03

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

Automation of fluorescent differential display with digital readout.

PubMed

Meade, Jonathan D; Cho, Yong-Jig; Fisher, Jeffrey S; Walden, Jamie C; Guo, Zhen; Liang, Peng

2006-01-01

Since its invention in 1992, differential display (DD) has become the most commonly used technique for identifying differentially expressed genes because of its many advantages over competing technologies such as DNA microarray, serial analysis of gene expression (SAGE), and subtractive hybridization. Despite the great impact of the method on biomedical research, there has been a lack of automation of DD technology to increase its throughput and accuracy for systematic gene expression analysis. Most of previous DD work has taken a "shot-gun" approach of identifying one gene at a time, with a limited number of polymerase chain reaction (PCR) reactions set up manually, giving DD a low-tech and low-throughput image. We have optimized the DD process with a new platform that incorporates fluorescent digital readout, automated liquid handling, and large-format gels capable of running entire 96-well plates. The resulting streamlined fluorescent DD (FDD) technology offers an unprecedented accuracy, sensitivity, and throughput in comprehensive and quantitative analysis of gene expression. These major improvements will allow researchers to find differentially expressed genes of interest, both known and novel, quickly and easily.

Development of a high-throughput screening system for identification of novel reagents regulating DNA damage in human dermal fibroblasts.

PubMed

Bae, Seunghee; An, In-Sook; An, Sungkwan

2015-09-01

Ultraviolet (UV) radiation is a major inducer of skin aging and accumulated exposure to UV radiation increases DNA damage in skin cells, including dermal fibroblasts. In the present study, we developed a novel DNA repair regulating material discovery (DREAM) system for the high-throughput screening and identification of putative materials regulating DNA repair in skin cells. First, we established a modified lentivirus expressing the luciferase and hypoxanthine phosphoribosyl transferase (HPRT) genes. Then, human dermal fibroblast WS-1 cells were infected with the modified lentivirus and selected with puromycin to establish cells that stably expressed luciferase and HPRT (DREAM-F cells). The first step in the DREAM protocol was a 96-well-based screening procedure, involving the analysis of cell viability and luciferase activity after pretreatment of DREAM-F cells with reagents of interest and post-treatment with UVB radiation, and vice versa. In the second step, we validated certain effective reagents identified in the first step by analyzing the cell cycle, evaluating cell death, and performing HPRT-DNA sequencing in DREAM-F cells treated with these reagents and UVB. This DREAM system is scalable and forms a time-saving high-throughput screening system for identifying novel anti-photoaging reagents regulating DNA damage in dermal fibroblasts.

High Resolution Melting (HRM) for High-Throughput Genotyping-Limitations and Caveats in Practical Case Studies.

PubMed

Słomka, Marcin; Sobalska-Kwapis, Marta; Wachulec, Monika; Bartosz, Grzegorz; Strapagiel, Dominik

2017-11-03

High resolution melting (HRM) is a convenient method for gene scanning as well as genotyping of individual and multiple single nucleotide polymorphisms (SNPs). This rapid, simple, closed-tube, homogenous, and cost-efficient approach has the capacity for high specificity and sensitivity, while allowing easy transition to high-throughput scale. In this paper, we provide examples from our laboratory practice of some problematic issues which can affect the performance and data analysis of HRM results, especially with regard to reference curve-based targeted genotyping. We present those examples in order of the typical experimental workflow, and discuss the crucial significance of the respective experimental errors and limitations for the quality and analysis of results. The experimental details which have a decisive impact on correct execution of a HRM genotyping experiment include type and quality of DNA source material, reproducibility of isolation method and template DNA preparation, primer and amplicon design, automation-derived preparation and pipetting inconsistencies, as well as physical limitations in melting curve distinction for alternative variants and careful selection of samples for validation by sequencing. We provide a case-by-case analysis and discussion of actual problems we encountered and solutions that should be taken into account by researchers newly attempting HRM genotyping, especially in a high-throughput setup.

High Resolution Melting (HRM) for High-Throughput Genotyping—Limitations and Caveats in Practical Case Studies

PubMed Central

Słomka, Marcin; Sobalska-Kwapis, Marta; Wachulec, Monika; Bartosz, Grzegorz

2017-01-01

High resolution melting (HRM) is a convenient method for gene scanning as well as genotyping of individual and multiple single nucleotide polymorphisms (SNPs). This rapid, simple, closed-tube, homogenous, and cost-efficient approach has the capacity for high specificity and sensitivity, while allowing easy transition to high-throughput scale. In this paper, we provide examples from our laboratory practice of some problematic issues which can affect the performance and data analysis of HRM results, especially with regard to reference curve-based targeted genotyping. We present those examples in order of the typical experimental workflow, and discuss the crucial significance of the respective experimental errors and limitations for the quality and analysis of results. The experimental details which have a decisive impact on correct execution of a HRM genotyping experiment include type and quality of DNA source material, reproducibility of isolation method and template DNA preparation, primer and amplicon design, automation-derived preparation and pipetting inconsistencies, as well as physical limitations in melting curve distinction for alternative variants and careful selection of samples for validation by sequencing. We provide a case-by-case analysis and discussion of actual problems we encountered and solutions that should be taken into account by researchers newly attempting HRM genotyping, especially in a high-throughput setup. PMID:29099791

Identification of Development-Related Genes in the Ovaries of Adult Harmonia axyridis (Pallas) Lady Beetles Using a Time- Series Analysis by RNA-seq.

PubMed

Du, Wenxiao; Zeng, Fanrong

2016-12-14

Adults of the lady beetle species Harmonia axyridis (Pallas) are bred artificially en masse for classic biological control, which requires egg-laying by the H. axyridis ovary. Development-related genes may impact the growth of the H. axyridis adult ovary but have not been reported. Here, we used integrative time-series RNA-seq analysis of the ovary in H. axyridis adults to detect development-related genes. A total of 28,558 unigenes were functionally annotated using seven types of databases to obtain an annotated unigene database for ovaries in H. axyridis adults. We also analysed differentially expressed genes (DEGs) between samples. Based on a combination of the results of this bioinformatics analysis with literature reports and gene expression level changes in four different stages, we focused on the development of oocyte reproductive stem cell and yolk formation process and identified 26 genes with high similarity to development-related genes. 20 DEGs were randomly chosen for quantitative real-time PCR (qRT-PCR) to validate the accuracy of the RNA-seq results. This study establishes a robust pipeline for the discovery of key genes using high-throughput sequencing and the identification of a class of development-related genes for characterization.

An integrative computational approach for prioritization of genomic variants

DOE PAGES

Dubchak, Inna; Balasubramanian, Sandhya; Wang, Sheng; ...

2014-12-15

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidatemore » genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. This study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.« less

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

PubMed

Rienksma, Rienk A; Suarez-Diez, Maria; Spina, Lucie; Schaap, Peter J; Martins dos Santos, Vitor A P

2014-12-01

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Molecular subtyping of European swine influenza viruses and scaling to high-throughput analysis.

PubMed

Bonin, Emilie; Quéguiner, Stéphane; Woudstra, Cédric; Gorin, Stéphane; Barbier, Nicolas; Harder, Timm C; Fach, Patrick; Hervé, Séverine; Simon, Gaëlle

2018-01-10

Swine influenza is a respiratory infection of pigs that may have a significant economic impact in affected herds and pose a threat to the human population since swine influenza A viruses (swIAVs) are zoonotic pathogens. Due to the increasing genetic diversity of swIAVs and because novel reassortants or variants may become enzootic or have zoonotic implications, surveillance is strongly encouraged. Therefore, diagnostic tests and advanced technologies able to identify the circulating strains rapidly are critically important. Several reverse transcription real-time PCR assays (RT-qPCRs) were developed to subtype European swIAVs in clinical samples previously identified as containing IAV genome. The RT-qPCRs aimed to discriminate HA genes of four H1 genetic lineages (H1 av , H1 hu , H1 huΔ146-147 , H1pdm) and one H3 lineage, and NA genes of two N1 lineages (N1, N1pdm) and one N2 lineage. After individual validation, each RT-qPCR was adapted to high-throughput analyses in parallel to the amplification of the IAV M gene (target for IAV detection) and the β-actin gene (as an internal control), in order to test the ten target genes simultaneously on a large number of clinical samples, using low volumes of reagents and RNA extracts. The RT-qPCRs dedicated to IAV molecular subtyping enabled the identification of swIAVs from the four viral subtypes that are known to be enzootic in European pigs, i.e. H1 av N1, H1 hu N2, H3N2 and H1N1pdm. They also made it possible to discriminate a new antigenic variant (H1 hu N2 Δ146-147 ) among H1 hu N2 viruses, as well as reassortant viruses, such as H1 hu N1 or H1 av N2 for example, and virus mixtures. These PCR techniques exhibited a gain in sensitivity as compared to end-point RT-PCRs, enabling the characterization of biological samples with low genetic loads, with considerable time saving. Adaptation to high-throughput analyses appeared effective, both in terms of specificity and sensitivity. This new development opens novel perspectives in diagnostic capacities that could be very useful for swIAV surveillance and large-scale epidemiological studies.

A high-throughput virus-induced gene silencing protocol identifies genes involved in multi-stress tolerance

PubMed Central

2013-01-01

Background Understanding the function of a particular gene under various stresses is important for engineering plants for broad-spectrum stress tolerance. Although virus-induced gene silencing (VIGS) has been used to characterize genes involved in abiotic stress tolerance, currently available gene silencing and stress imposition methodology at the whole plant level is not suitable for high-throughput functional analyses of genes. This demands a robust and reliable methodology for characterizing genes involved in abiotic and multi-stress tolerance. Results Our methodology employs VIGS-based gene silencing in leaf disks combined with simple stress imposition and effect quantification methodologies for easy and faster characterization of genes involved in abiotic and multi-stress tolerance. By subjecting leaf disks from gene-silenced plants to various abiotic stresses and inoculating silenced plants with various pathogens, we show the involvement of several genes for multi-stress tolerance. In addition, we demonstrate that VIGS can be used to characterize genes involved in thermotolerance. Our results also showed the functional relevance of NtEDS1 in abiotic stress, NbRBX1 and NbCTR1 in oxidative stress; NtRAR1 and NtNPR1 in salinity stress; NbSOS1 and NbHSP101 in biotic stress; and NtEDS1, NbETR1, NbWRKY2 and NbMYC2 in thermotolerance. Conclusions In addition to widening the application of VIGS, we developed a robust, easy and high-throughput methodology for functional characterization of genes involved in multi-stress tolerance. PMID:24289810

Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

PubMed

Zeidan, Amer M; Prebet, Thomas; Saad Aldin, Ehab; Gore, Steven David

2014-04-01

Evaluation of: Pellagatti A, Benner A, Mills KI et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J. Clin. Oncol. 31(28), 3557-3564 (2013). Patients with myelodysplastic syndromes (MDS) exhibit wide heterogeneity in clinical outcomes making accurate risk-stratification an integral part of the risk-adaptive management paradigm. Current prognostic schemes for MDS rely on clinicopathological parameters. Despite the increasing knowledge of the genetic landscape of MDS and the prognostic impact of many newly discovered molecular aberrations, none to date has been incorporated formally into the major risk models. Efforts are ongoing to use data generated from genome-wide high-throughput techniques to improve the 'individualized' outcome prediction for patients. We here discuss an important paper in which gene expression profiling (GEP) technology was applied to marrow CD34(+) cells from 125 MDS patients to generate and validate a standardized GEP-based prognostic signature.

Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.

PubMed

Payne, Philip R O; Kwok, Alan; Dhaval, Rakesh; Borlawsky, Tara B

2009-03-01

The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

Evaluating High-Throughput Ab Initio Gene Finders to Discover Proteins Encoded in Eukaryotic Pathogen Genomes Missed by Laboratory Techniques

PubMed Central

Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.

2012-01-01

Next generation sequencing technology is advancing genome sequencing at an unprecedented level. By unravelling the code within a pathogen’s genome, every possible protein (prior to post-translational modifications) can theoretically be discovered, irrespective of life cycle stages and environmental stimuli. Now more than ever there is a great need for high-throughput ab initio gene finding. Ab initio gene finders use statistical models to predict genes and their exon-intron structures from the genome sequence alone. This paper evaluates whether existing ab initio gene finders can effectively predict genes to deduce proteins that have presently missed capture by laboratory techniques. An aim here is to identify possible patterns of prediction inaccuracies for gene finders as a whole irrespective of the target pathogen. All currently available ab initio gene finders are considered in the evaluation but only four fulfil high-throughput capability: AUGUSTUS, GeneMark_hmm, GlimmerHMM, and SNAP. These gene finders require training data specific to a target pathogen and consequently the evaluation results are inextricably linked to the availability and quality of the data. The pathogen, Toxoplasma gondii, is used to illustrate the evaluation methods. The results support current opinion that predicted exons by ab initio gene finders are inaccurate in the absence of experimental evidence. However, the results reveal some patterns of inaccuracy that are common to all gene finders and these inaccuracies may provide a focus area for future gene finder developers. PMID:23226328

Development and Validation of an Automated High-Throughput System for Zebrafish In Vivo Screenings

PubMed Central

Virto, Juan M.; Holgado, Olaia; Diez, Maria; Izpisua Belmonte, Juan Carlos; Callol-Massot, Carles

2012-01-01

The zebrafish is a vertebrate model compatible with the paradigms of drug discovery. The small size and transparency of zebrafish embryos make them amenable for the automation necessary in high-throughput screenings. We have developed an automated high-throughput platform for in vivo chemical screenings on zebrafish embryos that includes automated methods for embryo dispensation, compound delivery, incubation, imaging and analysis of the results. At present, two different assays to detect cardiotoxic compounds and angiogenesis inhibitors can be automatically run in the platform, showing the versatility of the system. A validation of these two assays with known positive and negative compounds, as well as a screening for the detection of unknown anti-angiogenic compounds, have been successfully carried out in the system developed. We present a totally automated platform that allows for high-throughput screenings in a vertebrate organism. PMID:22615792

Genome wide transcriptome profiling reveals differential gene expression in secondary metabolite pathway of Cymbopogon winterianus.

PubMed

Devi, Kamalakshi; Mishra, Surajit K; Sahu, Jagajjit; Panda, Debashis; Modi, Mahendra K; Sen, Priyabrata

2016-02-15

Advances in transcriptome sequencing provide fast, cost-effective and reliable approach to generate large expression datasets especially suitable for non-model species to identify putative genes, key pathway and regulatory mechanism. Citronella (Cymbopogon winterianus) is an aromatic medicinal grass used for anti-tumoral, antibacterial, anti-fungal, antiviral, detoxifying and natural insect repellent properties. Despite of having number of utilities, the genes involved in terpenes biosynthetic pathway is not yet clearly elucidated. The present study is a pioneering attempt to generate an exhaustive molecular information of secondary metabolite pathway and to increase genomic resources in Citronella. Using high-throughput RNA-Seq technology, root and leaf transcriptome was analysed at an unprecedented depth (11.7 Gb). Targeted searches identified majority of the genes associated with metabolic pathway and other natural product pathway viz. antibiotics synthesis along with many novel genes. Terpenoid biosynthesis genes comparative expression results were validated for 15 unigenes by RT-PCR and qRT-PCR. Thus the coverage of these transcriptome is comprehensive enough to discover all known genes of major metabolic pathways. This transcriptome dataset can serve as important public information for gene expression, genomics and function genomics studies in Citronella and shall act as a benchmark for future improvement of the crop.

Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies.

PubMed

Ganna, Andrea; Lee, Donghwan; Ingelsson, Erik; Pawitan, Yudi

2015-07-01

It is common and advised practice in biomedical research to validate experimental or observational findings in a population different from the one where the findings were initially assessed. This practice increases the generalizability of the results and decreases the likelihood of reporting false-positive findings. Validation becomes critical when dealing with high-throughput experiments, where the large number of tests increases the chance to observe false-positive results. In this article, we review common approaches to determine statistical thresholds for validation and describe the factors influencing the proportion of significant findings from a 'training' sample that are replicated in a 'validation' sample. We refer to this proportion as rediscovery rate (RDR). In high-throughput studies, the RDR is a function of false-positive rate and power in both the training and validation samples. We illustrate the application of the RDR using simulated data and real data examples from metabolomics experiments. We further describe an online tool to calculate the RDR using t-statistics. We foresee two main applications. First, if the validation study has not yet been collected, the RDR can be used to decide the optimal combination between the proportion of findings taken to validation and the size of the validation study. Secondly, if a validation study has already been done, the RDR estimated using the training data can be compared with the observed RDR from the validation data; hence, the success of the validation study can be assessed. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.

PubMed

Gierliński, Marek; Cole, Christian; Schofield, Pietà; Schurch, Nicholas J; Sherstnev, Alexander; Singh, Vijender; Wrobel, Nicola; Gharbi, Karim; Simpson, Gordon; Owen-Hughes, Tom; Blaxter, Mark; Barton, Geoffrey J

2015-11-15

High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of 'bad' replicates, which can drastically affect the gene read-count distribution. RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. g.j.barton@dundee.ac.uk. © The Author 2015. Published by Oxford University Press.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

PubMed Central

Cole, Christian; Schofield, Pietà; Schurch, Nicholas J.; Sherstnev, Alexander; Singh, Vijender; Wrobel, Nicola; Gharbi, Karim; Simpson, Gordon; Owen-Hughes, Tom; Blaxter, Mark; Barton, Geoffrey J.

2015-01-01

Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution. Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. Contact: g.j.barton@dundee.ac.uk PMID:26206307

High-Throughput, Motility-Based Sorter for Microswimmers such as C. elegans

PubMed Central

Yuan, Jinzhou; Zhou, Jessie; Raizen, David M.; Bau, Haim H.

2015-01-01

Animal motility varies with genotype, disease, aging, and environmental conditions. In many studies, it is desirable to carry out high throughput motility-based sorting to isolate rare animals for, among other things, forward genetic screens to identify genetic pathways that regulate phenotypes of interest. Many commonly used screening processes are labor-intensive, lack sensitivity, and require extensive investigator training. Here, we describe a sensitive, high throughput, automated, motility-based method for sorting nematodes. Our method is implemented in a simple microfluidic device capable of sorting thousands of animals per hour per module, and is amenable to parallelism. The device successfully enriches for known C. elegans motility mutants. Furthermore, using this device, we isolate low-abundance mutants capable of suppressing the somnogenic effects of the flp-13 gene, which regulates C. elegans sleep. By performing genetic complementation tests, we demonstrate that our motility-based sorting device efficiently isolates mutants for the same gene identified by tedious visual inspection of behavior on an agar surface. Therefore, our motility-based sorter is capable of performing high throughput gene discovery approaches to investigate fundamental biological processes. PMID:26008643

HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings

PubMed Central

Rioualen, Claire; Da Costa, Quentin; Chetrit, Bernard; Charafe-Jauffret, Emmanuelle; Ginestier, Christophe

2017-01-01

High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2). PMID:28949986

Reduced Set of Virulence Genes Allows High Accuracy Prediction of Bacterial Pathogenicity in Humans

PubMed Central

Iraola, Gregorio; Vazquez, Gustavo; Spangenberg, Lucía; Naya, Hugo

2012-01-01

Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of different virulence-related genes among more than finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes () is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at ), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions. PMID:22916122

Genome-Wide siRNA-Based Functional Genomics of Pigmentation Identifies Novel Genes and Pathways That Impact Melanogenesis in Human Cells

PubMed Central

Bodemann, Brian; Petersen, Sean; Aruri, Jayavani; Koshy, Shiney; Richardson, Zachary; Le, Lu Q.; Krasieva, Tatiana; Roth, Michael G.; Farmer, Pat; White, Michael A.

2008-01-01

Melanin protects the skin and eyes from the harmful effects of UV irradiation, protects neural cells from toxic insults, and is required for sound conduction in the inner ear. Aberrant regulation of melanogenesis underlies skin disorders (melasma and vitiligo), neurologic disorders (Parkinson's disease), auditory disorders (Waardenburg's syndrome), and opthalmologic disorders (age related macular degeneration). Much of the core synthetic machinery driving melanin production has been identified; however, the spectrum of gene products participating in melanogenesis in different physiological niches is poorly understood. Functional genomics based on RNA-mediated interference (RNAi) provides the opportunity to derive unbiased comprehensive collections of pharmaceutically tractable single gene targets supporting melanin production. In this study, we have combined a high-throughput, cell-based, one-well/one-gene screening platform with a genome-wide arrayed synthetic library of chemically synthesized, small interfering RNAs to identify novel biological pathways that govern melanin biogenesis in human melanocytes. Ninety-two novel genes that support pigment production were identified with a low false discovery rate. Secondary validation and preliminary mechanistic studies identified a large panel of targets that converge on tyrosinase expression and stability. Small molecule inhibition of a family of gene products in this class was sufficient to impair chronic tyrosinase expression in pigmented melanoma cells and UV-induced tyrosinase expression in primary melanocytes. Isolation of molecular machinery known to support autophagosome biosynthesis from this screen, together with in vitro and in vivo validation, exposed a close functional relationship between melanogenesis and autophagy. In summary, these studies illustrate the power of RNAi-based functional genomics to identify novel genes, pathways, and pharmacologic agents that impact a biological phenotype and operate outside of preconceived mechanistic relationships. PMID:19057677

Pyrosequencing-based validation of a simple cell-suspension polymerase chain reaction assay for Campylobacter with application of high-processivity polymerase and novel internal amplification controls for rapid and specific detection.

PubMed

Oakley, Brian B; Line, J Eric; Berrang, Mark E; Johnson, Jessica M; Buhr, R Jeff; Cox, Nelson A; Hiett, Kelli L; Seal, Bruce S

2012-02-01

Although Campylobacter is an important food-borne human pathogen, there remains a lack of molecular diagnostic assays that are simple to use, cost-effective, and provide rapid results in research, clinical, or regulatory laboratories. Of the numerous Campylobacter assays that do exist, to our knowledge none has been empirically tested for specificity using high-throughput sequencing. Here we demonstrate the power of next-generation sequencing to determine the specificity of a widely cited Campylobacter-specific polymerase chain reaction (PCR) assay and describe a rapid method for direct cell suspension PCR to quickly and easily screen samples for Campylobacter. We present a specific protocol which eliminates the need for time-consuming and expensive genomic DNA extractions and, using a high-processivity polymerase, demonstrate conclusive screening of samples in <1 h. Pyrosequencing results show the assay to be extremely (>99%) sensitive, and spike-back experiments demonstrated a detection threshold of <10(2) CFU mL(-1). Additionally, we present 2 newly designed broad-range bacterial primer sets targeting the 23S rRNA gene that have wide applicability as internal amplification controls. Empirical testing of putative taxon-specific assays using high-throughput sequencing is an important validation step that is now financially feasible for research, regulatory, or clinical applications. Published by Elsevier Inc.

The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

PubMed Central

Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

2009-01-01

We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

Genomic-assisted haplotype analysis and the development of high-throughput SNP markers for salinity tolerance in soybean

PubMed Central

Patil, Gunvant; Do, Tuyen; Vuong, Tri D.; Valliyodan, Babu; Lee, Jeong-Dong; Chaudhary, Juhi; Shannon, J. Grover; Nguyen, Henry T.

2016-01-01

Soil salinity is a limiting factor of crop yield. The soybean is sensitive to soil salinity, and a dominant gene, Glyma03g32900 is primarily responsible for salt-tolerance. The identification of high throughput and robust markers as well as the deployment of salt-tolerant cultivars are effective approaches to minimize yield loss under saline conditions. We utilized high quality (15x) whole-genome resequencing (WGRS) on 106 diverse soybean lines and identified three major structural variants and allelic variation in the promoter and genic regions of the GmCHX1 gene. The discovery of single nucleotide polymorphisms (SNPs) associated with structural variants facilitated the design of six KASPar assays. Additionally, haplotype analysis and pedigree tracking of 93 U.S. ancestral lines were performed using publically available WGRS datasets. Identified SNP markers were validated, and a strong correlation was observed between the genotype and salt treatment phenotype (leaf scorch, chlorophyll content and Na+ accumulation) using a panel of 104 soybean lines and, an interspecific bi-parental population (F8) from PI483463 x Hutcheson. These markers precisely identified salt-tolerant/sensitive genotypes (>91%), and different structural-variants (>98%). These SNP assays, supported by accurate phenotyping, haplotype analyses and pedigree tracking information, will accelerate marker-assisted selection programs to enhance the development of salt-tolerant soybean cultivars. PMID:26781337

Comparative Transcriptomes Analysis of Red- and White-Fleshed Apples in an F1 Population of Malus sieversii f. niedzwetzkyana Crossed with M. domestica ‘Fuji’

PubMed Central

Wang, Nan; Zheng, Yi; Duan, Naibin; Zhang, Zongying; Ji, Xiaohao; Jiang, Shenghui; Sun, Shasha; Yang, Long; Bai, Yang; Fei, Zhangjun; Chen, Xuesen

2015-01-01

Transcriptome profiles of the red- and white-fleshed apples in an F1 segregating population of Malus sieversii f.Niedzwetzkyana and M.domestica ‘Fuji’ were generated using the next-generation high-throughput RNA sequencing (RNA-Seq) technology and compared. A total of 114 differentially expressed genes (DEGs) were obtained, of which 88 were up-regulated and 26 were down-regulated in red-fleshed apples. The 88 up-regulated genes were enriched with those related to flavonoid biosynthetic process and stress responses. Further analysis identified 22 genes associated with flavonoid biosynthetic process and 68 genes that may be related to stress responses. Furthermore, the expression of 20 up-regulated candidate genes (10 related to flavonoid biosynthesis, two encoding MYB transcription factors and eight related to stress responses) and 10 down-regulated genes were validated by quantitative real-time PCR. After exploring the possible regulatory network, we speculated that flavonoid metabolism might be involved in stress responses in red-fleshed apple. Our findings provide a theoretical basis for further enriching gene resources associated with flavonoid synthesis and stress responses of fruit trees and for breeding elite apples with high flavonoid content and/or increased stress tolerances. PMID:26207813

Identification of evolutionarily conserved DNA damage response genes that alter sensitivity to cisplatin

PubMed Central

Gaponova, Anna V.; Deneka, Alexander Y.; Beck, Tim N.; Liu, Hanqing; Andrianov, Gregory; Nikonova, Anna S.; Nicolas, Emmanuelle; Einarson, Margret B.; Golemis, Erica A.; Serebriiskii, Ilya G.

2017-01-01

Ovarian, head and neck, and other cancers are commonly treated with cisplatin and other DNA damaging cytotoxic agents. Altered DNA damage response (DDR) contributes to resistance of these tumors to chemotherapies, some targeted therapies, and radiation. DDR involves multiple protein complexes and signaling pathways, some of which are evolutionarily ancient and involve protein orthologs conserved from yeast to humans. To identify new regulators of cisplatin-resistance in human tumors, we integrated high throughput and curated datasets describing yeast genes that regulate sensitivity to cisplatin and/or ionizing radiation. Next, we clustered highly validated genes based on chemogenomic profiling, and then mapped orthologs of these genes in expanded genomic networks for multiple metazoans, including humans. This approach identified an enriched candidate set of genes involved in the regulation of resistance to radiation and/or cisplatin in humans. Direct functional assessment of selected candidate genes using RNA interference confirmed their activity in influencing cisplatin resistance, degree of γH2AX focus formation and ATR phosphorylation, in ovarian and head and neck cancer cell lines, suggesting impaired DDR signaling as the driving mechanism. This work enlarges the set of genes that may contribute to chemotherapy resistance and provides a new contextual resource for interpreting next generation sequencing (NGS) genomic profiling of tumors. PMID:27863405

A gene expression signature associated with survival in metastatic melanoma

PubMed Central

Mandruzzato, Susanna; Callegaro, Andrea; Turcatel, Gianluca; Francescato, Samuela; Montesco, Maria C; Chiarion-Sileni, Vanna; Mocellin, Simone; Rossi, Carlo R; Bicciato, Silvio; Wang, Ena; Marincola, Francesco M; Zanovello, Paola

2006-01-01

Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells. PMID:17129373

Comparative Transcriptomes Analysis of Red- and White-Fleshed Apples in an F1 Population of Malus sieversii f. niedzwetzkyana Crossed with M. domestica 'Fuji'.

PubMed

Wang, Nan; Zheng, Yi; Duan, Naibin; Zhang, Zongying; Ji, Xiaohao; Jiang, Shenghui; Sun, Shasha; Yang, Long; Bai, Yang; Fei, Zhangjun; Chen, Xuesen

2015-01-01

Transcriptome profiles of the red- and white-fleshed apples in an F1 segregating population of Malus sieversii f.Niedzwetzkyana and M.domestica 'Fuji' were generated using the next-generation high-throughput RNA sequencing (RNA-Seq) technology and compared. A total of 114 differentially expressed genes (DEGs) were obtained, of which 88 were up-regulated and 26 were down-regulated in red-fleshed apples. The 88 up-regulated genes were enriched with those related to flavonoid biosynthetic process and stress responses. Further analysis identified 22 genes associated with flavonoid biosynthetic process and 68 genes that may be related to stress responses. Furthermore, the expression of 20 up-regulated candidate genes (10 related to flavonoid biosynthesis, two encoding MYB transcription factors and eight related to stress responses) and 10 down-regulated genes were validated by quantitative real-time PCR. After exploring the possible regulatory network, we speculated that flavonoid metabolism might be involved in stress responses in red-fleshed apple. Our findings provide a theoretical basis for further enriching gene resources associated with flavonoid synthesis and stress responses of fruit trees and for breeding elite apples with high flavonoid content and/or increased stress tolerances.

Evaluation of the hormonal state of columnar apple trees (Malus x domestica) based on high throughput gene expression studies.

PubMed

Krost, Clemens; Petersen, Romina; Lokan, Stefanie; Brauksiepe, Bastienne; Braun, Peter; Schmidt, Erwin R

2013-02-01

The columnar phenotype of apple trees (Malus x domestica) is characterized by a compact growth habit with fruit spurs instead of lateral branches. These properties provide significant economic advantages by enabling high density plantings. The columnar growth results from the presence of a dominant allele of the gene Columnar (Co) located on chromosome 10 which can appear in a heterozygous (Co/co) or homozygous (Co/Co) state. Although two deep sequencing approaches could shed some light on the transcriptome of columnar shoot apical meristems (SAMs), the molecular mechanisms of columnar growth are not yet elaborated. Since the influence of phytohormones is believed to have a pivotal role in the establishment of the phenotype, we performed RNA-Seq experiments to study genes associated with hormone homeostasis and clearly affected by the presence of Co. Our results provide a molecular explanation for earlier findings on the hormonal state of columnar apple trees. Additionally, they allow hypotheses on how the columnar phenotype might develop. Furthermore, we show a statistically approved enrichment of differentially regulated genes on chromosome 10 in the course of validating RNA-Seq results using additional gene expression studies.

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

NASA Technical Reports Server (NTRS)

Polacek, Denise C.; Passerini, Anthony G.; Shi, Congzhu; Francesco, Nadeene M.; Manduchi, Elisabetta; Grant, Gregory R.; Powell, Steven; Bischof, Helen; Winkler, Hans; Stoeckert, Christian J Jr;

HTP-OligoDesigner: An Online Primer Design Tool for High-Throughput Gene Cloning and Site-Directed Mutagenesis.

PubMed

Camilo, Cesar M; Lima, Gustavo M A; Maluf, Fernando V; Guido, Rafael V C; Polikarpov, Igor

2016-01-01

Following burgeoning genomic and transcriptomic sequencing data, biochemical and molecular biology groups worldwide are implementing high-throughput cloning and mutagenesis facilities in order to obtain a large number of soluble proteins for structural and functional characterization. Since manual primer design can be a time-consuming and error-generating step, particularly when working with hundreds of targets, the automation of primer design process becomes highly desirable. HTP-OligoDesigner was created to provide the scientific community with a simple and intuitive online primer design tool for both laboratory-scale and high-throughput projects of sequence-independent gene cloning and site-directed mutagenesis and a Tm calculator for quick queries.

Large-scale mapping of mutations affecting zebrafish development.

PubMed

Geisler, Robert; Rauch, Gerd-Jörg; Geiger-Rudolph, Silke; Albrecht, Andrea; van Bebber, Frauke; Berger, Andrea; Busch-Nentwich, Elisabeth; Dahm, Ralf; Dekens, Marcus P S; Dooley, Christopher; Elli, Alexandra F; Gehring, Ines; Geiger, Horst; Geisler, Maria; Glaser, Stefanie; Holley, Scott; Huber, Matthias; Kerr, Andy; Kirn, Anette; Knirsch, Martina; Konantz, Martina; Küchler, Axel M; Maderspacher, Florian; Neuhauss, Stephan C; Nicolson, Teresa; Ober, Elke A; Praeg, Elke; Ray, Russell; Rentzsch, Brit; Rick, Jens M; Rief, Eva; Schauerte, Heike E; Schepp, Carsten P; Schönberger, Ulrike; Schonthaler, Helia B; Seiler, Christoph; Sidi, Samuel; Söllner, Christian; Wehner, Anja; Weiler, Christian; Nüsslein-Volhard, Christiane

2007-01-09

Large-scale mutagenesis screens in the zebrafish employing the mutagen ENU have isolated several hundred mutant loci that represent putative developmental control genes. In order to realize the potential of such screens, systematic genetic mapping of the mutations is necessary. Here we report on a large-scale effort to map the mutations generated in mutagenesis screening at the Max Planck Institute for Developmental Biology by genome scanning with microsatellite markers. We have selected a set of microsatellite markers and developed methods and scoring criteria suitable for efficient, high-throughput genome scanning. We have used these methods to successfully obtain a rough map position for 319 mutant loci from the Tübingen I mutagenesis screen and subsequent screening of the mutant collection. For 277 of these the corresponding gene is not yet identified. Mapping was successful for 80 % of the tested loci. By comparing 21 mutation and gene positions of cloned mutations we have validated the correctness of our linkage group assignments and estimated the standard error of our map positions to be approximately 6 cM. By obtaining rough map positions for over 300 zebrafish loci with developmental phenotypes, we have generated a dataset that will be useful not only for cloning of the affected genes, but also to suggest allelism of mutations with similar phenotypes that will be identified in future screens. Furthermore this work validates the usefulness of our methodology for rapid, systematic and inexpensive microsatellite mapping of zebrafish mutations.

Alkylation sensitivity screens reveal a conserved cross-species functionome

PubMed Central

Svilar, David; Dyavaiah, Madhu; Brown, Ashley R.; Tang, Jiang-bo; Li, Jianfeng; McDonald, Peter R.; Shun, Tong Ying; Braganza, Andrea; Wang, Xiao-hong; Maniar, Salony; St Croix, Claudette M.; Lazo, John S.; Pollack, Ian F.; Begley, Thomas J.; Sobol, Robert W.

2013-01-01

To identify genes that contribute to chemotherapy resistance in glioblastoma, we conducted a synthetic lethal screen in a chemotherapy-resistant glioblastoma derived cell line with the clinical alkylator temozolomide (TMZ) and an siRNA library tailored towards “druggable” targets. Select DNA repair genes in the screen were validated independently, confirming the DNA glycosylases UNG and MYH as well as MPG to be involved in the response to high dose TMZ. The involvement of UNG and MYH is likely the result of a TMZ-induced burst of reactive oxygen species. We then compared the human TMZ sensitizing genes identified in our screen with those previously identified from alkylator screens conducted in E. coli and S. cerevisiae. The conserved biological processes across all three species composes an Alkylation Functionome that includes many novel proteins not previously thought to impact alkylator resistance. This high-throughput screen, validation and cross-species analysis was then followed by a mechanistic analysis of two essential nodes: base excision repair (BER) DNA glycosylases (UNG, human and mag1, S. cerevisiae) and protein modification systems, including UBE3B and ICMT in human cells or pby1, lip22, stp22 and aim22 in S. cerevisiae. The conserved processes of BER and protein modification were dual targeted and yielded additive sensitization to alkylators in S. cerevisiae. In contrast, dual targeting of BER and protein modification genes in human cells did not increase sensitivity, suggesting an epistatic relationship. Importantly, these studies provide potential new targets to overcome alkylating agent resistance. PMID:23038810

Strain Library Imaging Protocol for high-throughput, automated single-cell microscopy of large bacterial collections arrayed on multiwell plates.

PubMed

Shi, Handuo; Colavin, Alexandre; Lee, Timothy K; Huang, Kerwyn Casey

2017-02-01

Single-cell microscopy is a powerful tool for studying gene functions using strain libraries, but it suffers from throughput limitations. Here we describe the Strain Library Imaging Protocol (SLIP), which is a high-throughput, automated microscopy workflow for large strain collections that requires minimal user involvement. SLIP involves transferring arrayed bacterial cultures from multiwell plates onto large agar pads using inexpensive replicator pins and automatically imaging the resulting single cells. The acquired images are subsequently reviewed and analyzed by custom MATLAB scripts that segment single-cell contours and extract quantitative metrics. SLIP yields rich data sets on cell morphology and gene expression that illustrate the function of certain genes and the connections among strains in a library. For a library arrayed on 96-well plates, image acquisition can be completed within 4 min per plate.

[Weighted gene co-expression network analysis in biomedicine research].

PubMed

Liu, Wei; Li, Li; Ye, Hua; Tu, Wei

2017-11-25

High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.

Comprehensive analysis of the T-cell receptor beta chain gene in rhesus monkey by high throughput sequencing

PubMed Central

Li, Zhoufang; Liu, Guangjie; Tong, Yin; Zhang, Meng; Xu, Ying; Qin, Li; Wang, Zhanhui; Chen, Xiaoping; He, Jiankui

2015-01-01

Profiling immune repertoires by high throughput sequencing enhances our understanding of immune system complexity and immune-related diseases in humans. Previously, cloning and Sanger sequencing identified limited numbers of T cell receptor (TCR) nucleotide sequences in rhesus monkeys, thus their full immune repertoire is unknown. We applied multiplex PCR and Illumina high throughput sequencing to study the TCRβ of rhesus monkeys. We identified 1.26 million TCRβ sequences corresponding to 643,570 unique TCRβ sequences and 270,557 unique complementarity-determining region 3 (CDR3) gene sequences. Precise measurements of CDR3 length distribution, CDR3 amino acid distribution, length distribution of N nucleotide of junctional region, and TCRV and TCRJ gene usage preferences were performed. A comprehensive profile of rhesus monkey immune repertoire might aid human infectious disease studies using rhesus monkeys. PMID:25961410

A versatile toolkit for high throughput functional genomics with Trichoderma reesei

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

Schuster, Andre; Bruno, Kenneth S.; Collett, James R.

2012-01-02

The ascomycete fungus, Trichoderma reesei (anamorph of Hypocrea jecorina), represents a biotechnological workhorse and is currently one of the most proficient cellulase producers. While strain improvement was traditionally accomplished by random mutagenesis, a detailed understanding of cellulase regulation can only be gained using recombinant technologies. RESULTS: Aiming at high efficiency and high throughput methods, we present here a construction kit for gene knock out in T. reesei. We provide a primer database for gene deletion using the pyr4, amdS and hph selection markers. For high throughput generation of gene knock outs, we constructed vectors using yeast mediated recombination and thenmore » transformed a T. reesei strain deficient in non-homologous end joining (NHEJ) by spore electroporation. This NHEJ-defect was subsequently removed by crossing of mutants with a sexually competent strain derived from the parental strain, QM9414.CONCLUSIONS:Using this strategy and the materials provided, high throughput gene deletion in T. reesei becomes feasible. Moreover, with the application of sexual development, the NHEJ-defect can be removed efficiently and without the need for additional selection markers. The same advantages apply for the construction of multiple mutants by crossing of strains with different gene deletions, which is now possible with considerably less hands-on time and minimal screening effort compared to a transformation approach. Consequently this toolkit can considerably boost research towards efficient exploitation of the resources of T. reesei for cellulase expression and hence second generation biofuel production.« less

Microplate-based platform for combined chromatin and DNA methylation immunoprecipitation assays

PubMed Central

2011-01-01

Background The processes that compose expression of a given gene are far more complex than previously thought presenting unprecedented conceptual and mechanistic challenges that require development of new tools. Chromatin structure, which is regulated by DNA methylation and histone modification, is at the center of gene regulation. Immunoprecipitations of chromatin (ChIP) and methylated DNA (MeDIP) represent a major achievement in this area that allow researchers to probe chromatin modifications as well as specific protein-DNA interactions in vivo and to estimate the density of proteins at specific sites genome-wide. Although a critical component of chromatin structure, DNA methylation has often been studied independently of other chromatin events and transcription. Results To allow simultaneous measurements of DNA methylation with other genomic processes, we developed and validated a simple and easy-to-use high throughput microplate-based platform for analysis of DNA methylation. Compared to the traditional beads-based MeDIP the microplate MeDIP was more sensitive and had lower non-specific binding. We integrated the MeDIP method with a microplate ChIP assay which allows measurements of both DNA methylation and histone marks at the same time, Matrix ChIP-MeDIP platform. We illustrated several applications of this platform to relate DNA methylation, with chromatin and transcription events at selected genes in cultured cells, human cancer and in a model of diabetic kidney disease. Conclusion The high throughput capacity of Matrix ChIP-MeDIP to profile tens and potentially hundreds of different genomic events at the same time as DNA methylation represents a powerful platform to explore complex genomic mechanism at selected genes in cultured cells and in whole tissues. In this regard, Matrix ChIP-MeDIP should be useful to complement genome-wide studies where the rich chromatin and transcription database resources provide fruitful foundation to pursue mechanistic, functional and diagnostic information at genes of interest in health and disease. PMID:22098709

Development of SNP Genotyping Assays for Seed Composition Traits in Soybean

PubMed Central

Patil, Gunvant; Chaudhary, Juhi; Vuong, Tri D.; Jenkins, Brian; Qiu, Dan; Kadam, Suhas; Shannon, Grover J.

2017-01-01

Seed composition is one of the most important determinants of the economic values in soybean. The quality and quantity of different seed components, such as oil, protein, and carbohydrates, are crucial ingredients in food, feed, and numerous industrial products. Soybean researchers have successfully developed and utilized a diverse set of molecular markers for seed trait improvement in soybean breeding programs. It is imperative to design and develop molecular assays that are accurate, robust, high-throughput, cost-effective, and available on a common genotyping platform. In the present study, we developed and validated KASP (Kompetitive allele-specific polymerase chain reaction) genotyping assays based on previously known functional mutant alleles for the seed composition traits, including fatty acids, oligosaccharides, trypsin inhibitor, and lipoxygenase. These assays were validated on mutant sources as well as mapping populations and precisely distinguish the homozygotes and heterozygotes of the mutant genes. With the obvious advantages, newly developed KASP assays in this study can substitute the genotyping assays that were previously developed for marker-assisted selection (MAS). The functional gene-based assay resource developed using common genotyping platform will be helpful to accelerate efforts to improve soybean seed composition traits. PMID:28630621

From the Cover: Development and Application of a Dual Rat and Human AHR Activation Assay.

PubMed

Brown, Martin R; Garside, Helen; Thompson, Emma; Atwal, Saseela; Bean, Chloe; Goodall, Tony; Sullivan, Michael; Graham, Mark J

2017-12-01

Significant prolonged aryl hydrocarbon receptor (AHR) activation, classically exhibited following exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin, can cause a variety of undesirable toxicological effects. Novel pharmaceutical chemistries also have the potential to cause activation of AHR and consequent toxicities in pre-clinical species and man. Previous methods either employed relatively expensive and low-throughput primary hepatocyte dosing with PCR endpoint, or low resolution overexpressing reporter gene assays. We have developed, validated and applied an in vitro microtitre plate imaging-based medium throughput screening assay for the assessment of endogenous species-specific AHR activation potential via detection of induction of the surrogate transcriptional target Cytochrome P450 CYP1A1. Routine testing of pharmaceutical drug development candidate chemistries using this assay can influence the chemical design process and highlight AHR liabilities. This assay should be introduced such that human AHR activation liability is flagged early for confirmatory testing. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

PubMed Central

Pauli, Chantal; Hopkins, Benjamin D.; Prandi, Davide; Shaw, Reid; Fedrizzi, Tarcisio; Sboner, Andrea; Sailer, Verena; Augello, Michael; Puca, Loredana; Rosati, Rachele; McNary, Terra J.; Churakova, Yelena; Cheung, Cynthia; Triscott, Joanna; Pisapia, David; Rao, Rema; Mosquera, Juan Miguel; Robinson, Brian; Faltas, Bishoy M.; Emerling, Brooke E.; Gadi, Vijayakrishna K.; Bernard, Brady; Elemento, Olivier; Beltran, Himisha; Dimichelis, Francesca; Kemp, Christopher J.; Grandori, Carla; Cantley, Lewis C.; Rubin, Mark A.

2017-01-01

Precision Medicine is an approach that takes into account the influence of individuals' genes, environment and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform, which integrates whole exome sequencing (WES) with a living biobank that enables high throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures, and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an IRB approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high throughput drug screening effective strategies. Analysis of tumor derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted. PMID:28331002

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.

MIPHENO: Data normalization for high throughput metabolic analysis.

EPA Science Inventory

High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course...

High Throughput Computing Impact on Meta Genomics (Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

ScienceCinema

Gore, Brooklin

2018-02-01

This presentation includes a brief background on High Throughput Computing, correlating gene transcription factors, optical mapping, genotype to phenotype mapping via QTL analysis, and current work on next gen sequencing.

Gene cassette knock-in in mammalian cells and zygotes by enhanced MMEJ.

PubMed

Aida, Tomomi; Nakade, Shota; Sakuma, Tetsushi; Izu, Yayoi; Oishi, Ayu; Mochida, Keiji; Ishikubo, Harumi; Usami, Takako; Aizawa, Hidenori; Yamamoto, Takashi; Tanaka, Kohichi

2016-11-28

Although CRISPR/Cas enables one-step gene cassette knock-in, assembling targeting vectors containing long homology arms is a laborious process for high-throughput knock-in. We recently developed the CRISPR/Cas-based precise integration into the target chromosome (PITCh) system for a gene cassette knock-in without long homology arms mediated by microhomology-mediated end-joining. Here, we identified exonuclease 1 (Exo1) as an enhancer for PITCh in human cells. By combining the Exo1 and PITCh-directed donor vectors, we achieved convenient one-step knock-in of gene cassettes and floxed allele both in human cells and mouse zygotes. Our results provide a technical platform for high-throughput knock-in.

SNP discovery by high-throughput sequencing in soybean

PubMed Central

2010-01-01

Background With the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology. Results A total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp. Conclusions We have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops. PMID:20701770

Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer’s disease

PubMed Central

2013-01-01

Background High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation. Results The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer’s disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer’s. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of AT-1 mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Alignment with emerging literature confirmed many predictions derived from early-stage Alzheimer’s disease’ CDM. Conclusions A flexible approach for high-throughput data analysis, the Compact Disease Model generation, allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses. PMID:24196233

A high-throughput method for the detection of homoeologous gene deletions in hexaploid wheat

PubMed Central

2010-01-01

Background Mutational inactivation of plant genes is an essential tool in gene function studies. Plants with inactivated or deleted genes may also be exploited for crop improvement if such mutations/deletions produce a desirable agronomical and/or quality phenotype. However, the use of mutational gene inactivation/deletion has been impeded in polyploid plant species by genetic redundancy, as polyploids contain multiple copies of the same genes (homoeologous genes) encoded by each of the ancestral genomes. Similar to many other crop plants, bread wheat (Triticum aestivum L.) is polyploid; specifically allohexaploid possessing three progenitor genomes designated as 'A', 'B', and 'D'. Recently modified TILLING protocols have been developed specifically for mutation detection in wheat. Whilst extremely powerful in detecting single nucleotide changes and small deletions, these methods are not suitable for detecting whole gene deletions. Therefore, high-throughput methods for screening of candidate homoeologous gene deletions are needed for application to wheat populations generated by the use of certain mutagenic agents (e.g. heavy ion irradiation) that frequently generate whole-gene deletions. Results To facilitate the screening for specific homoeologous gene deletions in hexaploid wheat, we have developed a TaqMan qPCR-based method that allows high-throughput detection of deletions in homoeologous copies of any gene of interest, provided that sufficient polymorphism (as little as a single nucleotide difference) amongst homoeologues exists for specific probe design. We used this method to identify deletions of individual TaPFT1 homoeologues, a wheat orthologue of the disease susceptibility and flowering regulatory gene PFT1 in Arabidopsis. This method was applied to wheat nullisomic-tetrasomic lines as well as other chromosomal deletion lines to locate the TaPFT1 gene to the long arm of chromosome 5. By screening of individual DNA samples from 4500 M2 mutant wheat lines generated by heavy ion irradiation, we detected multiple mutants with deletions of each TaPFT1 homoeologue, and confirmed these deletions using a CAPS method. We have subsequently designed, optimized, and applied this method for the screening of homoeologous deletions of three additional wheat genes putatively involved in plant disease resistance. Conclusions We have developed a method for automated, high-throughput screening to identify deletions of individual homoeologues of a wheat gene. This method is also potentially applicable to other polyploidy plants. PMID:21114819

High-resolution molecular validation of self-renewal and spontaneous differentiation in adipose-tissue derived human mesenchymal stem cells cultured in human platelet lysate

PubMed Central

Dudakovic, Amel Dudakovic; Camilleri, Emily; Riester, Scott M.; Lewallen, Eric A.; Kvasha, Sergiy; Chen, Xiaoyue; Radel, Darcie J.; Anderson, Jarett M.; Nair, Asha A.; Evans, Jared M.; Krych, Aaron J.; Smith, Jay; Deyle, David R.; Stein, Janet L.; Stein, Gary S.; Im, Hee-Jeong; Cool, Simon M.; Westendorf, Jennifer J.; Kakar, Sanjeev; Dietz, Allan B.; van Wijnen, Andre J.

2014-01-01

Improving the effectiveness of adipose-tissue derived human mesenchymal stromal/stem cells (AMSCs) for skeletal therapies requires a detailed characterization of mechanisms supporting cell proliferation and multi-potency. We investigated the molecular phenotype of AMSCs that were either actively proliferating in platelet lysate or in a basal non-proliferative state. Flow cytometry combined with high-throughput RNA sequencing (RNASeq) and RT-qPCR analyses validate that AMSCs express classic mesenchymal cell surface markers (e.g., CD44, CD73/NT5E, CD90/THY1 and CD105/ENG). Expression of CD90 is selectively elevated at confluence. Self-renewing AMSCs express a standard cell cycle program that successively mediates DNA replication, chromatin packaging, cyto-architectural enlargement and mitotic division. Confluent AMSCs preferentially express genes involved in extracellular matrix (ECM) formation and cellular communication. For example, cell cycle-related biomarkers (e.g., cyclins E2 and B2, transcription factor E2F1) and histone-related genes (e.g., H4, HINFP, NPAT) are elevated in proliferating AMSCs, while ECM genes are strongly upregulated (>10 fold) in quiescent AMSCs. AMSCs also express pluripotency genes (e.g., POU5F1, NANOG, KLF4) and early mesenchymal markers (e.g., NES, ACTA2) consistent with their multipotent phenotype. Strikingly, AMSCs modulate expression of WNT signaling components and switch production of WNT ligands (from WNT5A/WNT5B/WNT7B to WNT2/WNT2B), while up-regulating WNT-related genes (WISP2, SFRP2 and SFRP4). Furthermore, post-proliferative AMSCs spontaneously express fibroblastic, osteogenic, chondrogenic and adipogenic biomarkers when maintained in confluent cultures. Our findings validate the biological properties of self-renewing and multi-potent AMSCs by providing high-resolution quality control data that support their clinical versatility. PMID:24905804

GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.

PubMed

Jiang, Lan; Chen, Huidong; Pinello, Luca; Yuan, Guo-Cheng

2016-07-01

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population.

Drug discovery in the next millennium.

PubMed

Ohlstein, E H; Ruffolo, R R; Elliott, J D

2000-01-01

Selection and validation of novel molecular targets have become of paramount importance in light of the plethora of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. This review addresses these technological advances as well as several new areas that have been created by necessity to deal with this new paradigm, such as bioinformatics, cheminformatics, and functional genomics. With many of these key components of future drug discovery now in place, it is possible to map out a critical path for this process that will be used into the new millennium.

[Caenorhabditis elegans: a powerful tool for drug discovery].

PubMed

Jia, Xi-Hua; Cao, Cheng

2009-07-01

A simple model organism Caenorhabditis elegans has contributed substantially to the fundamental researches in biology. In an era of functional genomics, nematode worm has been developed into a multi-purpose tool that can be exploited to identify disease-causing or disease-associated genes, validate potential drug targets. This, coupled with its genetic amenability, low cost experimental manipulation and compatibility with high throughput screening in an intact physiological condition, makes the model organism into an effective toolbox for drug discovery. This review shows the unique features of C. elegans, how it can play a valuable role in our understanding of the molecular mechanism of human diseases and finding drug leads in drug development process.

A high-throughput screen for single gene activities: isolation of apoptosis inducers.

PubMed

Albayrak, Timur; Grimm, Stefan

2003-05-16

We describe a novel genetic screen that is performed by transfecting every individual clone of an expression library into a separate population of cells in a high-throughput mode. The screen allows one to achieve a hitherto unattained sensitivity in expression cloning which was exploited in a first read-out to clone apoptosis-inducing genes. This led to the isolation of several genes whose proteins induce distinct phenotypes of apoptosis in 293T cells. One of the isolated genes is the tumor suppressor cytochrome b(L) (cybL), a component of the respiratory chain complex II, that diminishes the activity of this complex for apoptosis induction. This gene is more efficient and specific for causing cell death than a drug with the same activity. These results suggest further applications, both of the isolated genes and the screen.

Transcriptome-wide analysis of alternative RNA splicing events in Epstein-Barr virus-associated gastric carcinomas

PubMed Central

Armero, Victoria E. S.; Tremblay, Marie-Pier; Allaire, Andréa; Boudreault, Simon; Martenon-Brodeur, Camille; Duval, Cyntia; Durand, Mathieu; Lapointe, Elvy; Thibault, Philippe; Tremblay-Létourneau, Maude; Perreault, Jean-Pierre; Scott, Michelle S.

2017-01-01

Multiple human diseases including cancer have been associated with a dysregulation in RNA splicing patterns. In the current study, modifications to the global RNA splicing landscape of cellular genes were investigated in the context of Epstein-Barr virus-associated gastric cancer. Global alterations to the RNA splicing landscape of cellular genes was examined in a large-scale screen from 295 primary gastric adenocarcinomas using high-throughput RNA sequencing data. RT-PCR analysis, mass spectrometry, and co-immunoprecipitation studies were also used to experimentally validate and investigate the differential alternative splicing (AS) events that were observed through RNA-seq studies. Our study identifies alterations in the AS patterns of approximately 900 genes such as tumor suppressor genes, transcription factors, splicing factors, and kinases. These findings allowed the identification of unique gene signatures for which AS is misregulated in both Epstein-Barr virus-associated gastric cancer and EBV-negative gastric cancer. Moreover, we show that the expression of Epstein–Barr nuclear antigen 1 (EBNA1) leads to modifications in the AS profile of cellular genes and that the EBNA1 protein interacts with cellular splicing factors. These findings provide insights into the molecular differences between various types of gastric cancer and suggest a role for the EBNA1 protein in the dysregulation of cellular AS. PMID:28493890

Transcriptome-wide analysis of alternative RNA splicing events in Epstein-Barr virus-associated gastric carcinomas.

PubMed

Armero, Victoria E S; Tremblay, Marie-Pier; Allaire, Andréa; Boudreault, Simon; Martenon-Brodeur, Camille; Duval, Cyntia; Durand, Mathieu; Lapointe, Elvy; Thibault, Philippe; Tremblay-Létourneau, Maude; Perreault, Jean-Pierre; Scott, Michelle S; Bisaillon, Martin

2017-01-01

Multiple human diseases including cancer have been associated with a dysregulation in RNA splicing patterns. In the current study, modifications to the global RNA splicing landscape of cellular genes were investigated in the context of Epstein-Barr virus-associated gastric cancer. Global alterations to the RNA splicing landscape of cellular genes was examined in a large-scale screen from 295 primary gastric adenocarcinomas using high-throughput RNA sequencing data. RT-PCR analysis, mass spectrometry, and co-immunoprecipitation studies were also used to experimentally validate and investigate the differential alternative splicing (AS) events that were observed through RNA-seq studies. Our study identifies alterations in the AS patterns of approximately 900 genes such as tumor suppressor genes, transcription factors, splicing factors, and kinases. These findings allowed the identification of unique gene signatures for which AS is misregulated in both Epstein-Barr virus-associated gastric cancer and EBV-negative gastric cancer. Moreover, we show that the expression of Epstein-Barr nuclear antigen 1 (EBNA1) leads to modifications in the AS profile of cellular genes and that the EBNA1 protein interacts with cellular splicing factors. These findings provide insights into the molecular differences between various types of gastric cancer and suggest a role for the EBNA1 protein in the dysregulation of cellular AS.

Biomining active cellulases from a mining bioremediation system.

PubMed

Mewis, Keith; Armstrong, Zachary; Song, Young C; Baldwin, Susan A; Withers, Stephen G; Hallam, Steven J

2013-09-20

Functional metagenomics has emerged as a powerful method for gene model validation and enzyme discovery from natural and human engineered ecosystems. Here we report development of a high-throughput functional metagenomic screen incorporating bioinformatic and biochemical analyses features. A fosmid library containing 6144 clones sourced from a mining bioremediation system was screened for cellulase activity using 2,4-dinitrophenyl β-cellobioside, a previously proven cellulose model substrate. Fifteen active clones were recovered and fully sequenced revealing 9 unique clones with the ability to hydrolyse 1,4-β-D-glucosidic linkages. Transposon mutagenesis identified genes belonging to glycoside hydrolase (GH) 1, 3, or 5 as necessary for mediating this activity. Reference trees for GH 1, 3, and 5 families were generated from sequences in the CAZy database for automated phylogenetic analysis of fosmid end and active clone sequences revealing known and novel cellulase encoding genes. Active cellulase genes recovered in functional screens were subcloned into inducible high copy plasmids, expressed and purified to determine enzymatic properties including thermostability, pH optima, and substrate specificity. The workflow described here provides a general paradigm for recovery and characterization of microbially derived genes and gene products based on genetic logic and contemporary screening technologies developed for model organismal systems. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

High-throughput method for the quantitation of metabolites and co-factors from homocysteine-methionine cycle for nutritional status assessment.

PubMed

Da Silva, Laeticia; Collino, Sebastiano; Cominetti, Ornella; Martin, Francois-Pierre; Montoliu, Ivan; Moreno, Sergio Oller; Corthesy, John; Kaput, Jim; Kussmann, Martin; Monteiro, Jacqueline Pontes; Guiraud, Seu Ping

2016-09-01

There is increasing interest in the profiling and quantitation of methionine pathway metabolites for health management research. Currently, several analytical approaches are required to cover metabolites and co-factors. We report the development and the validation of a method for the simultaneous detection and quantitation of 13 metabolites in red blood cells. The method, validated in a cohort of healthy human volunteers, shows a high level of accuracy and reproducibility. This high-throughput protocol provides a robust coverage of central metabolites and co-factors in one single analysis and in a high-throughput fashion. In large-scale clinical settings, the use of such an approach will significantly advance the field of nutritional research in health and disease.

RED: A Java-MySQL Software for Identifying and Visualizing RNA Editing Sites Using Rule-Based and Statistical Filters.

PubMed

Sun, Yongmei; Li, Xing; Wu, Di; Pan, Qi; Ji, Yuefeng; Ren, Hong; Ding, Keyue

2016-01-01

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software - 'RED' (RNA Editing sites Detector) - for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector.

RED: A Java-MySQL Software for Identifying and Visualizing RNA Editing Sites Using Rule-Based and Statistical Filters

PubMed Central

Sun, Yongmei; Li, Xing; Wu, Di; Pan, Qi; Ji, Yuefeng; Ren, Hong; Ding, Keyue

2016-01-01

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software − ‘RED’ (RNA Editing sites Detector) − for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector. PMID:26930599

A high-quality annotated transcriptome of swine peripheral blood

USDA-ARS?s Scientific Manuscript database

Background: High throughput gene expression profiling assays of peripheral blood are widely used in biomedicine, as well as in animal genetics and physiology research. Accurate, comprehensive, and precise interpretation of such high throughput assays relies on well-characterized reference genomes an...

Picking Cell Lines for High-Throughput Transcriptomic Toxicity Screening (SOT)

EPA Science Inventory

High throughput, whole genome transcriptomic profiling is a promising approach to comprehensively evaluate chemicals for potential biological effects. To be useful for in vitro toxicity screening, gene expression must be quantified in a set of representative cell types that captu...

High-throughput detection and screening of plants modified by gene editing using quantitative real-time polymerase chain reaction.

PubMed

Peng, Cheng; Wang, Hua; Xu, Xiaoli; Wang, Xiaofu; Chen, Xiaoyun; Wei, Wei; Lai, Yongmin; Liu, Guoquan; Godwin, Ian Douglas; Li, Jieqin; Zhang, Ling; Xu, Junfeng

2018-05-15

Gene editing techniques are becoming powerful tools for modifying target genes in organisms. Although several methods have been developed to detect gene-edited organisms, these techniques are time and labour intensive. Meanwhile, few studies have investigated high-throughput detection and screening strategies for plants modified by gene editing. In this study, we developed a simple, sensitive and high-throughput quantitative real-time (qPCR)-based method. The qPCR-based method exploits two differently labelled probes that are placed within one amplicon at the gene editing target site to simultaneously detect the wild-type and a gene-edited mutant. We showed that the qPCR-based method can accurately distinguish CRISPR/Cas9-induced mutants from the wild-type in several different plant species, such as Oryza sativa, Arabidopsis thaliana, Sorghum bicolor, and Zea mays. Moreover, the method can subsequently determine the mutation type by direct sequencing of the qPCR products of mutations due to gene editing. The qPCR-based method is also sufficiently sensitive to distinguish between heterozygous and homozygous mutations in T 0 transgenic plants. In a 384-well plate format, the method enabled the simultaneous analysis of up to 128 samples in three replicates without handling the post-polymerase chain reaction (PCR) products. Thus, we propose that our method is an ideal choice for screening plants modified by gene editing from many candidates in T 0 transgenic plants, which will be widely used in the area of plant gene editing. © 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.

Investigating the Responses of Cronobacter sakazakii to Garlic-Drived Organosulfur Compounds: a Systematic Study of Pathogenic-Bacterium Injury by Use of High-Throughput Whole-Transcriptome Sequencing and Confocal Micro-Raman Spectroscopy

PubMed Central

Feng, Shaolong; Eucker, Tyson P.; Holly, Mayumi K.; Konkel, Michael E.

2014-01-01

We present the results of a study using high-throughput whole-transcriptome sequencing (RNA-seq) and vibrational spectroscopy to characterize and fingerprint pathogenic-bacterium injury under conditions of unfavorable stress. Two garlic-derived organosulfur compounds were found to be highly effective antimicrobial compounds against Cronobacter sakazakii, a leading pathogen associated with invasive infection of infants and causing meningitis, necrotizing entercolitis, and bacteremia. RNA-seq shows changes in gene expression patterns and transcriptomic response, while confocal micro-Raman spectroscopy characterizes macromolecular changes in the bacterial cell resulting from this chemical stress. RNA-seq analyses showed that the bacterial response to ajoene differed from the response to diallyl sulfide. Specifically, ajoene caused downregulation of motility-related genes, while diallyl sulfide treatment caused an increased expression of cell wall synthesis genes. Confocal micro-Raman spectroscopy revealed that the two compounds appear to have the same phase I antimicrobial mechanism of binding to thiol-containing proteins/enzymes in bacterial cells generating a disulfide stretching band but different phase II antimicrobial mechanisms, showing alterations in the secondary structures of proteins in two different ways. Diallyl sulfide primarily altered the α-helix and β-sheet, as reflected in changes in amide I, while ajoene altered the structures containing phenylalanine and tyrosine. Bayesian probability analysis validated the ability of principal component analysis to differentiate treated and control C. sakazakii cells. Scanning electron microscopy confirmed cell injury, showing significant morphological variations in cells following treatments by these two compounds. Findings from this study aid in the development of effective intervention strategies to reduce the risk of C. sakazakii contamination in the food production environment and on food contact surfaces, reducing the risks to susceptible consumers. PMID:24271174

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

PubMed

Cui, Hui; Zhang, Menghuan; Yang, Qingmin; Li, Xiangyi; Liebman, Michael; Yu, Ying; Xie, Lu

2018-01-01

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

Tiny giants of gene regulation: experimental strategies for microRNA functional studies

PubMed Central

Steinkraus, Bruno R.; Toegel, Markus

2016-01-01

The discovery over two decades ago of short regulatory microRNAs (miRNAs) has led to the inception of a vast biomedical research field dedicated to understanding these powerful orchestrators of gene expression. Here we aim to provide a comprehensive overview of the methods and techniques underpinning the experimental pipeline employed for exploratory miRNA studies in animals. Some of the greatest challenges in this field have been uncovering the identity of miRNA–target interactions and deciphering their significance with regard to particular physiological or pathological processes. These endeavors relied almost exclusively on the development of powerful research tools encompassing novel bioinformatics pipelines, high‐throughput target identification platforms, and functional target validation methodologies. Thus, in an unparalleled manner, the biomedical technology revolution unceasingly enhanced and refined our ability to dissect miRNA regulatory networks and understand their roles in vivo in the context of cells and organisms. Recurring motifs of target recognition have led to the creation of a large number of multifactorial bioinformatics analysis platforms, which have proved instrumental in guiding experimental miRNA studies. Subsequently, the need for discovery of miRNA–target binding events in vivo drove the emergence of a slew of high‐throughput multiplex strategies, which now provide a viable prospect for elucidating genome‐wide miRNA–target binding maps in a variety of cell types and tissues. Finally, deciphering the functional relevance of miRNA post‐transcriptional gene silencing under physiological conditions, prompted the evolution of a host of technologies enabling systemic manipulation of miRNA homeostasis as well as high‐precision interference with their direct, endogenous targets. WIREs Dev Biol 2016, 5:311–362. doi: 10.1002/wdev.223 For further resources related to this article, please visit the WIREs website. PMID:26950183

Gold Nanoparticle Mediated Laser Transfection for Efficient siRNA Mediated Gene Knock Down

PubMed Central

Heinemann, Dag; Schomaker, Markus; Kalies, Stefan; Schieck, Maximilian; Carlson, Regina; Escobar, Hugo Murua; Ripken, Tammo; Meyer, Heiko; Heisterkamp, Alexander

2013-01-01

Laser based transfection methods have proven to be an efficient and gentle alternative to established molecule delivery methods like lipofection or electroporation. Among the laser based methods, gold nanoparticle mediated laser transfection bears the major advantage of high throughput and easy usability. This approach uses plasmon resonances on gold nanoparticles unspecifically attached to the cell membrane to evoke transient and spatially defined cell membrane permeabilization. In this study, we explore the parameter regime for gold nanoparticle mediated laser transfection for the delivery of molecules into cell lines and prove its suitability for siRNA mediated gene knock down. The developed setup allows easy usage and safe laser operation in a normal lab environment. We applied a 532 nm Nd:YAG microchip laser emitting 850 ps pulses at a repetition rate of 20.25 kHz. Scanning velocities of the laser spot over the sample of up to 200 mm/s were tested without a decline in perforation efficiency. This velocity leads to a process speed of ∼8 s per well of a 96 well plate. The optimal particle density was determined to be ∼6 particles per cell using environmental scanning electron microscopy. Applying the optimized parameters transfection efficiencies of 88% were achieved in canine pleomorphic adenoma ZMTH3 cells using a fluorescent labeled siRNA while maintaining a high cell viability of >90%. Gene knock down of d2-EGFP was demonstrated and validated by fluorescence repression and western blot analysis. On basis of our findings and established mathematical models we suppose a mixed transfection mechanism consisting of thermal and multiphoton near field effects. Our findings emphasize that gold nanoparticle mediated laser transfection provides an excellent tool for molecular delivery for both, high throughput purposes and the transfection of sensitive cells types. PMID:23536802

MMACHC gene mutation in familial hypogonadism with neurological symptoms.

PubMed

Shi, Changhe; Shang, Dandan; Sun, Shilei; Mao, Chengyuan; Qin, Jie; Luo, Haiyang; Shao, Mingwei; Chen, Zhengguang; Liu, Yutao; Liu, Xinjing; Song, Bo; Xu, Yuming

2015-12-15

Recent studies have convincingly documented that hypogonadism is a component of various hereditary disorders and is often recognized as an important clinical feature in combination with various neurological symptoms, yet, the causative genes in a few related families are still unknown. High-throughput sequencing has become an efficient method to identify causative genes in related complex hereditary disorders. In this study, we performed exome sequencing in a family presenting hypergonadotropic hypogonadism with neurological presentations of mental retardation, epilepsy, ataxia, and leukodystrophy. After bioinformatic analysis and Sanger sequencing validation, we identified compound heterozygous mutations: c.482G>A (p.R161Q) and c.609G>A (p.W203X) in MMACHC gene in this pedigree. MMACHC was previously confirmed to be responsible for methylmalonic aciduria (MMA) combined with homocystinuria, cblC type (cblC disease), a hereditary vitamin B12 metabolic disorder. Biochemical and gas chromatography-mass spectrometry (GC-MS) examinations in this pedigree further supported the cblC disease diagnosis. These results indicated that hypergonadotropic hypogonadism may be a novel clinical manifestation of cblC disease, but more reports on additional patients are needed to support this hypothesis. Copyright © 2015 Elsevier B.V. All rights reserved.

High-Throughput Cloning and Expression Library Creation for Functional Proteomics

PubMed Central

Festa, Fernanda; Steel, Jason; Bian, Xiaofang; Labaer, Joshua

2013-01-01

The study of protein function usually requires the use of a cloned version of the gene for protein expression and functional assays. This strategy is particular important when the information available regarding function is limited. The functional characterization of the thousands of newly identified proteins revealed by genomics requires faster methods than traditional single gene experiments, creating the need for fast, flexible and reliable cloning systems. These collections of open reading frame (ORF) clones can be coupled with high-throughput proteomics platforms, such as protein microarrays and cell-based assays, to answer biological questions. In this tutorial we provide the background for DNA cloning, discuss the major high-throughput cloning systems (Gateway® Technology, Flexi® Vector Systems, and Creator™ DNA Cloning System) and compare them side-by-side. We also report an example of high-throughput cloning study and its application in functional proteomics. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP12). Details can be found at http://www.proteomicstutorials.org. PMID:23457047

RIPiT-Seq: A high-throughput approach for footprinting RNA:protein complexes

PubMed Central

Singh, Guramrit; Ricci, Emiliano P.; Moore, Melissa J.

2013-01-01

Development of high-throughput approaches to map the RNA interaction sites of individual RNA binding proteins (RBPs) transcriptome-wide is rapidly transforming our understanding of post-transcriptional gene regulatory mechanisms. Here we describe a ribonucleoprotein (RNP) footprinting approach we recently developed for identifying occupancy sites of both individual RBPs and multi-subunit RNP complexes. RNA:protein immunoprecipitation in tandem (RIPiT) yields highly specific RNA footprints of cellular RNPs isolated via two sequential purifications; the resulting RNA footprints can then be identified by high-throughput sequencing (Seq). RIPiT-Seq is broadly applicable to all RBPs regardless of their RNA binding mode and thus provides a means to map the RNA binding sites of RBPs with poor inherent ultraviolet (UV) crosslinkability. Further, among current high-throughput approaches, RIPiT has the unique capacity to differentiate binding sites of RNPs with overlapping protein composition. It is therefore particularly suited for studying dynamic RNP assemblages whose composition evolves as gene expression proceeds. PMID:24096052

High-throughput cloning and expression library creation for functional proteomics.

PubMed

Festa, Fernanda; Steel, Jason; Bian, Xiaofang; Labaer, Joshua

2013-05-01

The study of protein function usually requires the use of a cloned version of the gene for protein expression and functional assays. This strategy is particularly important when the information available regarding function is limited. The functional characterization of the thousands of newly identified proteins revealed by genomics requires faster methods than traditional single-gene experiments, creating the need for fast, flexible, and reliable cloning systems. These collections of ORF clones can be coupled with high-throughput proteomics platforms, such as protein microarrays and cell-based assays, to answer biological questions. In this tutorial, we provide the background for DNA cloning, discuss the major high-throughput cloning systems (Gateway® Technology, Flexi® Vector Systems, and Creator(TM) DNA Cloning System) and compare them side-by-side. We also report an example of high-throughput cloning study and its application in functional proteomics. This tutorial is part of the International Proteomics Tutorial Programme (IPTP12). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The origin, global distribution, and functional impact of the human 8p23 inversion polymorphism.

PubMed

Salm, Maximilian P A; Horswell, Stuart D; Hutchison, Claire E; Speedy, Helen E; Yang, Xia; Liang, Liming; Schadt, Eric E; Cookson, William O; Wierzbicki, Anthony S; Naoumova, Rossi P; Shoulders, Carol C

2012-06-01

Genomic inversions are an increasingly recognized source of genetic variation. However, a lack of reliable high-throughput genotyping assays for these structures has precluded a full understanding of an inversion's phylogenetic, phenotypic, and population genetic properties. We characterize these properties for one of the largest polymorphic inversions in man (the ∼4.5-Mb 8p23.1 inversion), a structure that encompasses numerous signals of natural selection and disease association. We developed and validated a flexible bioinformatics tool that utilizes SNP data to enable accurate, high-throughput genotyping of the 8p23.1 inversion. This tool was applied retrospectively to diverse genome-wide data sets, revealing significant population stratification that largely follows a clinal "serial founder effect" distribution model. Phylogenetic analyses establish the inversion's ancestral origin within the Homo lineage, indicating that 8p23.1 inversion has occurred independently in the Pan lineage. The human inversion breakpoint was localized to an inverted pair of human endogenous retrovirus elements within the large, flanking low-copy repeats; experimental validation of this breakpoint confirmed these elements as the likely intermediary substrates that sponsored inversion formation. In five data sets, mRNA levels of disease-associated genes were robustly associated with inversion genotype. Moreover, a haplotype associated with systemic lupus erythematosus was restricted to the derived inversion state. We conclude that the 8p23.1 inversion is an evolutionarily dynamic structure that can now be accommodated into the understanding of human genetic and phenotypic diversity.

The origin, global distribution, and functional impact of the human 8p23 inversion polymorphism

PubMed Central

Salm, Maximilian P.A.; Horswell, Stuart D.; Hutchison, Claire E.; Speedy, Helen E.; Yang, Xia; Liang, Liming; Schadt, Eric E.; Cookson, William O.; Wierzbicki, Anthony S.; Naoumova, Rossi P.; Shoulders, Carol C.

2012-01-01

Genomic inversions are an increasingly recognized source of genetic variation. However, a lack of reliable high-throughput genotyping assays for these structures has precluded a full understanding of an inversion's phylogenetic, phenotypic, and population genetic properties. We characterize these properties for one of the largest polymorphic inversions in man (the ∼4.5-Mb 8p23.1 inversion), a structure that encompasses numerous signals of natural selection and disease association. We developed and validated a flexible bioinformatics tool that utilizes SNP data to enable accurate, high-throughput genotyping of the 8p23.1 inversion. This tool was applied retrospectively to diverse genome-wide data sets, revealing significant population stratification that largely follows a clinal “serial founder effect” distribution model. Phylogenetic analyses establish the inversion's ancestral origin within the Homo lineage, indicating that 8p23.1 inversion has occurred independently in the Pan lineage. The human inversion breakpoint was localized to an inverted pair of human endogenous retrovirus elements within the large, flanking low-copy repeats; experimental validation of this breakpoint confirmed these elements as the likely intermediary substrates that sponsored inversion formation. In five data sets, mRNA levels of disease-associated genes were robustly associated with inversion genotype. Moreover, a haplotype associated with systemic lupus erythematosus was restricted to the derived inversion state. We conclude that the 8p23.1 inversion is an evolutionarily dynamic structure that can now be accommodated into the understanding of human genetic and phenotypic diversity. PMID:22399572

Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution

PubMed Central

Downey, Mike J.; Jeziorska, Danuta M.; Ott, Sascha; Tamai, T. Katherine; Koentges, Georgy; Vance, Keith W.; Bretschneider, Till

2011-01-01

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers. PMID:22194797

High-Throughput, Motility-Based Sorter for Microswimmers and Gene Discovery Platform

NASA Astrophysics Data System (ADS)

Yuan, Jinzhou; Raizen, David; Bau, Haim

2015-11-01

Animal motility varies with genotype, disease progression, aging, and environmental conditions. In many studies, it is desirable to carry out high throughput motility-based sorting to isolate rare animals for, among other things, forward genetic screens to identify genetic pathways that regulate phenotypes of interest. Many commonly used screening processes are labor-intensive, lack sensitivity, and require extensive investigator training. Here, we describe a sensitive, high throughput, automated, motility-based method for sorting nematodes. Our method was implemented in a simple microfluidic device capable of sorting many thousands of animals per hour per module, and is amenable to parallelism. The device successfully enriched for known C. elegans motility mutants. Furthermore, using this device, we isolated low-abundance mutants capable of suppressing the somnogenic effects of the flp-13 gene, which regulates sleep-like quiescence in C. elegans. Subsequent genomic sequencing led to the identification of a flp-13-suppressor gene. This research was supported, in part, by NIH NIA Grant 5R03AG042690-02.

CAPN3, DCT, MLANA and TYRP1 are overexpressed in skin of vitiligo vulgaris Mexican patients.

PubMed

Salinas-Santander, Mauricio; Trevino, Víctor; De la Rosa-Moreno, Eduardo; Verduzco-Garza, Bárbara; Sánchez-Domínguez, Celia N; Cantú-Salinas, Cristina; Ocampo-Garza, Jorge; Lagos-Rodríguez, Armando; Ocampo-Candiani, Jorge; Ortiz-López, Rocio

2018-03-01

Vitiligo is a disorder causing skin depigmentation, in which several factors have been proposed for its pathogenesis: Environmental, genetic and biological aspects of melanocytes, even those of the surrounding keratinocytes. However, the lack of understanding of the mechanisms has complicated the task of predicting the development and progression. The present study used microarray analysis to characterize the transcriptional profile of skin from Vitiligo Vulgaris (VV) patients and the identified transcripts were validated using targeted high-throughput RNA sequencing in a broader set of patients. For microarrays, mRNA was taken from 20 skin biopsies of 10 patients with VV (pigmented and depigmented skin biopsy of each), and 5 biopsies of healthy subjects matched for age and sex were used as a control. A signature was identified that contains the expression pattern of 722 genes between depigmented vitiligo skin vs. healthy control, 1,108 between the pigmented skin of vitiligo vs. healthy controls and 1,927 between pigmented skin, depigmented vitiligo and healthy controls (P<0.05; false discovery rate, <0.1). When comparing the pigmented and depigmented skin of patients with vitiligo, which reflects the real difference between both skin types, 5 differentially expressed genes were identified and further validated in 45 additional VV patients by RNA sequencing. This analysis showed significantly higher RNA levels of calpain-3, dopachrome tautomerase, melan-A and tyrosinase-related protein-1 genes. The data revealed that the pigmented skin of vitiligo is already affected at the level of gene expression and that the main differences between pigmented and non-pigmented skin are explained by the expression of genes associated with pigment metabolism.

Monitoring the dynamics of syntrophic β-oxidizing bacteria during anaerobic degradation of oleic acid by quantitative PCR.

PubMed

Ziels, Ryan M; Beck, David A C; Martí, Magalí; Gough, Heidi L; Stensel, H David; Svensson, Bo H

2015-04-01

The ecophysiology of long-chain fatty acid-degrading syntrophic β-oxidizing bacteria has been poorly understood due to a lack of quantitative abundance data. Here, TaqMan quantitative PCR (qPCR) assays targeting the 16S rRNA gene of the known mesophilic syntrophic β-oxidizing bacterial genera Syntrophomonas and Syntrophus were developed and validated. Microbial community dynamics were followed using qPCR and Illumina-based high-throughput amplicon sequencing in triplicate methanogenic bioreactors subjected to five consecutive batch feedings of oleic acid. With repeated oleic acid feeding, the initial specific methane production rate significantly increased along with the relative abundances of Syntrophomonas and methanogenic archaea in the bioreactor communities. The novel qPCR assays showed that Syntrophomonas increased from 7 to 31% of the bacterial community 16S rRNA gene concentration, whereas that of Syntrophus decreased from 0.02 to less than 0.005%. High-throughput amplicon sequencing also revealed that Syntrophomonas became the dominant genus within the bioreactor microbiomes. These results suggest that increased specific mineralization rates of oleic acid were attributed to quantitative shifts within the microbial communities toward higher abundances of syntrophic β-oxidizing bacteria and methanogenic archaea. The novel qPCR assays targeting syntrophic β-oxidizing bacteria may thus serve as monitoring tools to indicate the fatty acid β-oxidization potential of anaerobic digester communities. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer

PubMed Central

Speers, Corey; Liu, Meilan; Wilder-Romans, Kari; Lawrence, Theodore S.; Pierce, Lori J.; Feng, Felix Y.

2015-01-01

Purpose The molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer. Experimental design In vivo screening for metastases was performed using Chick Chorioallantoic Membrane assays in 21 preclinical breast cancer models. Expressed genes associated with metastatic potential were identified using high-throughput analysis. Correlations with biological function were determined using the Database for Annotation, Visualization and Integrated Discovery. Results We identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts. Conclusion M-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets. PMID:25974184

Environmental surveillance and monitoring. The next frontiers for high-throughput toxicology

EPA Science Inventory

High throughput toxicity testing (HTT) technologies along with the world-wide web are revolutionizing both generation and access to data regarding the bioactivities that chemicals can elicit when they interact with specific proteins, genes, or other targets in the body of an orga...

Developing a gene biomarker at the tipping point of adaptive and adverse responses in human bronchial epithelial cells

EPA Science Inventory

Determining mechanism-based biomarkers that distinguish adaptive and adverse cellular processes is critical to understanding the health effects of environmental exposures. Shifting from in vivo, low-throughput toxicity studies to high-throughput screening (HTS) paradigms and risk...

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Integrative analyses shed new light on human ribosomal protein gene regulation

PubMed Central

Li, Xin; Zheng, Yiyu; Hu, Haiyan; Li, Xiaoman

2016-01-01

Ribosomal protein genes (RPGs) are important house-keeping genes that are well-known for their coordinated expression. Previous studies on RPGs are largely limited to their promoter regions. Recent high-throughput studies provide an unprecedented opportunity to study how human RPGs are transcriptionally modulated and how such transcriptional regulation may contribute to the coordinate gene expression in various tissues and cell types. By analyzing the DNase I hypersensitive sites under 349 experimental conditions, we predicted 217 RPG regulatory regions in the human genome. More than 86.6% of these computationally predicted regulatory regions were partially corroborated by independent experimental measurements. Motif analyses on these predicted regulatory regions identified 31 DNA motifs, including 57.1% of experimentally validated motifs in literature that regulate RPGs. Interestingly, we observed that the majority of the predicted motifs were shared by the predicted distal and proximal regulatory regions of the same RPGs, a likely general mechanism for enhancer-promoter interactions. We also found that RPGs may be differently regulated in different cells, indicating that condition-specific RPG regulatory regions still need to be discovered and investigated. Our study advances the understanding of how RPGs are coordinately modulated, which sheds light to the general principles of gene transcriptional regulation in mammals. PMID:27346035

Integrative analyses shed new light on human ribosomal protein gene regulation.

PubMed

Li, Xin; Zheng, Yiyu; Hu, Haiyan; Li, Xiaoman

2016-06-27

Ribosomal protein genes (RPGs) are important house-keeping genes that are well-known for their coordinated expression. Previous studies on RPGs are largely limited to their promoter regions. Recent high-throughput studies provide an unprecedented opportunity to study how human RPGs are transcriptionally modulated and how such transcriptional regulation may contribute to the coordinate gene expression in various tissues and cell types. By analyzing the DNase I hypersensitive sites under 349 experimental conditions, we predicted 217 RPG regulatory regions in the human genome. More than 86.6% of these computationally predicted regulatory regions were partially corroborated by independent experimental measurements. Motif analyses on these predicted regulatory regions identified 31 DNA motifs, including 57.1% of experimentally validated motifs in literature that regulate RPGs. Interestingly, we observed that the majority of the predicted motifs were shared by the predicted distal and proximal regulatory regions of the same RPGs, a likely general mechanism for enhancer-promoter interactions. We also found that RPGs may be differently regulated in different cells, indicating that condition-specific RPG regulatory regions still need to be discovered and investigated. Our study advances the understanding of how RPGs are coordinately modulated, which sheds light to the general principles of gene transcriptional regulation in mammals.

Identification of functional modules using network topology and high-throughput data.

PubMed

Ulitsky, Igor; Shamir, Ron

2007-01-26

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

RNAi High-Throughput Screening of Single- and Multi-Cell-Type Tumor Spheroids: A Comprehensive Analysis in Two and Three Dimensions.

PubMed

Fu, Jiaqi; Fernandez, Daniel; Ferrer, Marc; Titus, Steven A; Buehler, Eugen; Lal-Nag, Madhu A

2017-06-01

The widespread use of two-dimensional (2D) monolayer cultures for high-throughput screening (HTS) to identify targets in drug discovery has led to attrition in the number of drug targets being validated. Solid tumors are complex, aberrantly growing microenvironments that harness structural components from stroma, nutrients fed through vasculature, and immunosuppressive factors. Increasing evidence of stromally-derived signaling broadens the complexity of our understanding of the tumor microenvironment while stressing the importance of developing better models that reflect these interactions. Three-dimensional (3D) models may be more sensitive to certain gene-silencing events than 2D models because of their components of hypoxia, nutrient gradients, and increased dependence on cell-cell interactions and therefore are more representative of in vivo interactions. Colorectal cancer (CRC) and breast cancer (BC) models composed of epithelial cells only, deemed single-cell-type tumor spheroids (SCTS) and multi-cell-type tumor spheroids (MCTS), containing fibroblasts were developed for RNAi HTS in 384-well microplates with flat-bottom wells for 2D screening and round-bottom, ultra-low-attachment wells for 3D screening. We describe the development of a high-throughput assay platform that can assess physiologically relevant phenotypic differences between screening 2D versus 3D SCTS, 3D SCTS, and MCTS in the context of different cancer subtypes. This assay platform represents a paradigm shift in how we approach drug discovery that can reduce the attrition rate of drugs that enter the clinic.

Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms

PubMed Central

Pacheco, Maria P.; Pfau, Thomas; Sauter, Thomas

2016-01-01

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms. PMID:26834640

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.

PubMed

Subhash, Santhilal; Kanduri, Chandrasekhar

2016-09-13

High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

Diversity of the TLR4 Immunity Receptor in Czech Native Cattle Breeds Revealed Using the Pacific Biosciences Sequencing Platform.

PubMed

Novák, Karel; Pikousová, Jitka; Czerneková, Vladimíra; Mátlová, Věra

2017-07-03

The allelic variants of immunity genes in historical breeds likely reflect local infection pressure and therefore represent a reservoir for breeding. Screening to determine the diversity of the Toll-like receptor gene TLR4 was conducted in two conserved cattle breeds: Czech Red and Czech Red Pied. High-throughput sequencing of pooled PCR amplicons using the PacBio platform revealed polymorphisms, which were subsequently confirmed via genotyping techniques. Eight SNPs found in coding and adjacent regions were grouped into 18 haplotypes, representing a significant portion of the known diversity in the global breed panel and presumably exceeding diversity in production populations. Notably, the ancient Czech Red breed appeared to possess greater haplotype diversity than the Czech Red Pied breed, a Simmental variant, although the haplotype frequencies might have been distorted by significant crossbreeding and bottlenecks in the history of Czech Red cattle. The differences in haplotype frequencies validated the phenotypic distinctness of the local breeds. Due to the availability of Czech Red Pied production herds, the effect of intensive breeding on TLR diversity can be evaluated in this model. The advantages of the Pacific Biosciences technology for the resequencing of long PCR fragments with subsequent direct phasing were independently validated.

Hybridization-based antibody cDNA recovery for the production of recombinant antibodies identified by repertoire sequencing.

PubMed

Valdés-Alemán, Javier; Téllez-Sosa, Juan; Ovilla-Muñoz, Marbella; Godoy-Lozano, Elizabeth; Velázquez-Ramírez, Daniel; Valdovinos-Torres, Humberto; Gómez-Barreto, Rosa E; Martinez-Barnetche, Jesús

2014-01-01

High-throughput sequencing of the antibody repertoire is enabling a thorough analysis of B cell diversity and clonal selection, which may improve the novel antibody discovery process. Theoretically, an adequate bioinformatic analysis could allow identification of candidate antigen-specific antibodies, requiring their recombinant production for experimental validation of their specificity. Gene synthesis is commonly used for the generation of recombinant antibodies identified in silico. Novel strategies that bypass gene synthesis could offer more accessible antibody identification and validation alternatives. We developed a hybridization-based recovery strategy that targets the complementarity-determining region 3 (CDRH3) for the enrichment of cDNA of candidate antigen-specific antibody sequences. Ten clonal groups of interest were identified through bioinformatic analysis of the heavy chain antibody repertoire of mice immunized with hen egg white lysozyme (HEL). cDNA from eight of the targeted clonal groups was recovered efficiently, leading to the generation of recombinant antibodies. One representative heavy chain sequence from each clonal group recovered was paired with previously reported anti-HEL light chains to generate full antibodies, later tested for HEL-binding capacity. The recovery process proposed represents a simple and scalable molecular strategy that could enhance antibody identification and specificity assessment, enabling a more cost-efficient generation of recombinant antibodies.

Creation of knock out and knock in mice by CRISPR/Cas9 to validate candidate genes for human male infertility, interest, difficulties and feasibility.

PubMed

Kherraf, Zine-Eddine; Conne, Beatrice; Amiri-Yekta, Amir; Kent, Marie Christou; Coutton, Charles; Escoffier, Jessica; Nef, Serge; Arnoult, Christophe; Ray, Pierre F

2018-06-15

High throughput sequencing (HTS) and CRISPR/Cas9 are two recent technologies that are currently revolutionizing biological and clinical research. Both techniques are complementary as HTS permits to identify new genetic variants and genes involved in various pathologies and CRISPR/Cas9 permits to create animals or cell models to validate the effect of the identified variants, to characterize the pathogeny of the identified variants and the function of the genes of interest and ultimately to provide ways of correcting the molecular defects. We analyzed a cohort of 78 infertile men presenting with multiple morphological anomalies of the sperm flagella (MMAF), a severe form of male infertility. Using whole exome sequencing (WES), homozygous mutations in autosomal candidate genes were identified in 63% of the tested subjects. We decided to produce by CRISPR/cas9 four knock-out (KO) and one knock-in (KI) mouse lines to confirm these results and to increase our understanding of the physiopathology associated with these genetic variations. Overall 31% of the live pups obtained presented a mutational event in one of the targeted regions. All identified events were insertions or deletions localized near the PAM sequence. Surprisingly we observed a high rate of germline mosaicism as 30% of the F1 displayed a different mutation than the parental event characterized on somatic tissue (tail), indicating that CRISPR/Cas9 mutational events kept happening several cell divisions after the injection. Overall, we created mouse models for 5 distinct loci and in each case homozygous animals could be obtained in approximately 6 months. These results demonstrate that the combined use of WES and CRISPR/Cas9 is an efficient and timely strategy to identify and validate mutations responsible for infertility phenotypes in human. Copyright © 2018 Elsevier B.V. All rights reserved.

The 'PUCE CAFE' Project: the First 15K Coffee Microarray, a New Tool for Discovering Candidate Genes correlated to Agronomic and Quality Traits

PubMed Central

2011-01-01

Background Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. Results The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. Conclusion We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Génoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research. PMID:21208403

The 'PUCE CAFE' Project: the first 15K coffee microarray, a new tool for discovering candidate genes correlated to agronomic and quality traits.

PubMed

Privat, Isabelle; Bardil, Amélie; Gomez, Aureliano Bombarely; Severac, Dany; Dantec, Christelle; Fuentes, Ivanna; Mueller, Lukas; Joët, Thierry; Pot, David; Foucrier, Séverine; Dussert, Stéphane; Leroy, Thierry; Journot, Laurent; de Kochko, Alexandre; Campa, Claudine; Combes, Marie-Christine; Lashermes, Philippe; Bertrand, Benoit

2011-01-05

Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Génoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research.

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Development and validation of reagents and assays for EZH2 peptide and nucleosome high-throughput screens.

PubMed

Diaz, Elsie; Machutta, Carl A; Chen, Stephanie; Jiang, Yong; Nixon, Christopher; Hofmann, Glenn; Key, Danielle; Sweitzer, Sharon; Patel, Mehul; Wu, Zining; Creasy, Caretha L; Kruger, Ryan G; LaFrance, Louis; Verma, Sharad K; Pappalardi, Melissa B; Le, Baochau; Van Aller, Glenn S; McCabe, Michael T; Tummino, Peter J; Pope, Andrew J; Thrall, Sara H; Schwartz, Benjamin; Brandt, Martin

2012-12-01

Histone methyltransferases (HMT) catalyze the methylation of histone tail lysines, resulting in changes in gene transcription. Misregulation of these enzymes has been associated with various forms of cancer, making this target class a potential new area for the development of novel chemotherapeutics. EZH2 is the catalytic component of the polycomb group repressive complex (PRC2), which selectively methylates histone H3 lysine 27 (H3K27). EZH2 is overexpressed in prostate, breast, bladder, brain, and other tumor types and is recognized as a molecular marker for cancer progression and aggressiveness. Several new reagents and assays were developed to aid in the identification of EZH2 inhibitors, and these were used to execute two high-throughput screening campaigns. Activity assays using either an H3K27 peptide or nucleosomes as substrates for methylation are described. The strategy to screen EZH2 with either a surrogate peptide or a natural substrate led to the identification of the same tractable series. Compounds from this series are reversible, are [(3)H]-S-adenosyl-L-methionine competitive, and display biochemical inhibition of H3K27 methylation.

Comprehensive Ex Vivo Transposon Mutagenesis Identifies Genes That Promote Growth Factor Independence and Leukemogenesis.

PubMed

Guo, Yabin; Updegraff, Barrett L; Park, Sunho; Durakoglugil, Deniz; Cruz, Victoria H; Maddux, Sarah; Hwang, Tae Hyun; O'Donnell, Kathryn A

2016-02-15

Aberrant signaling through cytokine receptors and their downstream signaling pathways is a major oncogenic mechanism underlying hematopoietic malignancies. To better understand how these pathways become pathologically activated and to potentially identify new drivers of hematopoietic cancers, we developed a high-throughput functional screening approach using ex vivo mutagenesis with the Sleeping Beauty transposon. We analyzed over 1,100 transposon-mutagenized pools of Ba/F3 cells, an IL3-dependent pro-B-cell line, which acquired cytokine independence and tumor-forming ability. Recurrent transposon insertions could be mapped to genes in the JAK/STAT and MAPK pathways, confirming the ability of this strategy to identify known oncogenic components of cytokine signaling pathways. In addition, recurrent insertions were identified in a large set of genes that have been found to be mutated in leukemia or associated with survival, but were not previously linked to the JAK/STAT or MAPK pathways nor shown to functionally contribute to leukemogenesis. Forced expression of these novel genes resulted in IL3-independent growth in vitro and tumorigenesis in vivo, validating this mutagenesis-based approach for identifying new genes that promote cytokine signaling and leukemogenesis. Therefore, our findings provide a broadly applicable approach for classifying functionally relevant genes in diverse malignancies and offer new insights into the impact of cytokine signaling on leukemia development. ©2015 American Association for Cancer Research.

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours

PubMed Central

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-01-01

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes (CPQ, PLVAP, TFF3, ACVRL1, ZFYVE21, FAM189A2, and CLEC3B). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes (CPQ, PLVAP, TFF3, ACVRL1). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material. PMID:28574441

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours.

PubMed

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-06-02

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes ( CPQ , PLVAP , TFF3 , ACVRL1 , ZFYVE21 , FAM189A2 , and CLEC3B ). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes ( CPQ , PLVAP , TFF3 , ACVRL1 ). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material.

Multiple-endpoints gene alteration-based (MEGA) assay: A toxicogenomics approach for water quality assessment of wastewater effluents.

PubMed

Fukushima, Toshikazu; Hara-Yamamura, Hiroe; Nakashima, Koji; Tan, Lea Chua; Okabe, Satoshi

2017-12-01

Wastewater effluents contain a significant number of toxic contaminants, which, even at low concentrations, display a wide variety of toxic actions. In this study, we developed a multiple-endpoints gene alteration-based (MEGA) assay, a real-time PCR-based transcriptomic analysis, to assess the water quality of wastewater effluents for human health risk assessment and management. Twenty-one genes from the human hepatoblastoma cell line (HepG2), covering the basic health-relevant stress responses such as response to xenobiotics, genotoxicity, and cytotoxicity, were selected and incorporated into the MEGA assay. The genes related to the p53-mediated DNA damage response and cytochrome P450 were selected as markers for genotoxicity and response to xenobiotics, respectively. Additionally, the genes that were dose-dependently regulated by exposure to the wastewater effluents were chosen as markers for cytotoxicity. The alterations in the expression of an individual gene, induced by exposure to the wastewater effluents, were evaluated by real-time PCR and the results were validated by genotoxicity (e.g., comet assay) and cell-based cytotoxicity tests. In summary, the MEGA assay is a real-time PCR-based assay that targets cellular responses to contaminants present in wastewater effluents at the transcriptional level; it is rapid, cost-effective, and high-throughput and can thus complement any chemical analysis for water quality assessment and management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Molecular profiling of multiple myeloma: from gene expression analysis to next-generation sequencing.

PubMed

Agnelli, Luca; Tassone, Pierfrancesco; Neri, Antonino

2013-06-01

Multiple myeloma is a fatal malignant proliferation of clonal bone marrow Ig-secreting plasma cells, characterized by wide clinical, biological, and molecular heterogeneity. Herein, global gene and microRNA expression, genome-wide DNA profilings, and next-generation sequencing technology used to investigate the genomic alterations underlying the bio-clinical heterogeneity in multiple myeloma are discussed. High-throughput technologies have undoubtedly allowed a better comprehension of the molecular basis of the disease, a fine stratification, and early identification of high-risk patients, and have provided insights toward targeted therapy studies. However, such technologies are at risk of being affected by laboratory- or cohort-specific biases, and are moreover influenced by high number of expected false positives. This aspect has a major weight in myeloma, which is characterized by large molecular heterogeneity. Therefore, meta-analysis as well as multiple approaches are desirable if not mandatory to validate the results obtained, in line with commonly accepted recommendation for tumor diagnostic/prognostic biomarker studies.

Generation of a transgenic ORFeome library in Drosophila

PubMed Central

Bischof, Johannes; Sheils, Emma M.; Björklund, Mikael; Basler, Konrad

2014-01-01

Overexpression screens can be used to explore gene function in Drosophila melanogaster, but to demonstrate their full potential comprehensive and systematic collections of fly strains are required. Here we provide a protocol for high-throughput cloning of Drosophila open reading frames (ORFs) regulated by Upstream Activation Sequences (UAS sites); the resulting Gal4-inducible UAS-ORF plasmid library is then used to generate Drosophila strains by ΦC31 integrase-mediated site-specific integration. We also provide details for FLP/FRT-mediated in vivo exchange of epitope tags (or regulatory regions) in the ORF library strains, which further extends their potential applications. These transgenic UAS-ORF strains are a useful resource to complement and validate genetic experiments performed with loss-of-function mutants and RNAi lines. The duration of the complete protocol strongly depends on the number of ORFs required, but the procedure of injection and establishing balanced fly stocks can be completed within approx. 6-7 weeks for a few genes. PMID:24922270

Development of a Rapid Fluorescence-Based High-Throughput Screening Assay to Identify Novel Kynurenine 3-Monooxygenase Inhibitor Scaffolds.

PubMed

Jacobs, K R; Guillemin, G J; Lovejoy, D B

2018-02-01

Kynurenine 3-monooxygenase (KMO) is a well-validated therapeutic target for the treatment of neurodegenerative diseases, including Alzheimer's disease (AD) and Huntington's disease (HD). This work reports a facile fluorescence-based KMO assay optimized for high-throughput screening (HTS) that achieves a throughput approximately 20-fold higher than the fastest KMO assay currently reported. The screen was run with excellent performance (average Z' value of 0.80) from 110,000 compounds across 341 plates and exceeded all statistical parameters used to describe a robust HTS assay. A subset of molecules was selected for validation by ultra-high-performance liquid chromatography, resulting in the confirmation of a novel hit with an IC 50 comparable to that of the well-described KMO inhibitor Ro-61-8048. A medicinal chemistry program is currently underway to further develop our novel KMO inhibitor scaffolds.

Genome-wide characterization of differentially expressed genes provides insights into regulatory network of heat stress response in radish (Raphanus sativus L.).

PubMed

Wang, Ronghua; Mei, Yi; Xu, Liang; Zhu, Xianwen; Wang, Yan; Guo, Jun; Liu, Liwang

2018-03-01

Heat stress (HS) causes detrimental effects on plant morphology, physiology, and biochemistry that lead to drastic reduction in plant biomass production and economic yield worldwide. To date, little is known about HS-responsive genes involved in thermotolerance mechanism in radish. In this study, a total of 6600 differentially expressed genes (DEGs) from the control and Heat24 cDNA libraries of radish were isolated by high-throughput sequencing. With Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, some genes including MAPK, DREB, ERF, AP2, GST, Hsf, and Hsp were predominantly assigned in signal transductions, metabolic pathways, and biosynthesis and abiotic stress-responsive pathways. These pathways played significant roles in reducing stress-induced damages and enhancing heat tolerance in radish. Expression patterns of 24 candidate genes were validated by reverse-transcription quantitative PCR (RT-qPCR). Based mainly on the analysis of DEGs combining with the previous miRNAs analysis, the schematic model of HS-responsive regulatory network was proposed. To counter the effects of HS, a rapid response of the plasma membrane leads to the opening of specific calcium channels and cytoskeletal reorganization, after which HS-responsive genes are activated to repair damaged proteins and ultimately facilitate further enhancement of thermotolerance in radish. These results could provide fundamental insight into the regulatory network underlying heat tolerance in radish and facilitate further genetic manipulation of thermotolerance in root vegetable crops.

Inference of the oxidative stress network in Anopheles stephensi upon Plasmodium infection.

PubMed

Shrinet, Jatin; Nandal, Umesh Kumar; Adak, Tridibes; Bhatnagar, Raj K; Sunil, Sujatha

2014-01-01

Ookinete invasion of Anopheles midgut is a critical step for malaria transmission; the parasite numbers drop drastically and practically reach a minimum during the parasite's whole life cycle. At this stage, the parasite as well as the vector undergoes immense oxidative stress. Thereafter, the vector undergoes oxidative stress at different time points as the parasite invades its tissues during the parasite development. The present study was undertaken to reconstruct the network of differentially expressed genes involved in oxidative stress in Anopheles stephensi during Plasmodium development and maturation in the midgut. Using high throughput next generation sequencing methods, we generated the transcriptome of the An. stephensi midgut during Plasmodium vinckei petteri oocyst invasion of the midgut epithelium. Further, we utilized large datasets available on public domain on Anopheles during Plasmodium ookinete invasion and Drosophila datasets and arrived upon clusters of genes that may play a role in oxidative stress. Finally, we used support vector machines for the functional prediction of the un-annotated genes of An. stephensi. Integrating the results from all the different data analyses, we identified a total of 516 genes that were involved in oxidative stress in An. stephensi during Plasmodium development. The significantly regulated genes were further extracted from this gene cluster and used to infer an oxidative stress network of An. stephensi. Using system biology approaches, we have been able to ascertain the role of several putative genes in An. stephensi with respect to oxidative stress. Further experimental validations of these genes are underway.

Integrated analysis of gene expression and methylation profiles of 48 candidate genes in breast cancer patients.

PubMed

Li, Zibo; Heng, Jianfu; Yan, Jinhua; Guo, Xinwu; Tang, Lili; Chen, Ming; Peng, Limin; Wu, Yepeng; Wang, Shouman; Xiao, Zhi; Deng, Zhongping; Dai, Lizhong; Wang, Jun

2016-11-01

Gene-specific methylation and expression have shown biological and clinical importance for breast cancer diagnosis and prognosis. Integrated analysis of gene methylation and gene expression may identify genes associated with biology mechanism and clinical outcome of breast cancer and aid in clinical management. Using high-throughput microfluidic quantitative PCR, we analyzed the expression profiles of 48 candidate genes in 96 Chinese breast cancer patients and investigated their correlation with gene methylation and associations with breast cancer clinical parameters. Breast cancer-specific gene expression alternation was found in 25 genes with significant expression difference between paired tumor and normal tissues. A total of 9 genes (CCND2, EGFR, GSTP1, PGR, PTGS2, RECK, SOX17, TNFRSF10D, and WIF1) showed significant negative correlation between methylation and gene expression, which were validated in the TCGA database. Total 23 genes (ACADL, APC, BRCA2, CADM1, CAV1, CCND2, CST6, EGFR, ESR2, GSTP1, ICAM5, NPY, PGR, PTGS2, RECK, RUNX3, SFRP1, SOX17, SYK, TGFBR2, TNFRSF10D, WIF1, and WRN) annotated with potential TFBSs in the promoter regions showed negative correlation between methylation and expression. In logistics regression analysis, 31 of the 48 genes showed improved performance in disease prediction with combination of methylation and expression coefficient. Our results demonstrated the complex correlation and the possible regulatory mechanisms between DNA methylation and gene expression. Integration analysis of methylation and expression of candidate genes could improve performance in breast cancer prediction. These findings would contribute to molecular characterization and identification of biomarkers for potential clinical applications.

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.

Higher Throughput Calorimetry: Opportunities, Approaches and Challenges

PubMed Central

Recht, Michael I.; Coyle, Joseph E.; Bruce, Richard H.

2010-01-01

Higher throughput thermodynamic measurements can provide value in structure-based drug discovery during fragment screening, hit validation, and lead optimization. Enthalpy can be used to detect and characterize ligand binding, and changes that affect the interaction of protein and ligand can sometimes be detected more readily from changes in the enthalpy of binding than from the corresponding free-energy changes or from protein-ligand structures. Newer, higher throughput calorimeters are being incorporated into the drug discovery process. Improvements in titration calorimeters come from extensions of a mature technology and face limitations in scaling. Conversely, array calorimetry, an emerging technology, shows promise for substantial improvements in throughput and material utilization, but improved sensitivity is needed. PMID:20888754

Biomarker discovery for colon cancer using a 761 gene RT-PCR assay.

PubMed

Clark-Langone, Kim M; Wu, Jenny Y; Sangli, Chithra; Chen, Angela; Snable, James L; Nguyen, Anhthu; Hackett, James R; Baker, Joffre; Yothers, Greg; Kim, Chungyeul; Cronin, Maureen T

2007-08-15

Reverse transcription PCR (RT-PCR) is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE) clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis) and the likelihood of tumor response to standard chemotherapy regimens (prediction). We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application. RNA was extracted from formalin fixed paraffin embedded (FPE) tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery. We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR. Biomarkers discovered using this approach can be transferred to a clinical reference laboratory setting without having to re-validate the assay on a second technology platform.

CrossCheck: an open-source web tool for high-throughput screen data analysis.

PubMed

Najafov, Jamil; Najafov, Ayaz

2017-07-19

Modern high-throughput screening methods allow researchers to generate large datasets that potentially contain important biological information. However, oftentimes, picking relevant hits from such screens and generating testable hypotheses requires training in bioinformatics and the skills to efficiently perform database mining. There are currently no tools available to general public that allow users to cross-reference their screen datasets with published screen datasets. To this end, we developed CrossCheck, an online platform for high-throughput screen data analysis. CrossCheck is a centralized database that allows effortless comparison of the user-entered list of gene symbols with 16,231 published datasets. These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information. Moreover, CrossCheck includes a novel database of predicted protein kinase substrates, which was developed using proteome-wide consensus motif searches. CrossCheck dramatically simplifies high-throughput screen data analysis and enables researchers to dig deep into the published literature and streamline data-driven hypothesis generation. CrossCheck is freely accessible as a web-based application at http://proteinguru.com/crosscheck.

High-throughput transformation of Saccharomyces cerevisiae using liquid handling robots.

PubMed

Liu, Guangbo; Lanham, Clayton; Buchan, J Ross; Kaplan, Matthew E

2017-01-01

Saccharomyces cerevisiae (budding yeast) is a powerful eukaryotic model organism ideally suited to high-throughput genetic analyses, which time and again has yielded insights that further our understanding of cell biology processes conserved in humans. Lithium Acetate (LiAc) transformation of yeast with DNA for the purposes of exogenous protein expression (e.g., plasmids) or genome mutation (e.g., gene mutation, deletion, epitope tagging) is a useful and long established method. However, a reliable and optimized high throughput transformation protocol that runs almost no risk of human error has not been described in the literature. Here, we describe such a method that is broadly transferable to most liquid handling high-throughput robotic platforms, which are now commonplace in academic and industry settings. Using our optimized method, we are able to comfortably transform approximately 1200 individual strains per day, allowing complete transformation of typical genomic yeast libraries within 6 days. In addition, use of our protocol for gene knockout purposes also provides a potentially quicker, easier and more cost-effective approach to generating collections of double mutants than the popular and elegant synthetic genetic array methodology. In summary, our methodology will be of significant use to anyone interested in high throughput molecular and/or genetic analysis of yeast.

ToxCast Assay Network (TCAN) Viewer: A Visualization Tool for High-throughput Assay Chemical Data (SOT)

EPA Science Inventory

USEPA’s ToxCast program has generated high-throughput bioactivity screening (HTS) data on thousands of chemicals. The ToxCast program has described and annotated the HTS assay battery with respect to assay design and target information (e.g., gene target). Recent stakeholder and ...

Microfluidic guillotine for single-cell wound repair studies

NASA Astrophysics Data System (ADS)

Blauch, Lucas R.; Gai, Ya; Khor, Jian Wei; Sood, Pranidhi; Marshall, Wallace F.; Tang, Sindy K. Y.

2017-07-01

Wound repair is a key feature distinguishing living from nonliving matter. Single cells are increasingly recognized to be capable of healing wounds. The lack of reproducible, high-throughput wounding methods has hindered single-cell wound repair studies. This work describes a microfluidic guillotine for bisecting single Stentor coeruleus cells in a continuous-flow manner. Stentor is used as a model due to its robust repair capacity and the ability to perform gene knockdown in a high-throughput manner. Local cutting dynamics reveals two regimes under which cells are bisected, one at low viscous stress where cells are cut with small membrane ruptures and high viability and one at high viscous stress where cells are cut with extended membrane ruptures and decreased viability. A cutting throughput up to 64 cells per minute—more than 200 times faster than current methods—is achieved. The method allows the generation of more than 100 cells in a synchronized stage of their repair process. This capacity, combined with high-throughput gene knockdown in Stentor, enables time-course mechanistic studies impossible with current wounding methods.

Perspectives on Validation of High-Throughput Assays Supporting 21st Century Toxicity Testing1

PubMed Central

Judson, Richard; Kavlock, Robert; Martin, Matt; Reif, David; Houck, Keith; Knudsen, Thomas; Richard, Ann; Tice, Raymond R.; Whelan, Maurice; Xia, Menghang; Huang, Ruili; Austin, Christopher; Daston, George; Hartung, Thomas; Fowle, John R.; Wooge, William; Tong, Weida; Dix, David

2014-01-01

Summary In vitro, high-throughput screening (HTS) assays are seeing increasing use in toxicity testing. HTS assays can simultaneously test many chemicals, but have seen limited use in the regulatory arena, in part because of the need to undergo rigorous, time-consuming formal validation. Here we discuss streamlining the validation process, specifically for prioritization applications in which HTS assays are used to identify a high-concern subset of a collection of chemicals. The high-concern chemicals could then be tested sooner rather than later in standard guideline bioassays. The streamlined validation process would continue to ensure the reliability and relevance of assays for this application. We discuss the following practical guidelines: (1) follow current validation practice to the extent possible and practical; (2) make increased use of reference compounds to better demonstrate assay reliability and relevance; (3) deemphasize the need for cross-laboratory testing, and; (4) implement a web-based, transparent and expedited peer review process. PMID:23338806

HybPiper: Extracting coding sequence and introns for phylogenetics from high-throughput sequencing reads using target enrichment1

PubMed Central

Johnson, Matthew G.; Gardner, Elliot M.; Liu, Yang; Medina, Rafael; Goffinet, Bernard; Shaw, A. Jonathan; Zerega, Nyree J. C.; Wickett, Norman J.

2016-01-01

Premise of the study: Using sequence data generated via target enrichment for phylogenetics requires reassembly of high-throughput sequence reads into loci, presenting a number of bioinformatics challenges. We developed HybPiper as a user-friendly platform for assembly of gene regions, extraction of exon and intron sequences, and identification of paralogous gene copies. We test HybPiper using baits designed to target 333 phylogenetic markers and 125 genes of functional significance in Artocarpus (Moraceae). Methods and Results: HybPiper implements parallel execution of sequence assembly in three phases: read mapping, contig assembly, and target sequence extraction. The pipeline was able to recover nearly complete gene sequences for all genes in 22 species of Artocarpus. HybPiper also recovered more than 500 bp of nontargeted intron sequence in over half of the phylogenetic markers and identified paralogous gene copies in Artocarpus. Conclusions: HybPiper was designed for Linux and Mac OS X and is freely available at https://github.com/mossmatters/HybPiper. PMID:27437175

Abundance and Diversity of Bacterial Nitrifiers and Denitrifiers and Their Functional Genes in Tannery Wastewater Treatment Plants Revealed by High-Throughput Sequencing

PubMed Central

Wang, Zhu; Zhang, Xu-Xiang; Lu, Xin; Liu, Bo; Li, Yan; Long, Chao; Li, Aimin

2014-01-01

Biological nitrification/denitrification is frequently used to remove nitrogen from tannery wastewater containing high concentrations of ammonia. However, information is limited about the bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants (WWTPs) due to the low-throughput of the previously used methods. In this study, 454 pyrosequencing and Illumina high-throughput sequencing, combined with molecular methods, were used to comprehensively characterize structures and functions of nitrification and denitrification bacterial communities in aerobic and anaerobic sludge of two full-scale tannery WWTPs. Pyrosequencing of 16S rRNA genes showed that Proteobacteria and Synergistetes dominated in the aerobic and anaerobic sludge, respectively. Ammonia-oxidizing bacteria (AOB) amoA gene cloning revealed that Nitrosomonas europaea dominated the ammonia-oxidizing community in the WWTPs. Metagenomic analysis showed that the denitrifiers mainly included the genera of Thauera, Paracoccus, Hyphomicrobium, Comamonas and Azoarcus, which may greatly contribute to the nitrogen removal in the two WWTPs. It is interesting that AOB and ammonia-oxidizing archaea had low abundance although both WWTPs demonstrated high ammonium removal efficiency. Good correlation between the qPCR and metagenomic analysis is observed for the quantification of functional genes amoA, nirK, nirS and nosZ, indicating that the metagenomic approach may be a promising method used to comprehensively investigate the abundance of functional genes of nitrifiers and denitrifiers in the environment. PMID:25420093

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

PubMed

Gold, David L; Coombes, Kevin R; Wang, Jing; Mallick, Bani

2007-03-01

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

High-throughput cell-based screening reveals a role for ZNF131 as a repressor of ERalpha signaling

PubMed Central

Han, Xiao; Guo, Jinhai; Deng, Weiwei; Zhang, Chenying; Du, Peige; Shi, Taiping; Ma, Dalong

2008-01-01

Background Estrogen receptor α (ERα) is a transcription factor whose activity is affected by multiple regulatory cofactors. In an effort to identify the human genes involved in the regulation of ERα, we constructed a high-throughput, cell-based, functional screening platform by linking a response element (ERE) with a reporter gene. This allowed the cellular activity of ERα, in cells cotransfected with the candidate gene, to be quantified in the presence or absence of its cognate ligand E2. Results From a library of 570 human cDNA clones, we identified zinc finger protein 131 (ZNF131) as a repressor of ERα mediated transactivation. ZNF131 is a typical member of the BTB/POZ family of transcription factors, and shows both ubiquitous expression and a high degree of sequence conservation. The luciferase reporter gene assay revealed that ZNF131 inhibits ligand-dependent transactivation by ERα in a dose-dependent manner. Electrophoretic mobility shift assay clearly demonstrated that the interaction between ZNF131 and ERα interrupts or prevents ERα binding to the estrogen response element (ERE). In addition, ZNF131 was able to suppress the expression of pS2, an ERα target gene. Conclusion We suggest that the functional screening platform we constructed can be applied for high-throughput genomic screening candidate ERα-related genes. This in turn may provide new insights into the underlying molecular mechanisms of ERα regulation in mammalian cells. PMID:18847501

SpliceCenter: A suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies

PubMed Central

Ryan, Michael C; Zeeberg, Barry R; Caplen, Natasha J; Cleland, James A; Kahn, Ari B; Liu, Hongfang; Weinstein, John N

2008-01-01

Background Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant. Results A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript. Conclusion SpliceCenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists. PMID:18638396

Generalized schemes for high throughput manipulation of the Desulfovibrio vulgaris Hildenborough genome

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

Chhabra, S.R.; Butland, G.; Elias, D.

The ability to conduct advanced functional genomic studies of the thousands of sequenced bacteria has been hampered by the lack of available tools for making high- throughput chromosomal manipulations in a systematic manner that can be applied across diverse species. In this work, we highlight the use of synthetic biological tools to assemble custom suicide vectors with reusable and interchangeable DNA “parts” to facilitate chromosomal modification at designated loci. These constructs enable an array of downstream applications including gene replacement and creation of gene fusions with affinity purification or localization tags. We employed this approach to engineer chromosomal modifications inmore » a bacterium that has previously proven difficult to manipulate genetically, Desulfovibrio vulgaris Hildenborough, to generate a library of over 700 strains. Furthermore, we demonstrate how these modifications can be used for examining metabolic pathways, protein-protein interactions, and protein localization. The ubiquity of suicide constructs in gene replacement throughout biology suggests that this approach can be applied to engineer a broad range of species for a diverse array of systems biological applications and is amenable to high-throughput implementation.« less

Energy efficient strategy for throughput improvement in wireless sensor networks.

PubMed

Jabbar, Sohail; Minhas, Abid Ali; Imran, Muhammad; Khalid, Shehzad; Saleem, Kashif

2015-01-23

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.

Energy Efficient Strategy for Throughput Improvement in Wireless Sensor Networks

PubMed Central

Jabbar, Sohail; Minhas, Abid Ali; Imran, Muhammad; Khalid, Shehzad; Saleem, Kashif

2015-01-01

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature. PMID:25625902

Evaluation of the Effect of the Volume Throughput and Maximum Flux of Low-Surface-Tension Fluids on Bacterial Penetration of 0.2 Micron-Rated Filters during Process-Specific Filter Validation Testing.

PubMed

Folmsbee, Martha

2015-01-01

Approximately 97% of filter validation tests result in the demonstration of absolute retention of the test bacteria, and thus sterile filter validation failure is rare. However, while Brevundimonas diminuta (B. diminuta) penetration of sterilizing-grade filters is rarely detected, the observation that some fluids (such as vaccines and liposomal fluids) may lead to an increased incidence of bacterial penetration of sterilizing-grade filters by B. diminuta has been reported. The goal of the following analysis was to identify important drivers of filter validation failure in these rare cases. The identification of these drivers will hopefully serve the purpose of assisting in the design of commercial sterile filtration processes with a low risk of filter validation failure for vaccine, liposomal, and related fluids. Filter validation data for low-surface-tension fluids was collected and evaluated with regard to the effect of bacterial load (CFU/cm(2)), bacterial load rate (CFU/min/cm(2)), volume throughput (mL/cm(2)), and maximum filter flux (mL/min/cm(2)) on bacterial penetration. The data set (∼1162 individual filtrations) included all instances of process-specific filter validation failures performed at Pall Corporation, including those using other filter media, but did not include all successful retentive filter validation bacterial challenges. It was neither practical nor necessary to include all filter validation successes worldwide (Pall Corporation) to achieve the goals of this analysis. The percentage of failed filtration events for the selected total master data set was 27% (310/1162). Because it is heavily weighted with penetration events, this percentage is considerably higher than the actual rate of failed filter validations, but, as such, facilitated a close examination of the conditions that lead to filter validation failure. In agreement with our previous reports, two of the significant drivers of bacterial penetration identified were the total bacterial load and the bacterial load rate. In addition to these parameters, another three possible drivers of failure were also identified: volume throughput, maximum filter flux, and pressure. Of the data for which volume throughput information was available, 24% (249/1038) of the filtrations resulted in penetration. However, for the volume throughput range of 680-2260 mL/cm(2), only 9 out of 205 bacterial challenges (∼4%) resulted in penetration. Of the data for which flux information was available, 22% (212/946) resulted in bacterial penetration. However, in the maximum filter flux range from 7 to 18 mL/min/cm(2), only one out of 121 filtrations (0.6%) resulted in penetration. A slight increase in filter failure was observed in filter bacterial challenges with a differential pressure greater than 30 psid. When designing a commercial process for the sterile filtration of a low-surface-tension fluid (or any other potentially high-risk fluid), targeting the volume throughput range of 680-2260 mL/cm(2) or flux range of 7-18 mL/min/cm(2), and maintaining the differential pressure below 30 psid, could significantly decrease the risk of validation filter failure. However, it is important to keep in mind that these are general trends described in this study and some test fluids may not conform to the general trends described here. Ultimately, it is important to evaluate both filterability and bacterial retention of the test fluid under proposed process conditions prior to finalizing the manufacturing process to ensure successful process-specific filter validation of low-surface-tension fluids. An overwhelming majority of process-specific filter validation (qualification) tests result in the demonstration of absolute retention of test bacteria by sterilizing-grade membrane filters. As such, process-specific filter validation failure is rare. However, while bacterial penetration of sterilizing-grade filters during process-specific filter validation is rarely detected, some fluids (such as vaccines and liposomal fluids) have been associated with an increased incidence of bacterial penetration. The goal of the following analysis was to identify important drivers of process-specific filter validation failure. The identification of these drivers will possibly serve to assist in the design of commercial sterile filtration processes with a low risk of filter validation failure. Filter validation data for low-surface-tension fluids was collected and evaluated with regard to bacterial concentration and rates, as well as filtered fluid volume and rate (Pall Corporation). The master data set (∼1160 individual filtrations) included all recorded instances of process-specific filter validation failures but did not include all successful filter validation bacterial challenge tests. This allowed for a close examination of the conditions that lead to process-specific filter validation failure. As previously reported, two significant drivers of bacterial penetration were identified: the total bacterial load (the total number of bacteria per filter) and the bacterial load rate (the rate at which bacteria were applied to the filter). In addition to these parameters, another three possible drivers of failure were also identified: volumetric throughput, filter flux, and pressure. When designing a commercial process for the sterile filtration of a low-surface-tension fluid (or any other penetrative-risk fluid), targeting the identified bacterial challenge loads, volume throughput, and corresponding flux rates could decrease, and possibly eliminate, the risk of validation filter failure. However, it is important to keep in mind that these are general trends described in this study and some test fluids may not conform to the general trends described here. Ultimately, it is important to evaluate both filterability and bacterial retention of the test fluid under proposed process conditions prior to finalizing the manufacturing process to ensure successful filter validation of low-surface-tension fluids. © PDA, Inc. 2015.

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

PubMed Central

2014-01-01

Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

GoGene: gene annotation in the fast lane.

PubMed

Plake, Conrad; Royer, Loic; Winnenburg, Rainer; Hakenberg, Jörg; Schroeder, Michael

2009-07-01

High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing high-quality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4,000,000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18,000,000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http://gopubmed.org/gogene.

Pathway analyses and understanding disease associations

PubMed Central

Liu, Yu; Chance, Mark R

2013-01-01

High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of “omics” data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various “omics” data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions. PMID:24319650

A catalogue of polymorphisms related to xenobiotic metabolism and cancer susceptibility.

PubMed

Gemignani, Federica; Landi, Stefano; Vivant, Franck; Zienolddiny, Shanbeh; Brennan, Paul; Canzian, Federico

2002-08-01

High-throughput genotyping technology of multiple genes based on large samples of cases and controls are likely to be important in identifying common genes which have a moderate effect on the development of specific diseases. We present here a comprehensive list of 313 known experimentally confirmed polymorphisms in 54 genes which are particularly relevant for metabolism of drugs, alcohol, tobacco, and other potential carcinogens. We have compiled a catalog with a standardized format that summarizes the genetic and biochemical properties of the selected polymorphisms. We have also confirmed or redesigned experimental conditions for simplex or multiplex PCR amplification of a subset of 168 SNPs of particular interest, which will provide the basis for the design of assays compatible with high-throughput genotyping.

A Tox21 Approach to Altered Epigenetic Landscapes: Assessing Epigenetic Toxicity Pathways Leading to Altered Gene Expression and Oncogenic Transformation In Vitro

PubMed Central

Parfett, Craig L.; Desaulniers, Daniel

2017-01-01

An emerging vision for toxicity testing in the 21st century foresees in vitro assays assuming the leading role in testing for chemical hazards, including testing for carcinogenicity. Toxicity will be determined by monitoring key steps in functionally validated molecular pathways, using tests designed to reveal chemically-induced perturbations that lead to adverse phenotypic endpoints in cultured human cells. Risk assessments would subsequently be derived from the causal in vitro endpoints and concentration vs. effect data extrapolated to human in vivo concentrations. Much direct experimental evidence now shows that disruption of epigenetic processes by chemicals is a carcinogenic mode of action that leads to altered gene functions playing causal roles in cancer initiation and progression. In assessing chemical safety, it would therefore be advantageous to consider an emerging class of carcinogens, the epigenotoxicants, with the ability to change chromatin and/or DNA marks by direct or indirect effects on the activities of enzymes (writers, erasers/editors, remodelers and readers) that convey the epigenetic information. Evidence is reviewed supporting a strategy for in vitro hazard identification of carcinogens that induce toxicity through disturbance of functional epigenetic pathways in human somatic cells, leading to inactivated tumour suppressor genes and carcinogenesis. In the context of human cell transformation models, these in vitro pathway measurements ensure high biological relevance to the apical endpoint of cancer. Four causal mechanisms participating in pathways to persistent epigenetic gene silencing were considered: covalent histone modification, nucleosome remodeling, non-coding RNA interaction and DNA methylation. Within these four interacting mechanisms, 25 epigenetic toxicity pathway components (SET1, MLL1, KDM5, G9A, SUV39H1, SETDB1, EZH2, JMJD3, CBX7, CBX8, BMI, SUZ12, HP1, MPP8, DNMT1, DNMT3A, DNMT3B, TET1, MeCP2, SETDB2, BAZ2A, UHRF1, CTCF, HOTAIR and ANRIL) were found to have experimental evidence showing that functional perturbations played “driver” roles in human cellular transformation. Measurement of epigenotoxicants presents challenges for short-term carcinogenicity testing, especially in the high-throughput modes emphasized in the Tox21 chemicals testing approach. There is need to develop and validate in vitro tests to detect both, locus-specific, and genome-wide, epigenetic alterations with causal links to oncogenic cellular phenotypes. Some recent examples of cell-based high throughput chemical screening assays are presented that have been applied or have shown potential for application to epigenetic endpoints. PMID:28587163

Microelectronic electroporation array

NASA Astrophysics Data System (ADS)

Johnson, Lee J.; Shaffer, Kara J.; Skeath, Perry; Perkins, Frank K.; Pancrazio, Joseph; Scribner, Dean

2004-06-01

Gene Array technology has allowed for the study of gene binding by creating thousands of potential binding sites on a single device. A limitation of the current technology is that the effects of the gene and the gene-derived proteins cannot be studied in situ the same way, thousand site cell arrays are not readily available. We propose a new device structure to study the effects of gene modification on cells. This new array technology uses electroporation to target specific areas within a cell culture for transfection of genes. Electroporation arrays will allow high throughput analysis of gene effects on a given cell's response to a stress or a genes ability to restore normal cell function in disease modeling cells. Fluorescent imaging of dye labeled indicator molecules or cell viability will provide results indicating the most effective genes. The electroporation array consists of a microelectronic circuit, ancillary electronics, protecting electrode surface for cell culturing and a perfusion system for gene or drug delivery. The advantages of the current device are that there are 3200 sites for electroporation, all or any subsets of the electrodes can be activated. The cells are held in place by the electrode material. This technology could also be applied to high throughput screening of cell impermeant drugs.

Efficient production of a gene mutant cell line through integrating TALENs and high-throughput cell cloning.

PubMed

Sun, Changhong; Fan, Yu; Li, Juan; Wang, Gancheng; Zhang, Hanshuo; Xi, Jianzhong Jeff

2015-02-01

Transcription activator-like effectors (TALEs) are becoming powerful DNA-targeting tools in a variety of mammalian cells and model organisms. However, generating a stable cell line with specific gene mutations in a simple and rapid manner remains a challenging task. Here, we report a new method to efficiently produce monoclonal cells using integrated TALE nuclease technology and a series of high-throughput cell cloning approaches. Following this method, we obtained three mTOR mutant 293T cell lines within 2 months, which included one homozygous mutant line. © 2014 Society for Laboratory Automation and Screening.

The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders.

PubMed

Buxbaum, Joseph D; Daly, Mark J; Devlin, Bernie; Lehner, Thomas; Roeder, Kathryn; State, Matthew W

2012-12-20

Research during the past decade has seen significant progress in the understanding of the genetic architecture of autism spectrum disorders (ASDs), with gene discovery accelerating as the characterization of genomic variation has become increasingly comprehensive. At the same time, this research has highlighted ongoing challenges. Here we address the enormous impact of high-throughput sequencing (HTS) on ASD gene discovery, outline a consensus view for leveraging this technology, and describe a large multisite collaboration developed to accomplish these goals. Similar approaches could prove effective for severe neurodevelopmental disorders more broadly. Copyright © 2012 Elsevier Inc. All rights reserved.

Defining the Human Macula Transcriptome and Candidate Retinal Disease Genes UsingEyeSAGE

PubMed Central

Rickman, Catherine Bowes; Ebright, Jessica N.; Zavodni, Zachary J.; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P.; Wistow, Graeme; Boon, Kathy; Hauser, Michael A.

2009-01-01

Purpose To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Methods Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Results Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. Conclusions The EyeSAGE database, combining three different gene-profiling platforms including the authors’ multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions. PMID:16723438

Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE.

PubMed

Bowes Rickman, Catherine; Ebright, Jessica N; Zavodni, Zachary J; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P; Wistow, Graeme; Boon, Kathy; Hauser, Michael A

2006-06-01

To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. The EyeSAGE database, combining three different gene-profiling platforms including the authors' multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions.

An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions.

PubMed

De Diego, Nuria; Fürst, Tomáš; Humplík, Jan F; Ugena, Lydia; Podlešáková, Kateřina; Spíchal, Lukáš

2017-01-01

High-throughput plant phenotyping platforms provide new possibilities for automated, fast scoring of several plant growth and development traits, followed over time using non-invasive sensors. Using Arabidops is as a model offers important advantages for high-throughput screening with the opportunity to extrapolate the results obtained to other crops of commercial interest. In this study we describe the development of a highly reproducible high-throughput Arabidopsis in vitro bioassay established using our OloPhen platform, suitable for analysis of rosette growth in multi-well plates. This method was successfully validated on example of multivariate analysis of Arabidopsis rosette growth in different salt concentrations and the interaction with varying nutritional composition of the growth medium. Several traits such as changes in the rosette area, relative growth rate, survival rate and homogeneity of the population are scored using fully automated RGB imaging and subsequent image analysis. The assay can be used for fast screening of the biological activity of chemical libraries, phenotypes of transgenic or recombinant inbred lines, or to search for potential quantitative trait loci. It is especially valuable for selecting genotypes or growth conditions that improve plant stress tolerance.

Alternative to the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation

PubMed Central

Rotem, Asaf; Janzer, Andreas; Izar, Benjamin; Ji, Zhe; Doench, John G.; Garraway, Levi A.; Struhl, Kevin

2015-01-01

Colony formation in soft agar is the gold-standard assay for cellular transformation in vitro, but it is unsuited for high-throughput screening. Here, we describe an assay for cellular transformation that involves growth in low attachment (GILA) conditions and is strongly correlated with the soft-agar assay. Using GILA, we describe high-throughput screens for drugs and genes that selectively inhibit or increase transformation, but not proliferation. Such molecules are unlikely to be found through conventional drug screening, and they include kinase inhibitors and drugs for noncancer diseases. In addition to known oncogenes, the genetic screen identifies genes that contribute to cellular transformation. Lastly, we demonstrate the ability of Food and Drug Administration-approved noncancer drugs to selectively kill ovarian cancer cells derived from patients with chemotherapy-resistant disease, suggesting this approach may provide useful information for personalized cancer treatment. PMID:25902495

Alternative to the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation.

PubMed

Rotem, Asaf; Janzer, Andreas; Izar, Benjamin; Ji, Zhe; Doench, John G; Garraway, Levi A; Struhl, Kevin

2015-05-05

Colony formation in soft agar is the gold-standard assay for cellular transformation in vitro, but it is unsuited for high-throughput screening. Here, we describe an assay for cellular transformation that involves growth in low attachment (GILA) conditions and is strongly correlated with the soft-agar assay. Using GILA, we describe high-throughput screens for drugs and genes that selectively inhibit or increase transformation, but not proliferation. Such molecules are unlikely to be found through conventional drug screening, and they include kinase inhibitors and drugs for noncancer diseases. In addition to known oncogenes, the genetic screen identifies genes that contribute to cellular transformation. Lastly, we demonstrate the ability of Food and Drug Administration-approved noncancer drugs to selectively kill ovarian cancer cells derived from patients with chemotherapy-resistant disease, suggesting this approach may provide useful information for personalized cancer treatment.

Next-Generation High-Throughput Functional Annotation of Microbial Genomes.

PubMed

Baric, Ralph S; Crosson, Sean; Damania, Blossom; Miller, Samuel I; Rubin, Eric J

2016-10-04

Host infection by microbial pathogens cues global changes in microbial and host cell biology that facilitate microbial replication and disease. The complete maps of thousands of bacterial and viral genomes have recently been defined; however, the rate at which physiological or biochemical functions have been assigned to genes has greatly lagged. The National Institute of Allergy and Infectious Diseases (NIAID) addressed this gap by creating functional genomics centers dedicated to developing high-throughput approaches to assign gene function. These centers require broad-based and collaborative research programs to generate and integrate diverse data to achieve a comprehensive understanding of microbial pathogenesis. High-throughput functional genomics can lead to new therapeutics and better understanding of the next generation of emerging pathogens by rapidly defining new general mechanisms by which organisms cause disease and replicate in host tissues and by facilitating the rate at which functional data reach the scientific community. Copyright © 2016 Baric et al.

Transcriptional profiling of TLR-4/7/8-stimulated guinea pig splenocytes and whole blood by bDNA assay

PubMed Central

Ching, Lance K.; Mompoint, Farah; Guderian, Jeffrey A.; Picone, Alex; Orme, Ian M.; Coler, Rhea N.; Reed, Steven G.; Baldwin, Susan L.

2011-01-01

Toll-like receptor (TLR) agonists are currently being examined as adjuvants for vaccines, with several lead candidates now in licensed products or in late-stage clinical development. Guinea pigs are widely used for preclinical testing of drugs and vaccines; however, evaluation of TLR agonists in this model is hindered by the limited availability of immunological tools and reagents. In this study, we validated the use of a branched-chain DNA (bDNA) assay known as the QuantiGene Plex 2.0 Reagent System for measuring innate cytokine and chemokine mRNA levels following TLR stimulation of guinea pig cells. Gene expression for T-helper-1 (Th1) polarizing cytokines (TNF-α, IL-1β, IL-12) and chemokines (CXCL1, CCL2) was upregulated following ex vivo stimulation of guinea pig splenocytes and whole blood with TLR-4 or TLR-7/8 agonists. These data confirm the utility of the QuantiGene system both as an alternative to RT-PCR for measuring transcript levels and as a high-throughput screening tool for dissecting the immunological response to TLR stimulation in guinea pigs. Overall, the QuantiGene platform is reliable, reproducible, and sensitive. These agonists have the potential to be used as adjuvant components in vaccines against various pathogens. PMID:21839740

Identification of innate lymphoid cells in single-cell RNA-Seq data.

PubMed

Suffiotti, Madeleine; Carmona, Santiago J; Jandus, Camilla; Gfeller, David

2017-07-01

Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.

Identify mutation in amyotrophic lateral sclerosis cases using HaloPlex target enrichment system.

PubMed

Liu, Zhi-Jun; Li, Hong-Fu; Tan, Guo-He; Tao, Qing-Qing; Ni, Wang; Cheng, Xue-Wen; Xiong, Zhi-Qi; Wu, Zhi-Ying

2014-12-01

To date, at least 18 causative genes have been identified in amyotrophic lateral sclerosis (ALS). Because of the clinical and genetic heterogeneity, molecular diagnosis for ALS faces great challenges. HaloPlex target enrichment system is a new targeted sequencing approach, which can detect already known mutations or candidate genes. We performed this approach to screen 18 causative genes of ALS, including SOD1, SETX, FUS, ANG, TARDBP, ALS2, FIG4, VAPB, OPTN, DAO, VCP, UBQLN2, SPG11, SIGMAR1, DCTN1, SQSTM1, PFN1, and CHMP2B in 8 ALS probands. Using this approach, we got an average of 9.5 synonymous or missense mutations per sample. After validation by Sanger sequencing, we identified 3 documented SOD1 mutations (p.F21C, p.G148D, and p.C147R) and 1 novel DCTN1 p.G59R mutation in 4 probands. The novel DCTN1 mutation appeared to segregate with the disease in the pedigree and was absent in 200 control subjects. The high throughput and efficiency of this approach indicated that it could be applied to diagnose ALS and other inherited diseases with multiple causative genes in clinical practice. Copyright © 2014 Elsevier Inc. All rights reserved.

Novel KCNQ2 channel activators discovered using fluorescence-based and automated patch-clamp-based high-throughput screening techniques

PubMed Central

Yue, Jin-feng; Qiao, Guan-hua; Liu, Ni; Nan, Fa-jun; Gao, Zhao-bing

2016-01-01

Aim: To establish an improved, high-throughput screening techniques for identifying novel KCNQ2 channel activators. Methods: KCNQ2 channels were stably expressed in CHO cells (KCNQ2 cells). Thallium flux assay was used for primary screening, and 384-well automated patch-clamp IonWorks Barracuda was used for hit validation. Two validated activators were characterized using a conventional patch-clamp recording technique. Results: From a collection of 80 000 compounds, the primary screening revealed a total of 565 compounds that potentiated the fluorescence signals in thallium flux assay by more than 150%. When the 565 hits were examined in IonWorks Barracuda, 38 compounds significantly enhanced the outward currents recorded in KCNQ2 cells, and were confirmed as KCNQ2 activators. In the conventional patch-clamp recordings, two validated activators ZG1732 and ZG2083 enhanced KCNQ2 currents with EC50 values of 1.04±0.18 μmol/L and 1.37±0.06 μmol/L, respectively. Conclusion: The combination of thallium flux assay and IonWorks Barracuda assay is an efficient high-throughput screening (HTS) route for discovering KCNQ2 activators. PMID:26725738

[Current applications of high-throughput DNA sequencing technology in antibody drug research].

PubMed

Yu, Xin; Liu, Qi-Gang; Wang, Ming-Rong

2012-03-01

Since the publication of a high-throughput DNA sequencing technology based on PCR reaction was carried out in oil emulsions in 2005, high-throughput DNA sequencing platforms have been evolved to a robust technology in sequencing genomes and diverse DNA libraries. Antibody libraries with vast numbers of members currently serve as a foundation of discovering novel antibody drugs, and high-throughput DNA sequencing technology makes it possible to rapidly identify functional antibody variants with desired properties. Herein we present a review of current applications of high-throughput DNA sequencing technology in the analysis of antibody library diversity, sequencing of CDR3 regions, identification of potent antibodies based on sequence frequency, discovery of functional genes, and combination with various display technologies, so as to provide an alternative approach of discovery and development of antibody drugs.

Transcriptomic response to low salinity stress in gills of the Pacific white shrimp, Litopenaeus vannamei.

PubMed

Hu, Dongxu; Pan, Luqing; Zhao, Qun; Ren, Qin

2015-12-01

The Pacific white shrimp, Litopenaeus vannamei (L. vannamei), is one of the most farmed species. Salinity is an important environmental factor that affects its growth and distribution. However, the molecular mechanism of the shrimp in response to salinity stress remains largely unclear. High-throughput sequencing is a helpful tool to analyze the molecular response to salinity challenge in shrimp. In the present study, the transcriptomic responses of the gills in L. vannamei under low salinity stress were detected by Illumina's digital gene expression system. A total of 10,725,789 and 10,827,411 reads were generated from the non-changed and low salinity changed groups, respectively. 64,590 Unigenes with an average length of 764 bp were generated. Compared with the control, 585 genes were differentially expressed under low salinity. GO functional analysis and KEGG pathway analysis indicated some vital genes in response to the challenge. Ten genes related to osmoregulation and ambient salinity adaption were selected to validate the DGE results by RT-qPCR. This work provides valuable information to study the mechanism of salinity adaption in L. vannamei. Genes and pathways from the results will be beneficial to reveal the molecular basis of osmoregulation. It also gives an insight into the response to the salinity challenge in L. vannamei. Copyright © 2015 Elsevier B.V. All rights reserved.

Genome engineering and gene expression control for bacterial strain development.

PubMed

Song, Chan Woo; Lee, Joungmin; Lee, Sang Yup

2015-01-01

In recent years, a number of techniques and tools have been developed for genome engineering and gene expression control to achieve desired phenotypes of various bacteria. Here we review and discuss the recent advances in bacterial genome manipulation and gene expression control techniques, and their actual uses with accompanying examples. Genome engineering has been commonly performed based on homologous recombination. During such genome manipulation, the counterselection systems employing SacB or nucleases have mainly been used for the efficient selection of desired engineered strains. The recombineering technology enables simple and more rapid manipulation of the bacterial genome. The group II intron-mediated genome engineering technology is another option for some bacteria that are difficult to be engineered by homologous recombination. Due to the increasing demands on high-throughput screening of bacterial strains having the desired phenotypes, several multiplex genome engineering techniques have recently been developed and validated in some bacteria. Another approach to achieve desired bacterial phenotypes is the repression of target gene expression without the modification of genome sequences. This can be performed by expressing antisense RNA, small regulatory RNA, or CRISPR RNA to repress target gene expression at the transcriptional or translational level. All of these techniques allow efficient and rapid development and screening of bacterial strains having desired phenotypes, and more advanced techniques are expected to be seen. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effects of spaceflight on the immunoglobulin repertoire of unimmunized C57BL/6 mice

NASA Astrophysics Data System (ADS)

Ward, Claire; Rettig, Trisha A.; Hlavacek, Savannah; Bye, Bailey A.; Pecaut, Michael J.; Chapes, Stephen K.

2018-02-01

Spaceflight has been shown to suppress the adaptive immune response, altering the distribution and function of lymphocyte populations. B lymphocytes express highly specific and highly diversified receptors, known as immunoglobulins (Ig), that directly bind and neutralize pathogens. Ig diversity is achieved through the enzymatic splicing of gene segments within the genomic DNA of each B cell in a host. The collection of Ig specificities within a host, or Ig repertoire, has been increasingly characterized in both basic research and clinical settings using high-throughput sequencing technology (HTS). We utilized HTS to test the hypothesis that spaceflight affects the B-cell repertoire. To test this hypothesis, we characterized the impact of spaceflight on the unimmunized Ig repertoire of C57BL/6 mice that were flown aboard the International Space Station (ISS) during the Rodent Research One validation flight in comparison to ground controls. Individual gene segment usage was similar between ground control and flight animals, however, gene segment combinations and the junctions in which gene segments combine was varied among animals within and between treatment groups. We also found that spontaneous somatic mutations in the IgH and Igκ gene loci were not increased. These data suggest that space flight did not affect the B cell repertoire of mice flown and housed on the ISS over a short period of time.

Clinical characteristics and mutation analysis of five Chinese patients with maple syrup urine disease.

PubMed

Li, Xiaomei; Yang, Yali; Gao, Qing; Gao, Min; Lv, Yvqiang; Dong, Rui; Liu, Yi; Zhang, Kaihui; Gai, Zhongtao

2018-06-01

Maple syrup urine disease (MSUD) is an autosomal recessive disorder affecting branched-chain amino acids (BCAAs) metabolism and caused by a defect in the thiamine-dependent enzyme branched chain α-ketoacid dehydrogenase (BCKD) with subsequent accumulation of BCAAs and corresponding branched-chain keto acids (BCKAs) metabolites. Presently, at least 4 genes of BCKDHA, BCKDHB, DLD and DBT have been reported to cause MSUD. Furthermore, more than 265 mutations have been identified as the cause across different populations worldwide. Some studies have reported the data of gene mutations in Chinese people with MSUD. In this study, we present clinical characteristics and mutational analyses in five Chinese Han child with MSUD, which had been screened out by tandem mass spectrometry detection of amino acids in blood samples. High-throughput sequencing, Sanger sequence and real-time qualitative PCR were performed to detect and verify the genetic mutations. Six different novel genetic variants were validated in BCKDHB gene and BCKDHA gene, including c.523 T > C, c.659delA, c.550delT, c.863G > A and two gross deletions. Interestingly, 3 cases had identical mutation of BCKDHB gene (c.659delA). We predicted the pathogenicity and analyzed the clinical characteristics. The identification of these mutations in this study further expands the mutation spectrum of MSUD and contributes to prenatal molecular diagnosis of MSUD.

A collection of cytochrome P450 monooxygenase genes involved in modification and detoxification of herbicide atrazine in rice (Oryza sativa) plants.

PubMed

Rong Tan, Li; Chen Lu, Yi; Jing Zhang, Jing; Luo, Fang; Yang, Hong

2015-09-01

Plant cytochrome P450 monooxygenases constitute one of the largest families of protein genes involved in plant growth, development and acclimation to biotic and abiotic stresses. However, whether these genes respond to organic toxic compounds and their biological functions for detoxifying toxic compounds such as herbicides in rice are poorly understood. The present study identified 201 genes encoding cytochrome P450s from an atrazine-exposed rice transcriptome through high-throughput sequencing. Of these, 69 cytochrome P450 genes were validated by microarray and some of them were confirmed by real time PCR. Activities of NADPH-cytochrome P450 reductase (CPR) and p-nitroanisole O-demethylase (PNOD) related to toxicity were determined and significantly induced by atrazine exposure. To dissect the mechanism underlying atrazine modification and detoxification by P450, metabolites (or derivatives) of atrazine in plants were analyzed by ultra performance liquid chromatography mass spectrometry (UPLC/MS). Major metabolites comprised desmethylatrazine (DMA), desethylatrazine (DEA), desisopropylatrazine (DIA), hydroxyatrazine (HA), hydroxyethylatrazine (HEA) and hydroxyisopropylatrazine (HIA). All of them were chemically modified by P450s. Furthermore, two specific inhibitors of piperonyl butoxide (PBO) and malathion (MAL) were used to assess the correlation between the P450s activity and rice responses including accumulation of atrazine in tissues, shoot and root growth and detoxification. Copyright © 2015 Elsevier Inc. All rights reserved.

BIOREL: the benchmark resource to estimate the relevance of the gene networks.

PubMed

Antonov, Alexey V; Mewes, Hans W

2006-02-06

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.

A high-throughput in vitro ring assay for vasoactivity using magnetic 3D bioprinting

PubMed Central

Tseng, Hubert; Gage, Jacob A.; Haisler, William L.; Neeley, Shane K.; Shen, Tsaiwei; Hebel, Chris; Barthlow, Herbert G.; Wagoner, Matthew; Souza, Glauco R.

2016-01-01

Vasoactive liabilities are typically assayed using wire myography, which is limited by its high cost and low throughput. To meet the demand for higher throughput in vitro alternatives, this study introduces a magnetic 3D bioprinting-based vasoactivity assay. The principle behind this assay is the magnetic printing of vascular smooth muscle cells into 3D rings that functionally represent blood vessel segments, whose contraction can be altered by vasodilators and vasoconstrictors. A cost-effective imaging modality employing a mobile device is used to capture contraction with high throughput. The goal of this study was to validate ring contraction as a measure of vasoactivity, using a small panel of known vasoactive drugs. In vitro responses of the rings matched outcomes predicted by in vivo pharmacology, and were supported by immunohistochemistry. Altogether, this ring assay robustly models vasoactivity, which could meet the need for higher throughput in vitro alternatives. PMID:27477945

Generation and characterization of West Nile pseudo-infectious reporter virus for antiviral screening.

PubMed

Zhang, Hong-Lei; Ye, Han-Qing; Deng, Cheng-Lin; Liu, Si-Qing; Shi, Pei-Yong; Qin, Cheng-Feng; Yuan, Zhi-Ming; Zhang, Bo

2017-05-01

West Nile virus (WNV), a mosquito-borne flavivirus, is an important neurotropic human pathogen. As a biosafety level-3 (BSL-3) agent, WNV is strictly to BSL-3 laboratories for experimentations, thus greatly hindering the development of vaccine and antiviral drug. Here, we developed a novel pseudo-infectious WNV reporter virus expressing the Gaussia luciferase (Gluc). A stable 293T NS1 cell line expressing NS1 was selected for trans-supplying NS1 protein to support the replication of WNV-ΔNS1 virus and WNV-ΔNS1-Gluc reporter virus with large-fragment deletion of NS1. WNV-ΔNS1 virus and WNV-Gluc-ΔNS1 reporter virus were confined to complete their replication cycle in this 293T NS1 cell line, displaying nearly identical growth kinetics to WT WNV although the viral titers were lower than those of WT WNV. The reporter gene was stably maintained in virus genome at least within three rounds of passage in 293T NS1 cell line. Using a known flaviviruses inhibitor, NITD008, we demonstrated that the pseudo-infectious WNV-Gluc-ΔNS1 could be used for antiviral screening. Furthermore, a high-throughput screening (HTS) assay in a 96-well format was optimized and validated using several known WNV inhibitors, indicating that the optimized HTS assay was suitable for high-throughput screening WNV inhibitors. Our work provides a stable and safe tool to handle WNV outside of a BSL-3 facility and facilitates high throughput screening for anti-WNV drugs. Copyright © 2017 Elsevier B.V. All rights reserved.

Validation of visualized transgenic zebrafish as a high throughput model to assay bradycardia related cardio toxicity risk candidates.

PubMed

Wen, Dingsheng; Liu, Aiming; Chen, Feng; Yang, Julin; Dai, Renke

2012-10-01

Drug-induced QT prolongation usually leads to torsade de pointes (TdP), thus for drugs in the early phase of development this risk should be evaluated. In the present study, we demonstrated a visualized transgenic zebrafish as an in vivo high-throughput model to assay the risk of drug-induced QT prolongation. Zebrafish larvae 48 h post-fertilization expressing green fluorescent protein in myocardium were incubated with compounds reported to induce QT prolongation or block the human ether-a-go-go-related gene (hERG) K⁺ current. The compounds sotalol, indapaminde, erythromycin, ofoxacin, levofloxacin, sparfloxacin and roxithromycin were additionally administrated by microinjection into the larvae yolk sac. The ventricle heart rate was recorded using the automatic monitoring system after incubation or microinjection. As a result, 14 out of 16 compounds inducing dog QT prolongation caused bradycardia in zebrafish. A similar result was observed with 21 out of 26 compounds which block hERG current. Among the 30 compounds which induced human QT prolongation, 25 caused bradycardia in this model. Thus, the risk of compounds causing bradycardia in this transgenic zebrafish correlated with that causing QT prolongation and hERG K⁺ current blockage in established models. The tendency that high logP values lead to high risk of QT prolongation in this model was indicated, and non-sensitivity of this model to antibacterial agents was revealed. These data suggest application of this transgenic zebrafish as a high-throughput model to screen QT prolongation-related cardio toxicity of the drug candidates. Copyright © 2012 John Wiley & Sons, Ltd.

Quantitative Analysis of Focused A-To-I RNA Editing Sites by Ultra-High-Throughput Sequencing in Psychiatric Disorders

PubMed Central

Zhu, Hu; Urban, Daniel J.; Blashka, Jared; McPheeters, Matthew T.; Kroeze, Wesley K.; Mieczkowski, Piotr; Overholser, James C.; Jurjus, George J.; Dieter, Lesa; Mahajan, Gouri J.; Rajkowska, Grazyna; Wang, Zefeng; Sullivan, Patrick F.; Stockmeier, Craig A.; Roth, Bryan L.

2012-01-01

A-to-I RNA editing is a post-transcriptional modification of single nucleotides in RNA by adenosine deamination, which thereby diversifies the gene products encoded in the genome. Thousands of potential RNA editing sites have been identified by recent studies (e.g. see Li et al, Science 2009); however, only a handful of these sites have been independently confirmed. Here, we systematically and quantitatively examined 109 putative coding region A-to-I RNA editing sites in three sets of normal human brain samples by ultra-high-throughput sequencing (uHTS). Forty of 109 putative sites, including 25 previously confirmed sites, were validated as truly edited in our brain samples, suggesting an overestimation of A-to-I RNA editing in these putative sites by Li et al (2009). To evaluate RNA editing in human disease, we analyzed 29 of the confirmed sites in subjects with major depressive disorder and schizophrenia using uHTS. In striking contrast to many prior studies, we did not find significant alterations in the frequency of RNA editing at any of the editing sites in samples from these patients, including within the 5HT2C serotonin receptor (HTR2C). Our results indicate that uHTS is a fast, quantitative and high-throughput method to assess RNA editing in human physiology and disease and that many prior studies of RNA editing may overestimate both the extent and disease-related variability of RNA editing at the sites we examined in the human brain. PMID:22912834

High throughput 16SrRNA gene sequencing reveals the correlation between Propionibacterium acnes and sarcoidosis.

PubMed

Zhao, Meng-Meng; Du, Shan-Shan; Li, Qiu-Hong; Chen, Tao; Qiu, Hui; Wu, Qin; Chen, Shan-Shan; Zhou, Ying; Zhang, Yuan; Hu, Yang; Su, Yi-Liang; Shen, Li; Zhang, Fen; Weng, Dong; Li, Hui-Ping

2017-02-01

This study aims to use high throughput 16SrRNA gene sequencing to examine the bacterial profile of lymph node biopsy samples of patients with sarcoidosis and to further verify the association between Propionibacterium acnes (P. acnes) and sarcoidosis. A total of 36 mediastinal lymph node biopsy specimens were collected from 17 cases of sarcoidosis, 8 tuberculosis (TB group), and 11 non-infectious lung diseases (control group). The V4 region of the bacterial 16SrRNA gene in the specimens was amplified and sequenced using the high throughput sequencing platform MiSeq, and bacterial profile was established. The data analysis software QIIME and Metastats were used to compare bacterial relative abundance in the three patient groups. Overall, 545 genera were identified; 38 showed significantly lower and 29 had significantly higher relative abundance in the sarcoidosis group than in the TB and control groups (P < 0.01). P. acnes 16SrRNA was exclusively found in all the 17 samples of the sarcoidosis group, whereas was not detected in the TB and control groups. The relative abundance of P. acnes in the sarcoidosis group (0.16% ± 0. 11%) was significantly higher than that in the TB (Metastats analysis: P = 0.0010, q = 0.0044) and control groups (Metastats analysis: P = 0.0010, q = 0.0038). The relative abundance of P. granulosum was only 0.0022% ± 0. 0044% in the sarcoidosis group. P. granulosum 16SrRNA was not detected in the other two groups. High throughput 16SrRNA gene sequencing appears to be a useful tool to investigate the bacterial profile of sarcoidosis specimens. The results suggest that P. acnes may be involved in sarcoidosis development.

Development and Validation of a Computational Model for Androgen Receptor Activity

EPA Science Inventory

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can mo...

Gene Ontology annotations at SGD: new data sources and annotation methods

PubMed Central

Hong, Eurie L.; Balakrishnan, Rama; Dong, Qing; Christie, Karen R.; Park, Julie; Binkley, Gail; Costanzo, Maria C.; Dwight, Selina S.; Engel, Stacia R.; Fisk, Dianna G.; Hirschman, Jodi E.; Hitz, Benjamin C.; Krieger, Cynthia J.; Livstone, Michael S.; Miyasato, Stuart R.; Nash, Robert S.; Oughtred, Rose; Skrzypek, Marek S.; Weng, Shuai; Wong, Edith D.; Zhu, Kathy K.; Dolinski, Kara; Botstein, David; Cherry, J. Michael

2008-01-01

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current. PMID:17982175

High-throughput identification of antigen-specific TCRs by TCR gene capture.

PubMed

Linnemann, Carsten; Heemskerk, Bianca; Kvistborg, Pia; Kluin, Roelof J C; Bolotin, Dmitriy A; Chen, Xiaojing; Bresser, Kaspar; Nieuwland, Marja; Schotte, Remko; Michels, Samira; Gomez-Eerland, Raquel; Jahn, Lorenz; Hombrink, Pleun; Legrand, Nicolas; Shu, Chengyi Jenny; Mamedov, Ilgar Z; Velds, Arno; Blank, Christian U; Haanen, John B A G; Turchaninova, Maria A; Kerkhoven, Ron M; Spits, Hergen; Hadrup, Sine Reker; Heemskerk, Mirjam H M; Blankenstein, Thomas; Chudakov, Dmitriy M; Bendle, Gavin M; Schumacher, Ton N M

2013-11-01

The transfer of T cell receptor (TCR) genes into patient T cells is a promising approach for the treatment of both viral infections and cancer. Although efficient methods exist to identify antibodies for the treatment of these diseases, comparable strategies to identify TCRs have been lacking. We have developed a high-throughput DNA-based strategy to identify TCR sequences by the capture and sequencing of genomic DNA fragments encoding the TCR genes. We establish the value of this approach by assembling a large library of cancer germline tumor antigen-reactive TCRs. Furthermore, by exploiting the quantitative nature of TCR gene capture, we show the feasibility of identifying antigen-specific TCRs in oligoclonal T cell populations from either human material or TCR-humanized mice. Finally, we demonstrate the ability to identify tumor-reactive TCRs within intratumoral T cell subsets without knowledge of antigen specificities, which may be the first step toward the development of autologous TCR gene therapy to target patient-specific neoantigens in human cancer.

DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.

PubMed

Yu, Guangchuang; Wang, Li-Gen; Yan, Guang-Rong; He, Qing-Yu

2015-02-15

Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported. DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html). Supplementary data are available at Bioinformatics online. gcyu@connect.hku.hk or tqyhe@jnu.edu.cn. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A High-throughput Assay for mRNA Silencing in Primary Cortical Neurons in vitro with Oligonucleotide Therapeutics.

PubMed

Alterman, Julia F; Coles, Andrew H; Hall, Lauren M; Aronin, Neil; Khvorova, Anastasia; Didiot, Marie-Cécile

2017-08-20

Primary neurons represent an ideal cellular system for the identification of therapeutic oligonucleotides for the treatment of neurodegenerative diseases. However, due to the sensitive nature of primary cells, the transfection of small interfering RNAs (siRNA) using classical methods is laborious and often shows low efficiency. Recent progress in oligonucleotide chemistry has enabled the development of stabilized and hydrophobically modified small interfering RNAs (hsiRNAs). This new class of oligonucleotide therapeutics shows extremely efficient self-delivery properties and supports potent and durable effects in vitro and in vivo . We have developed a high-throughput in vitro assay to identify and test hsiRNAs in primary neuronal cultures. To simply, rapidly, and accurately quantify the mRNA silencing of hundreds of hsiRNAs, we use the QuantiGene 2.0 quantitative gene expression assay. This high-throughput, 96-well plate-based assay can quantify mRNA levels directly from sample lysate. Here, we describe a method to prepare short-term cultures of mouse primary cortical neurons in a 96-well plate format for high-throughput testing of oligonucleotide therapeutics. This method supports the testing of hsiRNA libraries and the identification of potential therapeutics within just two weeks. We detail methodologies of our high throughput assay workflow from primary neuron preparation to data analysis. This method can help identify oligonucleotide therapeutics for treatment of various neurological diseases.

Pre-amplification in the context of high-throughput qPCR gene expression experiment.

PubMed

Korenková, Vlasta; Scott, Justin; Novosadová, Vendula; Jindřichová, Marie; Langerová, Lucie; Švec, David; Šídová, Monika; Sjöback, Robert

2015-03-11

With the introduction of the first high-throughput qPCR instrument on the market it became possible to perform thousands of reactions in a single run compared to the previous hundreds. In the high-throughput reaction, only limited volumes of highly concentrated cDNA or DNA samples can be added. This necessity can be solved by pre-amplification, which became a part of the high-throughput experimental workflow. Here, we focused our attention on the limits of the specific target pre-amplification reaction and propose the optimal, general setup for gene expression experiment using BioMark instrument (Fluidigm). For evaluating different pre-amplification factors following conditions were combined: four human blood samples from healthy donors and five transcripts having high to low expression levels; each cDNA sample was pre-amplified at four cycles (15, 18, 21, and 24) and five concentrations (equivalent to 0.078 ng, 0.32 ng, 1.25 ng, 5 ng, and 20 ng of total RNA). Factors identified as critical for a success of cDNA pre-amplification were cycle of pre-amplification, total RNA concentration, and type of gene. The selected pre-amplification reactions were further tested for optimal Cq distribution in a BioMark Array. The following concentrations combined with pre-amplification cycles were optimal for good quality samples: 20 ng of total RNA with 15 cycles of pre-amplification, 20x and 40x diluted; and 5 ng and 20 ng of total RNA with 18 cycles of pre-amplification, both 20x and 40x diluted. We set up upper limits for the bulk gene expression experiment using gene expression Dynamic Array and provided an easy-to-obtain tool for measuring of pre-amplification success. We also showed that variability of the pre-amplification, introduced into the experimental workflow of reverse transcription-qPCR, is lower than variability caused by the reverse transcription step.

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

PubMed Central

Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

2016-01-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

Field Validation of a Transcriptional Assay for the Prediction of Age of Uncaged Aedes aegypti Mosquitoes in Northern Australia

PubMed Central

Hugo, Leon E.; Cook, Peter E.; Johnson, Petrina H.; Rapley, Luke P.; Kay, Brian H.; Ryan, Peter A.; Ritchie, Scott A.; O'Neill, Scott L.

2010-01-01

Background New strategies to eliminate dengue have been proposed that specifically target older Aedes aegypti mosquitoes, the proportion of the vector population that is potentially capable of transmitting dengue viruses. Evaluation of these strategies will require accurate and high-throughput methods of predicting mosquito age. We previously developed an age prediction assay for individual Ae. aegypti females based on the transcriptional profiles of a selection of age responsive genes. Here we conducted field testing of the method on Ae. aegypti that were entirely uncaged and free to engage in natural behavior. Methodology/Principal Findings We produced “free-range” test specimens by releasing 8007 adult Ae. aegypti inside and around an isolated homestead in north Queensland, Australia, and recapturing females at two day intervals. We applied a TaqMan probe-based assay design that enabled high-throughput quantitative RT-PCR of four transcripts from three age-responsive genes and a reference gene. An age prediction model was calibrated on mosquitoes maintained in small sentinel cages, in which 68.8% of the variance in gene transcription measures was explained by age. The model was then used to predict the ages of the free-range females. The relationship between the predicted and actual ages achieved an R2 value of 0.62 for predictions of females up to 29 days old. Transcriptional profiles and age predictions were not affected by physiological variation associated with the blood feeding/egg development cycle and we show that the age grading method could be applied to differentiate between two populations of mosquitoes having a two-fold difference in mean life expectancy. Conclusions/Significance The transcriptional profiles of age responsive genes facilitated age estimates of near-wild Ae. aegypti females. Our age prediction assay for Ae. aegypti provides a useful tool for the evaluation of mosquito control interventions against dengue where mosquito survivorship or lifespan reduction are crucial to their success. The approximate cost of the method was US$7.50 per mosquito and 60 mosquitoes could be processed in 3 days. The assay is based on conserved genes and modified versions are likely to support similar investigations of several important mosquito and other disease vectors. PMID:20186322

A Perspective on the Future of High-Throughput RNAi Screening: Will CRISPR Cut Out the Competition or Can RNAi Help Guide the Way?

PubMed

Taylor, Jessica; Woodcock, Simon

2015-09-01

For more than a decade, RNA interference (RNAi) has brought about an entirely new approach to functional genomics screening. Enabling high-throughput loss-of-function (LOF) screens against the human genome, identifying new drug targets, and significantly advancing experimental biology, RNAi is a fast, flexible technology that is compatible with existing high-throughput systems and processes; however, the recent advent of clustered regularly interspaced palindromic repeats (CRISPR)-Cas, a powerful new precise genome-editing (PGE) technology, has opened up vast possibilities for functional genomics. CRISPR-Cas is novel in its simplicity: one piece of easily engineered guide RNA (gRNA) is used to target a gene sequence, and Cas9 expression is required in the cells. The targeted double-strand break introduced by the gRNA-Cas9 complex is highly effective at removing gene expression compared to RNAi. Together with the reduced cost and complexity of CRISPR-Cas, there is the realistic opportunity to use PGE to screen for phenotypic effects in a total gene knockout background. This review summarizes the exciting development of CRISPR-Cas as a high-throughput screening tool, comparing its future potential to that of well-established RNAi screening techniques, and highlighting future challenges and opportunities within these disciplines. We conclude that the two technologies actually complement rather than compete with each other, enabling greater understanding of the genome in relation to drug discovery. © 2015 Society for Laboratory Automation and Screening.

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs.

PubMed

Ihlow, Alexander; Schweizer, Patrick; Seiffert, Udo

2008-01-23

To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps. We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the Mlo gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as mlo5. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of Mlo, thus providing proof of concept for its usefulness in detecting gene-target effects. Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.

High-Throughput Screening to Identify Regulators of Meiosis-Specific Gene Expression in Saccharomyces cerevisiae.

PubMed

Kassir, Yona

2017-01-01

Meiosis and gamete formation are processes that are essential for sexual reproduction in all eukaryotic organisms. Multiple intracellular and extracellular signals feed into pathways that converge on transcription factors that induce the expression of meiosis-specific genes. Once triggered the meiosis-specific gene expression program proceeds in a cascade that drives progress through the events of meiosis and gamete formation. Meiosis-specific gene expression is tightly controlled by a balance of positive and negative regulatory factors that respond to a plethora of signaling pathways. The budding yeast Saccharomyces cerevisiae has proven to be an outstanding model for the dissection of gametogenesis owing to the sophisticated genetic manipulations that can be performed with the cells. It is possible to use a variety selection and screening methods to identify genes and their functions. High-throughput screening technology has been developed to allow an array of all viable yeast gene deletion mutants to be screened for phenotypes and for regulators of gene expression. This chapter describes a protocol that has been used to screen a library of homozygous diploid yeast deletion strains to identify regulators of the meiosis-specific IME1 gene.

Structuring intuition with theory: The high-throughput way

NASA Astrophysics Data System (ADS)

Fornari, Marco

2015-03-01

First principles methodologies have grown in accuracy and applicability to the point where large databases can be built, shared, and analyzed with the goal of predicting novel compositions, optimizing functional properties, and discovering unexpected relationships between the data. In order to be useful to a large community of users, data should be standardized, validated, and distributed. In addition, tools to easily manage large datasets should be made available to effectively lead to materials development. Within the AFLOW consortium we have developed a simple frame to expand, validate, and mine data repositories: the MTFrame. Our minimalistic approach complement AFLOW and other existing high-throughput infrastructures and aims to integrate data generation with data analysis. We present few examples from our work on materials for energy conversion. Our intent s to pinpoint the usefulness of high-throughput methodologies to guide the discovery process by quantitatively structuring the scientific intuition. This work was supported by ONR-MURI under Contract N00014-13-1-0635 and the Duke University Center for Materials Genomics.

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

PubMed Central

2013-01-01

Background The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets. Results By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence. Conclusions Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation. PMID:24088245

TaqMan 5′-Nuclease Human Immunodeficiency Virus Type 1 PCR Assay with Phage-Packaged Competitive Internal Control for High-Throughput Blood Donor Screening

PubMed Central

Drosten, C.; Seifried, E.; Roth, W. K.

2001-01-01

Screening of blood donors for human immunodeficiency virus type 1 (HIV-1) infection by PCR permits the earlier diagnosis of HIV-1 infection compared with that by serologic assays. We have established a high-throughput reverse transcription (RT)-PCR assay based on 5′-nuclease PCR. By in-tube detection of HIV-1 RNA with a fluorogenic probe, the 5′-nuclease PCR technology (TaqMan PCR) eliminates the risk of carryover contamination, a major problem in PCR testing. We outline the development and evaluation of the PCR assay from a technical point of view. A one-step RT-PCR that targets the gag genes of all known HIV-1 group M isolates was developed. An internal control RNA detectable with a heterologous 5′-nuclease probe was derived from the viral target cDNA and was packaged into MS2 coliphages (Armored RNA). Because the RNA was protected against digestion with RNase, it could be spiked into patient plasma to control the complete sample preparation and amplification process. The assay detected 831 HIV-1 type B genome equivalents per ml of native plasma (95% confidence interval [CI], 759 to 936 HIV-1 B genome equivalents per ml) with a ≥95% probability of a positive result, as determined by probit regression analysis. A detection limit of 1,195 genome equivalents per ml of (individual) donor plasma (95% CI, 1,014 to 1,470 genome equivalents per ml of plasma pooled from individuals) was achieved when 96 samples were pooled and enriched by centrifugation. Up to 4,000 plasma samples per PCR run were tested in a 3-month trial period. Although data from the present pilot feasibility study will have to be complemented by a large clinical validation study, the assay is a promising approach to the high-throughput screening of blood donors and is the first noncommercial test for high-throughput screening for HIV-1. PMID:11724836

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

PubMed

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Fault Tolerance in Protein Interaction Networks: Stable Bipartite Subgraphs and Redundant Pathways

PubMed Central

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J.

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair. PMID:19399174

Transcriptome Profile Analysis of Breast Muscle Tissues from High or Low Levels of Atmospheric Ammonia Exposed Broilers (Gallus gallus)

PubMed Central

Sa, Renna; Zhong, Ruqing; Xing, Huan; Zhang, Hongfu

2016-01-01

Atmospheric ammonia is a common problem in poultry industry. High concentrations of aerial ammonia cause great harm to broilers' health and production. For the consideration of human health, the limit exposure concentration of ammonia in houses is set at 25 ppm. Previous reports have shown that 25 ppm is still detrimental to livestock, especially the gastrointestinal tract and respiratory tract, but the negative relationship between ammonia exposure and the tissue of breast muscle of broilers is still unknown. In the present study, 25 ppm ammonia in poultry houses was found to lower slaughter performance and breast yield. Then, high-throughput RNA sequencing was utilized to identify differentially expressed genes in breast muscle of broiler chickens exposed to high (25 ppm) or low (3 ppm) levels of atmospheric ammonia. The transcriptome analysis showed that 163 genes (fold change ≥ 2 or ≤ 0.5; P-value < 0.05) were differentially expressed between Ammonia25 (treatment group) and Ammonia3 (control group), including 96 down-regulated and 67 up-regulated genes. qRT-PCR analysis validated the transcriptomic results of RNA sequencing. Gene Ontology (GO) functional annotation analysis revealed potential genes, processes and pathways with putative involvement in growth and development inhibition of breast muscle in broilers caused by aerial ammonia exposure. This study facilitates understanding of the genetic architecture of the chicken breast muscle transcriptome, and has identified candidate genes for breast muscle response to atmospheric ammonia exposure. PMID:27611572

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

PubMed Central

2013-01-01

Background Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins. Results As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms. Conclusion Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/. PMID:23409969

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering.

PubMed

Groen, Nathalie; Guvendiren, Murat; Rabitz, Herschel; Welsh, William J; Kohn, Joachim; de Boer, Jan

2016-04-01

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. Copyright © 2016. Published by Elsevier Ltd.

Generalized Schemes for High Throughput Manipulation of the Desulfovibrio vulgaris Hildenborough Genome.

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

Chhabra, Swapnil; Butland, Gareth; Elias, Dwayne A

The ability to conduct advanced functional genomic studies of the thousands of 38 sequenced bacteria has been hampered by the lack of available tools for making high39 throughput chromosomal manipulations in a systematic manner that can be applied across 40 diverse species. In this work, we highlight the use of synthetic biological tools to 41 assemble custom suicide vectors with reusable and interchangeable DNA parts to 42 facilitate chromosomal modification at designated loci. These constructs enable an array 43 of downstream applications including gene replacement and creation of gene fusions with 44 affinity purification or localization tags. We employed thismore » approach to engineer 45 chromosomal modifications in a bacterium that has previously proven difficult to 46 manipulate genetically, Desulfovibrio vulgaris Hildenborough, to generate a library of 47 662 strains. Furthermore, we demonstrate how these modifications can be used for 48 examining metabolic pathways, protein-protein interactions, and protein localization. The 49 ubiquity of suicide constructs in gene replacement throughout biology suggests that this 50 approach can be applied to engineer a broad range of species for a diverse array of 51 systems biological applications and is amenable to high-throughput implementation.« less

RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer.

PubMed

Hou, Qi; Bing, Zhi-Tong; Hu, Cheng; Li, Mao-Yin; Yang, Ke-Hu; Mo, Zu; Xie, Xiang-Wei; Liao, Ji-Lin; Lu, Yan; Horie, Shigeo; Lou, Ming-Wu

2018-06-01

Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases. Copyright © 2018. Published by Elsevier B.V.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

PubMed Central

Gunasekara, Chathura; Zhang, Kui; Deng, Wenping; Brown, Laura

2018-01-01

Abstract Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories. PMID:29579312

Ultra-High Throughput Synthesis of Nanoparticles with Homogeneous Size Distribution Using a Coaxial Turbulent Jet Mixer

PubMed Central

2015-01-01

High-throughput production of nanoparticles (NPs) with controlled quality is critical for their clinical translation into effective nanomedicines for diagnostics and therapeutics. Here we report a simple and versatile coaxial turbulent jet mixer that can synthesize a variety of NPs at high throughput up to 3 kg/d, while maintaining the advantages of homogeneity, reproducibility, and tunability that are normally accessible only in specialized microscale mixing devices. The device fabrication does not require specialized machining and is easy to operate. As one example, we show reproducible, high-throughput formulation of siRNA-polyelectrolyte polyplex NPs that exhibit effective gene knockdown but exhibit significant dependence on batch size when formulated using conventional methods. The coaxial turbulent jet mixer can accelerate the development of nanomedicines by providing a robust and versatile platform for preparation of NPs at throughputs suitable for in vivo studies, clinical trials, and industrial-scale production. PMID:24824296

A High Throughput Model of Post-Traumatic Osteoarthritis using Engineered Cartilage Tissue Analogs

PubMed Central

Mohanraj, Bhavana; Meloni, Gregory R.; Mauck, Robert L.; Dodge, George R.

2014-01-01

(1) Objective A number of in vitro models of post-traumatic osteoarthritis (PTOA) have been developed to study the effect of mechanical overload on the processes that regulate cartilage degeneration. While such frameworks are critical for the identification therapeutic targets, existing technologies are limited in their throughput capacity. Here, we validate a test platform for high-throughput mechanical injury incorporating engineered cartilage. (2) Method We utilized a high throughput mechanical testing platform to apply injurious compression to engineered cartilage and determined their strain and strain rate dependent responses to injury. Next, we validated this response by applying the same injury conditions to cartilage explants. Finally, we conducted a pilot screen of putative PTOA therapeutic compounds. (3) Results Engineered cartilage response to injury was strain dependent, with a 2-fold increase in GAG loss at 75% compared to 50% strain. Extensive cell death was observed adjacent to fissures, with membrane rupture corroborated by marked increases in LDH release. Testing of established PTOA therapeutics showed that pan-caspase inhibitor (ZVF) was effective at reducing cell death, while the amphiphilic polymer (P188) and the free-radical scavenger (NAC) reduced GAG loss as compared to injury alone. (4) Conclusions The injury response in this engineered cartilage model replicated key features of the response from cartilage explants, validating this system for application of physiologically relevant injurious compression. This study establishes a novel tool for the discovery of mechanisms governing cartilage injury, as well as a screening platform for the identification of new molecules for the treatment of PTOA. PMID:24999113

Transcriptome analysis of the plateau fish (Triplophysa dalaica): Implications for adaptation to hypoxia in fishes.

PubMed

Wang, Ying; Yang, Liandong; Wu, Bo; Song, Zhaobin; He, Shunping

2015-07-10

Triplophysa dalaica, endemic species of Qinghai-Tibetan Plateau, is informative for understanding the genetic basis of adaptation to hypoxic conditions of high altitude habitats. Here, a comprehensive gene repertoire for this plateau fish was generated using the Illumina deep paired-end high-throughput sequencing technology. De novo assembly yielded 145, 256 unigenes with an average length of 1632 bp. Blast searches against GenBank non-redundant database annotated 74,594 (51.4%) unigenes encoding for 30,047 gene descriptions in T. dalaica. Functional annotation and classification of assembled sequences were performed using Gene Ontology (GO), clusters of euKaryotic Orthologous Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. After comparison with other fish transcriptomes, including silver carp (Hypophthalmichthys molitrix) and mud loach (Misgurnus anguillicaudatus), 2621 high-quality orthologous gene alignments were constructed among these species. 61 (2.3%) of the genes were identified as having undergone positive selection in the T. dalaica lineage. Within the positively selected genes, 13 genes were involved in hypoxia response, of which 11 were listed in HypoxiaDB. Furthermore, duplicated hif-α (hif-1αA/B and hif-2αA/B), EGLN1 and PPARA candidate genes involved in adaptation to hypoxia were identified in T. dalaica transcriptome. Branch-site model in PAML validated that hif-1αB and hif-2αA genes have undergone positive selection in T.dalaica. Finally, 37,501 simple sequence repeats (SSRs) and 19,497 high-quality single nucleotide polymorphisms (SNPs) were identified in T. dalaica. The identified SSR and SNP markers will facilitate the genetic structure, population geography and ecological studies of Triplophysa fishes. Copyright © 2015 Elsevier B.V. All rights reserved.

A Comprehensive Analysis of In Vitro and In Vivo Genetic Fitness of Pseudomonas aeruginosa Using High-Throughput Sequencing of Transposon Libraries

PubMed Central

Aschard, Hugues; Cattoir, Vincent; Yoder-Himes, Deborah; Lory, Stephen; Pier, Gerald B.

2013-01-01

High-throughput sequencing of transposon (Tn) libraries created within entire genomes identifies and quantifies the contribution of individual genes and operons to the fitness of organisms in different environments. We used insertion-sequencing (INSeq) to analyze the contribution to fitness of all non-essential genes in the chromosome of Pseudomonas aeruginosa strain PA14 based on a library of ∼300,000 individual Tn insertions. In vitro growth in LB provided a baseline for comparison with the survival of the Tn insertion strains following 6 days of colonization of the murine gastrointestinal tract as well as a comparison with Tn-inserts subsequently able to systemically disseminate to the spleen following induction of neutropenia. Sequencing was performed following DNA extraction from the recovered bacteria, digestion with the MmeI restriction enzyme that hydrolyzes DNA 16 bp away from the end of the Tn insert, and fractionation into oligonucleotides of 1,200–1,500 bp that were prepared for high-throughput sequencing. Changes in frequency of Tn inserts into the P. aeruginosa genome were used to quantify in vivo fitness resulting from loss of a gene. 636 genes had <10 sequencing reads in LB, thus defined as unable to grow in this medium. During in vivo infection there were major losses of strains with Tn inserts in almost all known virulence factors, as well as respiration, energy utilization, ion pumps, nutritional genes and prophages. Many new candidates for virulence factors were also identified. There were consistent changes in the recovery of Tn inserts in genes within most operons and Tn insertions into some genes enhanced in vivo fitness. Strikingly, 90% of the non-essential genes were required for in vivo survival following systemic dissemination during neutropenia. These experiments resulted in the identification of the P. aeruginosa strain PA14 genes necessary for optimal survival in the mucosal and systemic environments of a mammalian host. PMID:24039572

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties*

PubMed Central

Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

2013-01-01

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions. PMID:23422585

Deep Learning in Label-free Cell Classification

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

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

Multiplexed SNP genotyping using the Qbead™ system: a quantum dot-encoded microsphere-based assay

PubMed Central

Xu, Hongxia; Sha, Michael Y.; Wong, Edith Y.; Uphoff, Janet; Xu, Yanzhang; Treadway, Joseph A.; Truong, Anh; O’Brien, Eamonn; Asquith, Steven; Stubbins, Michael; Spurr, Nigel K.; Lai, Eric H.; Mahoney, Walt

2003-01-01

We have developed a new method using the Qbead™ system for high-throughput genotyping of single nucleotide polymorphisms (SNPs). The Qbead system employs fluorescent Qdot™ semiconductor nanocrystals, also known as quantum dots, to encode microspheres that subsequently can be used as a platform for multiplexed assays. By combining mixtures of quantum dots with distinct emission wavelengths and intensities, unique spectral ‘barcodes’ are created that enable the high levels of multiplexing required for complex genetic analyses. Here, we applied the Qbead system to SNP genotyping by encoding microspheres conjugated to allele-specific oligonucleotides. After hybridization of oligonucleotides to amplicons produced by multiplexed PCR of genomic DNA, individual microspheres are analyzed by flow cytometry and each SNP is distinguished by its unique spectral barcode. Using 10 model SNPs, we validated the Qbead system as an accurate and reliable technique for multiplexed SNP genotyping. By modifying the types of probes conjugated to microspheres, the Qbead system can easily be adapted to other assay chemistries for SNP genotyping as well as to other applications such as analysis of gene expression and protein–protein interactions. With its capability for high-throughput automation, the Qbead system has the potential to be a robust and cost-effective platform for a number of applications. PMID:12682378

Establishment and antitumor effects of dasatinib and PKI-587 in BD-138T, a patient-derived muscle invasive bladder cancer preclinical platform with concomitant EGFR amplification and PTEN deletion

PubMed Central

Lim, Joung Eun; Jeong, Da Eun; Song, Hye Jin; Kim, Sudong; Nam, Do-Hyun; Sung, Hyun Hwan; Jeong, Byong Chang; Seo, Seong Il; Jeon, Seong Soo; Lee, Hyun Moo; Choi, Han-Yong; Jeon, Hwang Gyun

2016-01-01

Muscle-invasive bladder cancer (MIBC) consists of a heterogeneous group of tumors with a high rate of metastasis and mortality. To facilitate the in-depth investigation and validation of tailored strategies for MIBC treatment, we have developed an integrated approach using advanced high-throughput drug screening and a clinically relevant patient-derived preclinical platform. We isolated patient-derived tumor cells (PDCs) from a rare MIBC case (BD-138T) that harbors concomitant epidermal growth factor receptor (EGFR) amplification and phosphatase and tensin homolog (PTEN) deletion. High-throughput in vitro drug screening demonstrated that dasatinib, a SRC inhibitor, and PKI-587, a dual PI3K/mTOR inhibitor, exhibited targeted anti-proliferative and pro-apoptotic effects against BD-138T PDCs. Using established patient-derived xenograft models that successfully retain the genomic and molecular characteristics of the parental tumor, we confirmed that these anti-tumor responses occurred through the inhibition of SRC and PI3K/AKT/mTOR signaling pathways. Taken together, these experimental results demonstrate that dasatinib and PKI-587 might serve as promising anticancer drug candidates for treating MIBC with combined EGFR gene amplification and PTEN deletion. PMID:27438149

Deep Learning in Label-free Cell Classification

DOE PAGES

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; ...

2016-03-15

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

High-throughput, luciferase-based reverse genetics systems for identifying inhibitors of Marburg and Ebola viruses.

PubMed

Uebelhoer, Luke S; Albariño, César G; McMullan, Laura K; Chakrabarti, Ayan K; Vincent, Joel P; Nichol, Stuart T; Towner, Jonathan S

2014-06-01

Marburg virus (MARV) and Ebola virus (EBOV), members of the family Filoviridae, represent a significant challenge to global public health. Currently, no licensed therapies exist to treat filovirus infections, which cause up to 90% mortality in human cases. To facilitate development of antivirals against these viruses, we established two distinct screening platforms based on MARV and EBOV reverse genetics systems that express secreted Gaussia luciferase (gLuc). The first platform is a mini-genome replicon to screen viral replication inhibitors using gLuc quantification in a BSL-2 setting. The second platform is complementary to the first and expresses gLuc as a reporter gene product encoded in recombinant infectious MARV and EBOV, thereby allowing for rapid quantification of viral growth during treatment with antiviral compounds. We characterized these viruses by comparing luciferase activity to virus production, and validated luciferase activity as an authentic real-time measure of viral growth. As proof of concept, we adapt both mini-genome and infectious virus platforms to high-throughput formats, and demonstrate efficacy of several antiviral compounds. We anticipate that both approaches will prove highly useful in the development of anti-filovirus therapies, as well as in basic research on the filovirus life cycle. Published by Elsevier B.V.

Establishment and antitumor effects of dasatinib and PKI-587 in BD-138T, a patient-derived muscle invasive bladder cancer preclinical platform with concomitant EGFR amplification and PTEN deletion.

PubMed

Chang, Nakho; Lee, Hye Won; Lim, Joung Eun; Jeong, Da Eun; Song, Hye Jin; Kim, Sudong; Nam, Do-Hyun; Sung, Hyun Hwan; Jeong, Byong Chang; Seo, Seong Il; Jeon, Seong Soo; Lee, Hyun Moo; Choi, Han-Yong; Jeon, Hwang Gyun

2016-08-09

Muscle-invasive bladder cancer (MIBC) consists of a heterogeneous group of tumors with a high rate of metastasis and mortality. To facilitate the in-depth investigation and validation of tailored strategies for MIBC treatment, we have developed an integrated approach using advanced high-throughput drug screening and a clinically relevant patient-derived preclinical platform. We isolated patient-derived tumor cells (PDCs) from a rare MIBC case (BD-138T) that harbors concomitant epidermal growth factor receptor (EGFR) amplification and phosphatase and tensin homolog (PTEN) deletion. High-throughput in vitro drug screening demonstrated that dasatinib, a SRC inhibitor, and PKI-587, a dual PI3K/mTOR inhibitor, exhibited targeted anti-proliferative and pro-apoptotic effects against BD-138T PDCs. Using established patient-derived xenograft models that successfully retain the genomic and molecular characteristics of the parental tumor, we confirmed that these anti-tumor responses occurred through the inhibition of SRC and PI3K/AKT/mTOR signaling pathways. Taken together, these experimental results demonstrate that dasatinib and PKI-587 might serve as promising anticancer drug candidates for treating MIBC with combined EGFR gene amplification and PTEN deletion.

Accurate, high-throughput typing of copy number variation using paralogue ratios from dispersed repeats

PubMed Central

Armour, John A. L.; Palla, Raquel; Zeeuwen, Patrick L. J. M.; den Heijer, Martin; Schalkwijk, Joost; Hollox, Edward J.

2007-01-01

Recent work has demonstrated an unexpected prevalence of copy number variation in the human genome, and has highlighted the part this variation may play in predisposition to common phenotypes. Some important genes vary in number over a high range (e.g. DEFB4, which commonly varies between two and seven copies), and have posed formidable technical challenges for accurate copy number typing, so that there are no simple, cheap, high-throughput approaches suitable for large-scale screening. We have developed a simple comparative PCR method based on dispersed repeat sequences, using a single pair of precisely designed primers to amplify products simultaneously from both test and reference loci, which are subsequently distinguished and quantified via internal sequence differences. We have validated the method for the measurement of copy number at DEFB4 by comparison of results from >800 DNA samples with copy number measurements by MAPH/REDVR, MLPA and array-CGH. The new Paralogue Ratio Test (PRT) method can require as little as 10 ng genomic DNA, appears to be comparable in accuracy to the other methods, and for the first time provides a rapid, simple and inexpensive method for copy number analysis, suitable for application to typing thousands of samples in large case-control association studies. PMID:17175532

High-Throughput Method for Ranking the Affinity of Peptide Ligands Selected from Phage Display Libraries

PubMed Central

González-Techera, A.; Umpiérrez-Failache, M.; Cardozo, S.; Obal, G.; Pritsch, O.; Last, J. A.; Gee, S. J.; Hammock, B. D.; González-Sapienza, G.

2010-01-01

The use of phage display peptide libraries allows rapid isolation of peptide ligands for any target selector molecule. However, due to differences in peptide expression and the heterogeneity of the phage preparations, there is no easy way to compare the binding properties of the selected clones, which operates as a major “bottleneck” of the technology. Here, we present the development of a new type of library that allows rapid comparison of the relative affinity of the selected peptides in a high-throughput screening format. As a model system, a phage display peptide library constructed on a phagemid vector that contains the bacterial alkaline phosphatase gene (BAP) was selected with an antiherbicide antibody. Due to the intrinsic switching capacity of the library, the selected peptides were transferred “en masse” from the phage coat protein to BAP. This was coupled to an optimized affinity ELISA where normalized amounts of the peptide–BAP fusion allow direct comparison of the binding properties of hundreds of peptide ligands. The system was validated by plasmon surface resonance experiments using synthetic peptides, showing that the method discriminates among the affinities of the peptides within 3 orders of magnitude. In addition, the peptide–BAP protein can find direct application as a tracer reagent. PMID:18393454

Development of a high-throughput microfluidic integrated microarray for the detection of chimeric bioweapons.

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

Sheppod, Timothy; Satterfield, Brent; Hukari, Kyle W.

2006-10-01

The advancement of DNA cloning has significantly augmented the potential threat of a focused bioweapon assault, such as a terrorist attack. With current DNA cloning techniques, toxin genes from the most dangerous (but environmentally labile) bacterial or viral organism can now be selected and inserted into robust organism to produce an infinite number of deadly chimeric bioweapons. In order to neutralize such a threat, accurate detection of the expressed toxin genes, rather than classification on strain or genealogical decent of these organisms, is critical. The development of a high-throughput microarray approach will enable the detection of unknowns chimeric bioweapons. Themore » development of a high-throughput microarray approach will enable the detection of unknown bioweapons. We have developed a unique microfluidic approach to capture and concentrate these threat genes (mRNA's) upto a 30 fold concentration. These captured oligonucleotides can then be used to synthesize in situ oligonucleotide copies (cDNA probes) of the captured genes. An integrated microfluidic architecture will enable us to control flows of reagents, perform clean-up steps and finally elute nanoliter volumes of synthesized oligonucleotides probes. The integrated approach has enabled a process where chimeric or conventional bioweapons can rapidly be identified based on their toxic function, rather than being restricted to information that may not identify the critical nature of the threat.« less

Design and construction of a first-generation high-throughput integrated robotic molecular biology platform for bioenergy applications.

PubMed

Hughes, Stephen R; Butt, Tauseef R; Bartolett, Scott; Riedmuller, Steven B; Farrelly, Philip

2011-08-01

The molecular biological techniques for plasmid-based assembly and cloning of gene open reading frames are essential for elucidating the function of the proteins encoded by the genes. High-throughput integrated robotic molecular biology platforms that have the capacity to rapidly clone and express heterologous gene open reading frames in bacteria and yeast and to screen large numbers of expressed proteins for optimized function are an important technology for improving microbial strains for biofuel production. The process involves the production of full-length complementary DNA libraries as a source of plasmid-based clones to express the desired proteins in active form for determination of their functions. Proteins that were identified by high-throughput screening as having desired characteristics are overexpressed in microbes to enable them to perform functions that will allow more cost-effective and sustainable production of biofuels. Because the plasmid libraries are composed of several thousand unique genes, automation of the process is essential. This review describes the design and implementation of an automated integrated programmable robotic workcell capable of producing complementary DNA libraries, colony picking, isolating plasmid DNA, transforming yeast and bacteria, expressing protein, and performing appropriate functional assays. These operations will allow tailoring microbial strains to use renewable feedstocks for production of biofuels, bioderived chemicals, fertilizers, and other coproducts for profitable and sustainable biorefineries. Published by Elsevier Inc.

Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review.

PubMed

Raddatz, Barbara B; Spitzbarth, Ingo; Matheis, Katja A; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang; Ulrich, Reiner

2017-09-01

High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

High-Throughput Functional Validation of Progression Drivers in Lung Adenocarcinoma

DTIC Science & Technology

2013-09-01

2) a novel molecular barcoding approach that facilitates cost- effective detection of driver events following in vitro and in vivo functional screens...aberration construction pipeline, which we named High-Throughput 3 Mutagenesis and Molecular Barcoding (HiTMMoB; Fig.1). We have therefore been able...lentiviral vector specially constructed for this project. This vector is compatible with our flexible molecular barcoding technology (Fig. 1), thus each

Custom Super-Resolution Microscope for the Structural Analysis of Nanostructures

DTIC Science & Technology

2018-05-29

research community. As part of our validation of the new design approach, we performed two - color imaging of pairs of adjacent oligo probes hybridized...nanostructures and biological targets. Our microscope features a large field of view and custom optics that facilitate 3D imaging and enhanced contrast in...our imaging throughput by creating two microscopy platforms for high-throughput, super-resolution materials characterization, with the AO set-up being

Reconstructing spatial organizations of chromosomes through manifold learning

PubMed Central

Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang

2018-01-01

Abstract Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data. PMID:29408992

Reconstructing spatial organizations of chromosomes through manifold learning.

PubMed

Zhu, Guangxiang; Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang

2018-05-04

Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data.

An XML-based interchange format for genotype-phenotype data.

PubMed

Whirl-Carrillo, M; Woon, M; Thorn, C F; Klein, T E; Altman, R B

2008-02-01

Recent advances in high-throughput genotyping and phenotyping have accelerated the creation of pharmacogenomic data. Consequently, the community requires standard formats to exchange large amounts of diverse information. To facilitate the transfer of pharmacogenomics data between databases and analysis packages, we have created a standard XML (eXtensible Markup Language) schema that describes both genotype and phenotype data as well as associated metadata. The schema accommodates information regarding genes, drugs, diseases, experimental methods, genomic/RNA/protein sequences, subjects, subject groups, and literature. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org) has used this XML schema for more than 5 years to accept and process submissions containing more than 1,814,139 SNPs on 20,797 subjects using 8,975 assays. Although developed in the context of pharmacogenomics, the schema is of general utility for exchange of genotype and phenotype data. We have written syntactic and semantic validators to check documents using this format. The schema and code for validation is available to the community at http://www.pharmgkb.org/schema/index.html (last accessed: 8 October 2007). (c) 2007 Wiley-Liss, Inc.

Discovery of sex-related genes through high-throughput transcriptome sequencing from the salmon louse Caligus rogercresseyi.

PubMed

Farlora, Rodolfo; Araya-Garay, José; Gallardo-Escárate, Cristian

2014-06-01

Understanding the molecular underpinnings involved in the reproduction of the salmon louse is critical for designing novel strategies of pest management for this ectoparasite. However, genomic information on sex-related genes is still limited. In the present work, sex-specific gene transcription was revealed in the salmon louse Caligus rogercresseyi using high-throughput Illumina sequencing. A total of 30,191,914 and 32,292,250 high quality reads were generated for females and males, and these were de novo assembled into 32,173 and 38,177 contigs, respectively. Gene ontology analysis showed a pattern of higher expression in the female as compared to the male transcriptome. Based on our sequence analysis and known sex-related proteins, several genes putatively involved in sex differentiation, including Dmrt3, FOXL2, VASA, and FEM1, and other potentially significant candidate genes in C. rogercresseyi, were identified for the first time. In addition, the occurrence of SNPs in several differentially expressed contigs annotating for sex-related genes was found. This transcriptome dataset provides a useful resource for future functional analyses, opening new opportunities for sea lice pest control. Copyright © 2014 Elsevier B.V. All rights reserved.

OSG-GEM: Gene Expression Matrix Construction Using the Open Science Grid.

PubMed

Poehlman, William L; Rynge, Mats; Branton, Chris; Balamurugan, D; Feltus, Frank A

2016-01-01

High-throughput DNA sequencing technology has revolutionized the study of gene expression while introducing significant computational challenges for biologists. These computational challenges include access to sufficient computer hardware and functional data processing workflows. Both these challenges are addressed with our scalable, open-source Pegasus workflow for processing high-throughput DNA sequence datasets into a gene expression matrix (GEM) using computational resources available to U.S.-based researchers on the Open Science Grid (OSG). We describe the usage of the workflow (OSG-GEM), discuss workflow design, inspect performance data, and assess accuracy in mapping paired-end sequencing reads to a reference genome. A target OSG-GEM user is proficient with the Linux command line and possesses basic bioinformatics experience. The user may run this workflow directly on the OSG or adapt it to novel computing environments.

OSG-GEM: Gene Expression Matrix Construction Using the Open Science Grid

PubMed Central

Poehlman, William L.; Rynge, Mats; Branton, Chris; Balamurugan, D.; Feltus, Frank A.

2016-01-01

High-throughput DNA sequencing technology has revolutionized the study of gene expression while introducing significant computational challenges for biologists. These computational challenges include access to sufficient computer hardware and functional data processing workflows. Both these challenges are addressed with our scalable, open-source Pegasus workflow for processing high-throughput DNA sequence datasets into a gene expression matrix (GEM) using computational resources available to U.S.-based researchers on the Open Science Grid (OSG). We describe the usage of the workflow (OSG-GEM), discuss workflow design, inspect performance data, and assess accuracy in mapping paired-end sequencing reads to a reference genome. A target OSG-GEM user is proficient with the Linux command line and possesses basic bioinformatics experience. The user may run this workflow directly on the OSG or adapt it to novel computing environments. PMID:27499617

High-Throughput Microfluidic Labyrinth for the Label-free Isolation of Circulating Tumor Cells.

PubMed

Lin, Eric; Rivera-Báez, Lianette; Fouladdel, Shamileh; Yoon, Hyeun Joong; Guthrie, Stephanie; Wieger, Jacob; Deol, Yadwinder; Keller, Evan; Sahai, Vaibhav; Simeone, Diane M; Burness, Monika L; Azizi, Ebrahim; Wicha, Max S; Nagrath, Sunitha

2017-09-27

We present "Labyrinth," a label-free microfluidic device to isolate circulating tumor cells (CTCs) using the combination of long loops and sharp corners to focus both CTCs and white blood cells (WBCs) at a high throughput of 2.5 mL/min. The high yield (>90%) and purity (600 WBCs/mL) of Labyrinth enabled us to profile gene expression in CTCs. As proof of principle, we used previously established cancer stem cell gene signatures to profile single cells isolated from the blood of breast cancer patients. We observed heterogeneous subpopulations of CTCs expressing genes for stem cells, epithelial cells, mesenchymal cells, and cells transitioning between epithelial and mesenchymal. Labyrinth offers a cell-surface marker-independent single-cell isolation platform to study heterogeneous CTC subpopulations. Copyright © 2017 Elsevier Inc. All rights reserved.

Phenotypic mutant library: potential for gene discovery

USDA-ARS?s Scientific Manuscript database

The rapid development of high throughput and affordable Next- Generation Sequencing (NGS) techniques has renewed interest in gene discovery using forward genetics. The conventional forward genetic approach starts with isolation of mutants with a phenotype of interest, mapping the mutation within a s...

Genome-wide retinal transcriptome analysis of endotoxin-induced uveitis in mice with next-generation sequencing

PubMed Central

Qiu, Yiguo; Yu, Peng; Lin, Ru; Fu, Xinyu; Hao, Bingtao

2017-01-01

Purpose Endotoxin-induced uveitis (EIU) is a well-established mouse model for studying human acute inflammatory uveitis. The purpose of this study is to investigate the genome-wide retinal transcriptome profile of EIU. Methods The anterior segment of the mice was examined with a slit-lamp, and clinical scores were evaluated simultaneously. The histological changes in the posterior segment of the eyes were evaluated with hematoxylin and eosin (H&E) staining. A high throughput RNA sequencing (RNA-seq) strategy using the Illumina Hiseq 2500 platform was applied to characterize the retinal transcriptome profile from lipopolysaccharide (LPS)-treated and untreated mice. The validation of the differentially expressed genes (DEGs) was analyzed with real-time PCR. Results At the 24th hour after challenge, the clinical score of the LPS group was significantly higher (3.83±0.75, mean ± standard deviation [SD]) than that of the control group (0.08±0.20, mean ± SD; p<0.001). The histological evaluation showed a large number of inflammatory cells infiltrated into the vitreous cavity in the LPS group compared with the control group. A total of 478 DEGs were identified with RNA-seq. Among these genes, 406 were upregulated and 72 were downregulated in the LPS group. Gene Ontology (GO) enrichment showed three significantly enriched upregulated terms. Twenty-one upregulated and seven downregulated pathways were remarkably enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven inflammatory response–, complement system–, fibrinolytic system–, and cell stress–related genes were validated to show similar results as the RNA-seq. Conclusions We first reported the retinal transcriptome profile of the EIU mouse with RNA-seq. The results indicate that the abnormal changes in the inflammatory response–, complement system–, fibrinolytic system–, and cell stress–related genes occurred concurrently in EIU. These genes may play an important role in the pathogenesis of EIU. This study will lead to a better understanding of the underlying mechanisms and shed light on discovering novel therapeutic targets for ocular inflammation. PMID:28706439

Genome-wide retinal transcriptome analysis of endotoxin-induced uveitis in mice with next-generation sequencing.

PubMed

Qiu, Yiguo; Yu, Peng; Lin, Ru; Fu, Xinyu; Hao, Bingtao; Lei, Bo

2017-01-01

Endotoxin-induced uveitis (EIU) is a well-established mouse model for studying human acute inflammatory uveitis. The purpose of this study is to investigate the genome-wide retinal transcriptome profile of EIU. The anterior segment of the mice was examined with a slit-lamp, and clinical scores were evaluated simultaneously. The histological changes in the posterior segment of the eyes were evaluated with hematoxylin and eosin (H&E) staining. A high throughput RNA sequencing (RNA-seq) strategy using the Illumina Hiseq 2500 platform was applied to characterize the retinal transcriptome profile from lipopolysaccharide (LPS)-treated and untreated mice. The validation of the differentially expressed genes (DEGs) was analyzed with real-time PCR. At the 24th hour after challenge, the clinical score of the LPS group was significantly higher (3.83±0.75, mean ± standard deviation [SD]) than that of the control group (0.08±0.20, mean ± SD; p<0.001). The histological evaluation showed a large number of inflammatory cells infiltrated into the vitreous cavity in the LPS group compared with the control group. A total of 478 DEGs were identified with RNA-seq. Among these genes, 406 were upregulated and 72 were downregulated in the LPS group. Gene Ontology (GO) enrichment showed three significantly enriched upregulated terms. Twenty-one upregulated and seven downregulated pathways were remarkably enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven inflammatory response-, complement system-, fibrinolytic system-, and cell stress-related genes were validated to show similar results as the RNA-seq. We first reported the retinal transcriptome profile of the EIU mouse with RNA-seq. The results indicate that the abnormal changes in the inflammatory response-, complement system-, fibrinolytic system-, and cell stress-related genes occurred concurrently in EIU. These genes may play an important role in the pathogenesis of EIU. This study will lead to a better understanding of the underlying mechanisms and shed light on discovering novel therapeutic targets for ocular inflammation.

Alternative polyadenylation of tumor suppressor genes in small intestinal neuroendocrine tumors.

PubMed

Rehfeld, Anders; Plass, Mireya; Døssing, Kristina; Knigge, Ulrich; Kjær, Andreas; Krogh, Anders; Friis-Hansen, Lennart

2014-01-01

The tumorigenesis of small intestinal neuroendocrine tumors (SI-NETs) is poorly understood. Recent studies have associated alternative polyadenylation (APA) with proliferation, cell transformation, and cancer. Polyadenylation is the process in which the pre-messenger RNA is cleaved at a polyA site and a polyA tail is added. Genes with two or more polyA sites can undergo APA. This produces two or more distinct mRNA isoforms with different 3' untranslated regions. Additionally, APA can also produce mRNAs containing different 3'-terminal coding regions. Therefore, APA alters both the repertoire and the expression level of proteins. Here, we used high-throughput sequencing data to map polyA sites and characterize polyadenylation genome-wide in three SI-NETs and a reference sample. In the tumors, 16 genes showed significant changes of APA pattern, which lead to either the 3' truncation of mRNA coding regions or 3' untranslated regions. Among these, 11 genes had been previously associated with cancer, with 4 genes being known tumor suppressors: DCC, PDZD2, MAGI1, and DACT2. We validated the APA in three out of three cases with quantitative real-time-PCR. Our findings suggest that changes of APA pattern in these 16 genes could be involved in the tumorigenesis of SI-NETs. Furthermore, they also point to APA as a new target for both diagnostic and treatment of SI-NETs. The identified genes with APA specific to the SI-NETs could be further tested as diagnostic markers and drug targets for disease prevention and treatment.

Alternative Polyadenylation of Tumor Suppressor Genes in Small Intestinal Neuroendocrine Tumors

PubMed Central

Rehfeld, Anders; Plass, Mireya; Døssing, Kristina; Knigge, Ulrich; Kjær, Andreas; Krogh, Anders; Friis-Hansen, Lennart

2014-01-01

The tumorigenesis of small intestinal neuroendocrine tumors (SI-NETs) is poorly understood. Recent studies have associated alternative polyadenylation (APA) with proliferation, cell transformation, and cancer. Polyadenylation is the process in which the pre-messenger RNA is cleaved at a polyA site and a polyA tail is added. Genes with two or more polyA sites can undergo APA. This produces two or more distinct mRNA isoforms with different 3′ untranslated regions. Additionally, APA can also produce mRNAs containing different 3′-terminal coding regions. Therefore, APA alters both the repertoire and the expression level of proteins. Here, we used high-throughput sequencing data to map polyA sites and characterize polyadenylation genome-wide in three SI-NETs and a reference sample. In the tumors, 16 genes showed significant changes of APA pattern, which lead to either the 3′ truncation of mRNA coding regions or 3′ untranslated regions. Among these, 11 genes had been previously associated with cancer, with 4 genes being known tumor suppressors: DCC, PDZD2, MAGI1, and DACT2. We validated the APA in three out of three cases with quantitative real-time-PCR. Our findings suggest that changes of APA pattern in these 16 genes could be involved in the tumorigenesis of SI-NETs. Furthermore, they also point to APA as a new target for both diagnostic and treatment of SI-NETs. The identified genes with APA specific to the SI-NETs could be further tested as diagnostic markers and drug targets for disease prevention and treatment. PMID:24782827

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...

High-throughput genotyping-by-sequencing facilitates molecular tagging of a novel rust resistance gene, R15, in sunflower (Helianthus annuus L.)

USDA-ARS?s Scientific Manuscript database

The rust virulence gene is co-evolving with the resistance gene in sunflower, leading to the emergence of new physiologic pathotypes. This presents a continuous threat to the sunflower crop necessitating the development of resistant sunflower hybrids providing a more efficient, durable, and environm...

A novel approach for data integration and disease subtyping

PubMed Central

Tagett, Rebecca; Diaz, Diana

2017-01-01

Advances in high-throughput technologies allow for measurements of many types of omics data, yet the meaningful integration of several different data types remains a significant challenge. Another important and difficult problem is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. Here we present a novel approach, called perturbation clustering for data integration and disease subtyping (PINS), which is able to address both challenges. The framework has been validated on thousands of cancer samples, using gene expression, DNA methylation, noncoding microRNA, and copy number variation data available from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. This simultaneous subtyping approach accurately identifies known cancer subtypes and novel subgroups of patients with significantly different survival profiles. The results were obtained from genome-scale molecular data without any other type of prior knowledge. The approach is sufficiently general to replace existing unsupervised clustering approaches outside the scope of bio-medical research, with the additional ability to integrate multiple types of data. PMID:29066617

Multiplex real-time PCR assay for detection of pathogenic Vibrio parahaemolyticus strains.

PubMed

He, Peiyan; Chen, Zhongwen; Luo, Jianyong; Wang, Henghui; Yan, Yong; Chen, Lixia; Gao, Wenjie

2014-01-01

Foodborne disease caused by pathogenic Vibrio parahaemolyticus has become a serious public health problem in many countries. Rapid diagnosis and the identification of pathogenic V. parahaemolyticus are very important in the context of public health. In this study, an EvaGreen-based multiplex real-time PCR assay was established for the detection of pathogenic V. parahaemolyticus. This assay targeted three genetic markers of V. parahaemolyticus (species-specific gene toxR and virulence genes tdh and trh). The assay could unambiguously identify pathogenic V. parahaemolyticus with a minimum detection limit of 1.4 pg genomic DNA per reaction (concentration giving a positive multiplex real-time PCR result in 95% of samples). The specificity of the assay was evaluated using 72 strains of V. parahaemolyticus and other bacteria. A validation of the assay with clinical samples confirmed its sensitivity and specificity. Our data suggest the newly established multiplex real-time PCR assay is practical, cost-effective, specific, sensitive and capable of high-throughput detection of pathogenic V. parahaemolyticus. Copyright © 2014. Published by Elsevier Ltd.

Strand-Specific RNA-Seq Analyses of Fruiting Body Development in Coprinopsis cinerea

DOE PAGES

Muraguchi, Hajime; Umezawa, Kiwamu; Niikura, Mai; ...

2015-10-28

We report that the basidiomycete fungus Coprinopsis cinerea is an important model system for multicellular development. Fruiting bodies of C. cinerea are typical mushrooms, which can be produced synchronously on defined media in the laboratory. To investigate the transcriptome in detail during fruiting body development, high-throughput sequencing (RNA-seq) was performed using cDNA libraries strand-specifically constructed from 13 points (stages/tissues) with two biological replicates. The reads were aligned to 14,245 predicted transcripts, and counted for forward and reverse transcripts. Differentially expressed genes (DEGs) between two adjacent points and between vegetative mycelium and each point were detected by Tag Count Comparison (TCC).more » To validate RNA-seq data, expression levels of selected genes were compared using RPKM values in RNA-seq data and qRT-PCR data, and DEGs detected in microarray data were examined in MA plots of RNA-seq data by TCC. We discuss events deduced from GO analysis of DEGs. In addition, we uncovered both transcription factor candidates and antisense transcripts that are likely to be involved in developmental regulation for fruiting.« less

Strand-Specific RNA-Seq Analyses of Fruiting Body Development in Coprinopsis cinerea

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

Muraguchi, Hajime; Umezawa, Kiwamu; Niikura, Mai

We report that the basidiomycete fungus Coprinopsis cinerea is an important model system for multicellular development. Fruiting bodies of C. cinerea are typical mushrooms, which can be produced synchronously on defined media in the laboratory. To investigate the transcriptome in detail during fruiting body development, high-throughput sequencing (RNA-seq) was performed using cDNA libraries strand-specifically constructed from 13 points (stages/tissues) with two biological replicates. The reads were aligned to 14,245 predicted transcripts, and counted for forward and reverse transcripts. Differentially expressed genes (DEGs) between two adjacent points and between vegetative mycelium and each point were detected by Tag Count Comparison (TCC).more » To validate RNA-seq data, expression levels of selected genes were compared using RPKM values in RNA-seq data and qRT-PCR data, and DEGs detected in microarray data were examined in MA plots of RNA-seq data by TCC. We discuss events deduced from GO analysis of DEGs. In addition, we uncovered both transcription factor candidates and antisense transcripts that are likely to be involved in developmental regulation for fruiting.« less

Fine-Scale Variation and Genetic Determinants of Alternative Splicing across Individuals

PubMed Central

Coulombe-Huntington, Jasmin; Lam, Kevin C. L.; Dias, Christel; Majewski, Jacek

2009-01-01

Recently, thanks to the increasing throughput of new technologies, we have begun to explore the full extent of alternative pre–mRNA splicing (AS) in the human transcriptome. This is unveiling a vast layer of complexity in isoform-level expression differences between individuals. We used previously published splicing sensitive microarray data from lymphoblastoid cell lines to conduct an in-depth analysis on splicing efficiency of known and predicted exons. By combining publicly available AS annotation with a novel algorithm designed to search for AS, we show that many real AS events can be detected within the usually unexploited, speculative majority of the array and at significance levels much below standard multiple-testing thresholds, demonstrating that the extent of cis-regulated differential splicing between individuals is potentially far greater than previously reported. Specifically, many genes show subtle but significant genetically controlled differences in splice-site usage. PCR validation shows that 42 out of 58 (72%) candidate gene regions undergo detectable AS, amounting to the largest scale validation of isoform eQTLs to date. Targeted sequencing revealed a likely causative SNP in most validated cases. In all 17 incidences where a SNP affected a splice-site region, in silico splice-site strength modeling correctly predicted the direction of the micro-array and PCR results. In 13 other cases, we identified likely causative SNPs disrupting predicted splicing enhancers. Using Fst and REHH analysis, we uncovered significant evidence that 2 putative causative SNPs have undergone recent positive selection. We verified the effect of five SNPs using in vivo minigene assays. This study shows that splicing differences between individuals, including quantitative differences in isoform ratios, are frequent in human populations and that causative SNPs can be identified using in silico predictions. Several cases affected disease-relevant genes and it is likely some of these differences are involved in phenotypic diversity and susceptibility to complex diseases. PMID:20011102

High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats.

PubMed

Zhou, Jizhong; He, Zhili; Yang, Yunfeng; Deng, Ye; Tringe, Susannah G; Alvarez-Cohen, Lisa

2015-01-27

Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied "open-format" and "closed-format" detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions. Copyright © 2015 Zhou et al.

High-Throughput Metagenomic Technologies for Complex Microbial Community Analysis: Open and Closed Formats

PubMed Central

He, Zhili; Yang, Yunfeng; Deng, Ye; Tringe, Susannah G.; Alvarez-Cohen, Lisa

2015-01-01

ABSTRACT   Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied “open-format” and “closed-format” detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions. PMID:25626903

High-throughput metagenomic technologies for complex microbial community analysis. Open and closed formats

DOE PAGES

Zhou, Jizhong; He, Zhili; Yang, Yunfeng; ...

2015-01-27

Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied “open-format” and “closed-format” detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications andmore » focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions.« less

High resolution array CGH and gene expression profiling of alveolar soft part sarcoma

PubMed Central

Selvarajah, Shamini; Pyne, Saumyadipta; Chen, Eleanor; Sompallae, Ramakrishna; Ligon, Azra H.; Nielsen, Gunnlaugur P.; Dranoff, Glenn; Stack, Edward; Loda, Massimo; Flavin, Richard

2014-01-01

Purpose Alveolar soft part sarcoma (ASPS) is a soft tissue sarcoma with poor prognosis, and little molecular evidence for its origin, initiation and progression. The aim of this study was to elucidate candidate molecular pathways involved in tumor pathogenesis. Experimental Design We employed high-throughput array comparative genomic hybridization and cDNA-Mediated Annealing, Selection, Ligation, and Extension Assay to profile the genomic and expression signatures of primary and metastatic ASPS from 17 tumors derived from 11 patients. We used an integrative bioinformatics approach to elucidate the molecular pathways associated with ASPS progression. Fluorescence in situ hybridization was performed to validate the presence of the t(X;17)(p11.2;q25) ASPL-TFE3 fusion and hence confirm the aCGH observations. Results FISH analysis identified the ASPL-TFE3 fusion in all cases. ArrayCGH revealed a higher number of numerical aberrations in metastatic tumors relative to primaries, but failed to identify consistent alterations in either group. Gene expression analysis highlighted 1,063 genes which were differentially expressed between the two groups. Gene set enrichment analysis identified 16 enriched gene sets (p < 0.1) associated with differentially expressed genes. Notable among these were several stem cell gene expression signatures and pathways related to differentiation. In particular, the paired box transcription factor PAX6 was up-regulated in the primary tumors, along with several genes whose mouse orthologs have previously been implicated in Pax6-DNA binding during neural stem cell differentiation. Conclusion In addition to suggesting a tentative neural line of differentiation for ASPS, these results implicate transcriptional deregulation from fusion genes in the pathogenesis of ASPS. PMID:24493828

Development and dissection of diagnostic SNP markers for the downy mildew resistance genes Pl Arg and Pl 8 and maker-assisted gene pyramiding in sunflower (Helianthus annuus L.).

PubMed

Qi, L L; Talukder, Z I; Hulke, B S; Foley, M E

2017-06-01

Diagnostic DNA markers are an invaluable resource in breeding programs for successful introgression and pyramiding of disease resistance genes. Resistance to downy mildew (DM) disease in sunflower is mediated by Pl genes which are known to be effective against the causal fungus, Plasmopara halstedii. Two DM resistance genes, Pl Arg and Pl 8 , are highly effective against P. halstedii races in the USA, and have been previously mapped to the sunflower linkage groups (LGs) 1 and 13, respectively, using simple sequence repeat (SSR) markers. In this study, we developed high-density single nucleotide polymorphism (SNP) maps encompassing the Pl arg and Pl 8 genes and identified diagnostic SNP markers closely linked to these genes. The specificity of the diagnostic markers was validated in a highly diverse panel of 548 sunflower lines. Dissection of a large marker cluster co-segregated with Pl Arg revealed that the closest SNP markers NSA_007595 and NSA_001835 delimited Pl Arg to an interval of 2.83 Mb on the LG1 physical map. The SNP markers SFW01497 and SFW06597 delimited Pl 8 to an interval of 2.85 Mb on the LG13 physical map. We also developed sunflower lines with homozygous, three gene pyramids carrying Pl Arg , Pl 8 , and the sunflower rust resistance gene R 12 using the linked SNP markers from a segregating F 2 population of RHA 340 (carrying Pl 8 )/RHA 464 (carrying Pl Arg and R 12 ). The high-throughput diagnostic SNP markers developed in this study will facilitate marker-assisted selection breeding, and the pyramided sunflower lines will provide durable resistance to downy mildew and rust diseases.

A gene expression resource generated by genome-wide lacZ profiling in the mouse

PubMed Central

Tuck, Elizabeth; Estabel, Jeanne; Oellrich, Anika; Maguire, Anna Karin; Adissu, Hibret A.; Souter, Luke; Siragher, Emma; Lillistone, Charlotte; Green, Angela L.; Wardle-Jones, Hannah; Carragher, Damian M.; Karp, Natasha A.; Smedley, Damian; Adams, Niels C.; Bussell, James N.; Adams, David J.; Ramírez-Solis, Ramiro; Steel, Karen P.; Galli, Antonella; White, Jacqueline K.

2015-01-01

ABSTRACT Knowledge of the expression profile of a gene is a critical piece of information required to build an understanding of the normal and essential functions of that gene and any role it may play in the development or progression of disease. High-throughput, large-scale efforts are on-going internationally to characterise reporter-tagged knockout mouse lines. As part of that effort, we report an open access adult mouse expression resource, in which the expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter gene. Many specific and informative expression patterns were noted. Expression was most commonly observed in the testis and brain and was most restricted in white adipose tissue and mammary gland. Over half of the assessed genes presented with an absent or localised expression pattern (categorised as 0-10 positive structures). A link between complexity of expression profile and viability of homozygous null animals was observed; inactivation of genes expressed in ≥21 structures was more likely to result in reduced viability by postnatal day 14 compared with more restricted expression profiles. For validation purposes, this mouse expression resource was compared with Bgee, a federated composite of RNA-based expression data sets. Strong agreement was observed, indicating a high degree of specificity in our data. Furthermore, there were 1207 observations of expression of a particular gene in an anatomical structure where Bgee had no data, indicating a large amount of novelty in our data set. Examples of expression data corroborating and extending genotype-phenotype associations and supporting disease gene candidacy are presented to demonstrate the potential of this powerful resource. PMID:26398943

Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

PubMed Central

Jia, Peilin; Wang, Lily; Fanous, Ayman H.; Pato, Carlos N.; Edwards, Todd L.; Zhao, Zhongming

2012-01-01

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P meta<1×10−4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available. PMID:22792057

Rapid analysis of aminoglycoside antibiotics in bovine tissues using disposable pipette extraction and ultrahigh performance liquid chromatography - tandem mass spectrometry

USDA-ARS?s Scientific Manuscript database

A high-throughput qualitative screening and identification method for 9 aminoglycosides of regulatory interest has been developed, validated, and implemented for bovine kidney, liver, and muscle tissues. The method involves extraction at previously validated conditions, cleanup using disposable pip...

Characterization of three human cell line models for high-throughput neuronal cytotoxicity screening.

PubMed

Tong, Zhi-Bin; Hogberg, Helena; Kuo, David; Sakamuru, Srilatha; Xia, Menghang; Smirnova, Lena; Hartung, Thomas; Gerhold, David

2017-02-01

More than 75 000 man-made chemicals contaminate the environment; many of these have not been tested for toxicities. These chemicals demand quantitative high-throughput screening assays to assess them for causative roles in neurotoxicities, including Parkinson's disease and other neurodegenerative disorders. To facilitate high throughput screening for cytotoxicity to neurons, three human neuronal cellular models were compared: SH-SY5Y neuroblastoma cells, LUHMES conditionally-immortalized dopaminergic neurons, and Neural Stem Cells (NSC) derived from human fetal brain. These three cell lines were evaluated for rapidity and degree of differentiation, and sensitivity to 32 known or candidate neurotoxicants. First, expression of neural differentiation genes was assayed during a 7-day differentiation period. Of the three cell lines, LUHMES showed the highest gene expression of neuronal markers after differentiation. Both in the undifferentiated state and after 7 days of neuronal differentiation, LUHMES cells exhibited greater cytotoxic sensitivity to most of 32 suspected or known neurotoxicants than SH-SY5Y or NSCs. LUHMES cells were also unique in being more susceptible to several compounds in the differentiating state than in the undifferentiated state; including known neurotoxicants colchicine, methyl-mercury (II), and vincristine. Gene expression results suggest that differentiating LUHMES cells may be susceptible to apoptosis because they express low levels of anti-apoptotic genes BCL2 and BIRC5/survivin, whereas SH-SY5Y cells may be resistant to apoptosis because they express high levels of BCL2, BIRC5/survivin, and BIRC3 genes. Thus, LUHMES cells exhibited favorable characteristics for neuro-cytotoxicity screening: rapid differentiation into neurons that exhibit high level expression neuronal marker genes, and marked sensitivity of LUHMES cells to known neurotoxicants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The Representation of Heart Development in the Gene Ontology

PubMed Central

Khodiyar, Varsha K.; Hill, David P.; Howe, Doug; Berardini, Tanya Z.; Tweedie, Susan; Talmud, Philippa J.; Breckenridge, Ross; Bhattarcharya, Shoumo; Riley, Paul; Scambler, Peter; Lovering, Ruth C.

2012-01-01

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling. In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development and aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject. The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area. PMID:21419760

miREE: miRNA recognition elements ensemble

PubMed Central

2011-01-01

Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/ PMID:22115078

High-Throughput Sequencing Reveals Differential Expression of miRNAs in Intestine from Sea Cucumber during Aestivation

PubMed Central

Chen, Muyan; Zhang, Xiumei; Liu, Jianning; Storey, Kenneth B.

2013-01-01

The regulatory role of miRNA in gene expression is an emerging hot new topic in the control of hypometabolism. Sea cucumber aestivation is a complicated physiological process that includes obvious hypometabolism as evidenced by a decrease in the rates of oxygen consumption and ammonia nitrogen excretion, as well as a serious degeneration of the intestine into a very tiny filament. To determine whether miRNAs play regulatory roles in this process, the present study analyzed profiles of miRNA expression in the intestine of the sea cucumber (Apostichopus japonicus), using Solexa deep sequencing technology. We identified 308 sea cucumber miRNAs, including 18 novel miRNAs specific to sea cucumber. Animals sampled during deep aestivation (DA) after at least 15 days of continuous torpor, were compared with animals from a non-aestivation (NA) state (animals that had passed through aestivation and returned to the active state). We identified 42 differentially expressed miRNAs [RPM (reads per million) >10, |FC| (|fold change|) ≥1, FDR (false discovery rate) <0.01] during aestivation, which were validated by two other miRNA profiling methods: miRNA microarray and real-time PCR. Among the most prominent miRNA species, miR-200-3p, miR-2004, miR-2010, miR-22, miR-252a, miR-252a-3p and miR-92 were significantly over-expressed during deep aestivation compared with non-aestivation animals. Preliminary analyses of their putative target genes and GO analysis suggest that these miRNAs could play important roles in global transcriptional depression and cell differentiation during aestivation. High-throughput sequencing data and microarray data have been submitted to GEO database. PMID:24143179

Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data.

PubMed

Gallant, Andrew; Leiserson, Mark D M; Kachalov, Maxim; Cowen, Lenore J; Hescott, Benjamin J

2013-01-18

New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available. Genecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 18:1399-1409, 2011) for generating generalized BPMs from high-throughput genetic interaction data. Given a matrix of weighted epistasis values for a set of double knock-outs, Genecentric returns a list of generalized BPMs that may represent compensatory pathways. Genecentric also has an extension, GenecentricGO, to query FuncAssociate (Bioinformatics 25:3043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation. We find that Genecentric can be used to find coherent functional and perhaps compensatory gene sets from high throughput genetic interaction data. Genecentric is made freely available for download under the GPLv2 from http://bcb.cs.tufts.edu/genecentric.

Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data

PubMed Central

2013-01-01

Background New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available. Results Genecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 18:1399-1409, 2011) for generating generalized BPMs from high-throughput genetic interaction data. Given a matrix of weighted epistasis values for a set of double knock-outs, Genecentric returns a list of generalized BPMs that may represent compensatory pathways. Genecentric also has an extension, GenecentricGO, to query FuncAssociate (Bioinformatics 25:3043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation. Conclusion We find that Genecentric can be used to find coherent functional and perhaps compensatory gene sets from high throughput genetic interaction data. Genecentric is made freely available for download under the GPLv2 from http://bcb.cs.tufts.edu/genecentric. PMID:23331614

Prediction of in vivo hepatotoxicity effects using in vitro transcriptomics data (SOT)

EPA Science Inventory

High-throughput in vitro transcriptomics data support molecular understanding of chemical-induced toxicity. Here, we evaluated the utility of such data to predict liver toxicity. First, in vitro gene expression data for 93 genes was generated following exposure of metabolically c...

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.

High throughput sequencing identifies chilling responsive genes in sweetpotato (Ipomoea batatas Lam.) during storage.

PubMed

Xie, Zeyi; Zhou, Zhilin; Li, Hongmin; Yu, Jingjing; Jiang, Jiaojiao; Tang, Zhonghou; Ma, Daifu; Zhang, Baohong; Han, Yonghua; Li, Zongyun

2018-05-21

Sweetpotato (Ipomoea batatas L.) is a globally important economic food crop. It belongs to Convolvulaceae family and origins in the tropics; however, sweetpotato is sensitive to cold stress during storage. In this study, we performed transcriptome sequencing to investigate the sweetpotato response to chilling stress during storage. A total of 110,110 unigenes were generated via high-throughput sequencing. Differentially expressed genes (DEGs) analysis showed that 18,681 genes were up-regulated and 21,983 genes were down-regulated in low temperature condition. Many DEGs were related to the cell membrane system, antioxidant enzymes, carbohydrate metabolism, and hormone metabolism, which are potentially associated with sweetpotato resistance to low temperature. The existence of DEGs suggests a molecular basis for the biochemical and physiological consequences of sweetpotato in low temperature storage conditions. Our analysis will provide a new target for enhancement of sweetpotato cold stress tolerance in postharvest storage through genetic manipulation. Copyright © 2018. Published by Elsevier Inc.

The Gene Expression Omnibus Database.

PubMed

Clough, Emily; Barrett, Tanya

2016-01-01

The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.

Design and engineering of intracellular-metabolite-sensing/regulation gene circuits in Saccharomyces cerevisiae.

PubMed

Wang, Meng; Li, Sijin; Zhao, Huimin

2016-01-01

The development of high-throughput phenotyping tools is lagging far behind the rapid advances of genotype generation methods. To bridge this gap, we report a new strategy for design, construction, and fine-tuning of intracellular-metabolite-sensing/regulation gene circuits by repurposing bacterial transcription factors and eukaryotic promoters. As proof of concept, we systematically investigated the design and engineering of bacterial repressor-based xylose-sensing/regulation gene circuits in Saccharomyces cerevisiae. We demonstrated that numerous properties, such as induction ratio and dose-response curve, can be fine-tuned at three different nodes, including repressor expression level, operator position, and operator sequence. By applying these gene circuits, we developed a cell sorting based, rapid and robust high-throughput screening method for xylose transporter engineering and obtained a sugar transporter HXT14 mutant with 6.5-fold improvement in xylose transportation capacity. This strategy should be generally applicable and highly useful for evolutionary engineering of proteins, pathways, and genomes in S. cerevisiae. © 2015 Wiley Periodicals, Inc.

The Gene Expression Omnibus database

PubMed Central

Clough, Emily; Barrett, Tanya

2016-01-01

The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome–protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/. PMID:27008011

Automated solid-phase subcloning based on beads brought into proximity by magnetic force.

PubMed

Hudson, Elton P; Nikoshkov, Andrej; Uhlen, Mathias; Rockberg, Johan

2012-01-01

In the fields of proteomics, metabolic engineering and synthetic biology there is a need for high-throughput and reliable cloning methods to facilitate construction of expression vectors and genetic pathways. Here, we describe a new approach for solid-phase cloning in which both the vector and the gene are immobilized to separate paramagnetic beads and brought into proximity by magnetic force. Ligation events were directly evaluated using fluorescent-based microscopy and flow cytometry. The highest ligation efficiencies were obtained when gene- and vector-coated beads were brought into close contact by application of a magnet during the ligation step. An automated procedure was developed using a laboratory workstation to transfer genes into various expression vectors and more than 95% correct clones were obtained in a number of various applications. The method presented here is suitable for efficient subcloning in an automated manner to rapidly generate a large number of gene constructs in various vectors intended for high throughput applications.

Automated Solid-Phase Subcloning Based on Beads Brought into Proximity by Magnetic Force

PubMed Central

Hudson, Elton P.; Nikoshkov, Andrej; Uhlen, Mathias; Rockberg, Johan

2012-01-01

In the fields of proteomics, metabolic engineering and synthetic biology there is a need for high-throughput and reliable cloning methods to facilitate construction of expression vectors and genetic pathways. Here, we describe a new approach for solid-phase cloning in which both the vector and the gene are immobilized to separate paramagnetic beads and brought into proximity by magnetic force. Ligation events were directly evaluated using fluorescent-based microscopy and flow cytometry. The highest ligation efficiencies were obtained when gene- and vector-coated beads were brought into close contact by application of a magnet during the ligation step. An automated procedure was developed using a laboratory workstation to transfer genes into various expression vectors and more than 95% correct clones were obtained in a number of various applications. The method presented here is suitable for efficient subcloning in an automated manner to rapidly generate a large number of gene constructs in various vectors intended for high throughput applications. PMID:22624028

Transcriptional profiling of TLR-4/7/8-stimulated guinea pig splenocytes and whole blood by bDNA assay.

PubMed

Ching, Lance K; Mompoint, Farah; Guderian, Jeffrey A; Picone, Alex; Orme, Ian M; Coler, Rhea N; Reed, Steven G; Baldwin, Susan L

2011-10-28

Toll-like receptor (TLR) agonists are currently being examined as adjuvants for vaccines, with several lead candidates now in licensed products or in late-stage clinical development. Guinea pigs are widely used for preclinical testing of drugs and vaccines; however, evaluation of TLR agonists in this model is hindered by the limited availability of immunological tools and reagents. In this study, we validated the use of a branched-chain DNA (bDNA) assay known as the QuantiGene Plex 2.0 Reagent System for measuring innate cytokine and chemokine mRNA levels following TLR stimulation of guinea pig cells. Gene expression for T-helper-1 (Th1) polarizing cytokines (TNF-α, IL-1β, IL-12) and chemokines (CXCL1, CCL2) was upregulated following ex vivo stimulation of guinea pig splenocytes and whole blood with TLR-4 or TLR-7/8 agonists. These data confirm the utility of the QuantiGene system both as an alternative to RT-PCR for measuring transcript levels and as a high-throughput screening tool for dissecting the immunological response to TLR stimulation in guinea pigs. Overall, the QuantiGene platform is reliable, reproducible, and sensitive. These agonists have the potential to be used as adjuvant components in vaccines against various pathogens. Copyright © 2011 Elsevier B.V. All rights reserved.

Transcriptomic response and perturbation of toxicity pathways in zebrafish larvae after exposure to graphene quantum dots (GQDs).

PubMed

Deng, Shun; Jia, Pan-Pan; Zhang, Jing-Hui; Junaid, Muhammad; Niu, Aping; Ma, Yan-Bo; Fu, Ailing; Pei, De-Sheng

2018-05-29

Graphene quantum dots (GQDs) are widely used for biomedical applications. Previously, the low-level toxicity of GQDs in vivo and in vitro has been elucidated, but the underlying molecular mechanisms remained largely unknown. Here, we employed the Illumina high-throughput RNA-sequencing to explore the whole-transcriptome profiling of zebrafish larvae after exposure to GQDs. Comparative transcriptome analysis identified 2116 differentially expressed genes between GQDs exposed groups and control. Functional classification demonstrated that a large proportion of genes involved in acute inflammatory responses and detoxifying process were significantly up-regulated by GQDs. The inferred gene regulatory network suggested that activator protein 1 (AP-1) was the early-response transcription factor in the linkage of a cascade of downstream (pro-) inflammatory signals with the apoptosis signals. Moreover, hierarchical signaling threshold determined the high sensitivity of complement system in zebrafish when exposed to the sublethal dose of GQDs. Further, 35 candidate genes from various signaling pathways were further validated by qPCR after exposure to 25, 50, and 100 μg/mL of GQDs. Taken together, our study provided a valuable insight into the molecular mechanisms of potential bleeding risks and detoxifying processes in response to GQDs exposure, thereby establishing a mechanistic basis for the biosafety evaluation of GQDs. Copyright © 2018 Elsevier B.V. All rights reserved.

Transfer RNA Derived Small RNAs Targeting Defense Responsive Genes Are Induced during Phytophthora capsici Infection in Black Pepper (Piper nigrum L.)

PubMed Central

Asha, Srinivasan; Soniya, Eppurath V.

2016-01-01

Small RNAs derived from transfer RNAs were recently assigned as potential gene regulatory candidates for various stress responses in eukaryotes. In this study, we report on the cloning and identification of tRNA derived small RNAs from black pepper plants in response to the infection of the quick wilt pathogen, Phytophthora capsici. 5′tRFs cloned from black pepper were validated as highly expressed during P. capsici infection. A high-throughput systematic analysis of the small RNAome (sRNAome) revealed the predominance of 5′tRFs in the infected leaf and root. The abundance of 5′tRFs in the sRNAome and the defense responsive genes as their potential targets indicated their regulatory role during stress response in black pepper. The 5′AlaCGC tRF mediated cleavage was experimentally mapped at the tRF binding sites on the mRNA targets of Non-expresser of pathogenesis related protein (NPR1), which was down-regulated during pathogen infection. Comparative sRNAome further demonstrated sequence conservation of 5′Ala tRFs across the angiosperm plant groups, and many important genes in the defense response were identified in silico as their potential targets. Our findings uncovered the diversity, differential expression and stress responsive functional role of tRNA-derived small RNAs during Phytophthora infection in black pepper. PMID:27313593

Transfer RNA Derived Small RNAs Targeting Defense Responsive Genes Are Induced during Phytophthora capsici Infection in Black Pepper (Piper nigrum L.).

PubMed

Asha, Srinivasan; Soniya, Eppurath V

2016-01-01

Small RNAs derived from transfer RNAs were recently assigned as potential gene regulatory candidates for various stress responses in eukaryotes. In this study, we report on the cloning and identification of tRNA derived small RNAs from black pepper plants in response to the infection of the quick wilt pathogen, Phytophthora capsici. 5'tRFs cloned from black pepper were validated as highly expressed during P. capsici infection. A high-throughput systematic analysis of the small RNAome (sRNAome) revealed the predominance of 5'tRFs in the infected leaf and root. The abundance of 5'tRFs in the sRNAome and the defense responsive genes as their potential targets indicated their regulatory role during stress response in black pepper. The 5'Ala(CGC) tRF mediated cleavage was experimentally mapped at the tRF binding sites on the mRNA targets of Non-expresser of pathogenesis related protein (NPR1), which was down-regulated during pathogen infection. Comparative sRNAome further demonstrated sequence conservation of 5'Ala tRFs across the angiosperm plant groups, and many important genes in the defense response were identified in silico as their potential targets. Our findings uncovered the diversity, differential expression and stress responsive functional role of tRNA-derived small RNAs during Phytophthora infection in black pepper.

Natural Allelic Diversity, Genetic Structure and Linkage Disequilibrium Pattern in Wild Chickpea

PubMed Central

Kujur, Alice; Das, Shouvik; Badoni, Saurabh; Kumar, Vinod; Singh, Mohar; Bansal, Kailash C.; Tyagi, Akhilesh K.; Parida, Swarup K.

2014-01-01

Characterization of natural allelic diversity and understanding the genetic structure and linkage disequilibrium (LD) pattern in wild germplasm accessions by large-scale genotyping of informative microsatellite and single nucleotide polymorphism (SNP) markers is requisite to facilitate chickpea genetic improvement. Large-scale validation and high-throughput genotyping of genome-wide physically mapped 478 genic and genomic microsatellite markers and 380 transcription factor gene-derived SNP markers using gel-based assay, fluorescent dye-labelled automated fragment analyser and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass array have been performed. Outcome revealed their high genotyping success rate (97.5%) and existence of a high level of natural allelic diversity among 94 wild and cultivated Cicer accessions. High intra- and inter-specific polymorphic potential and wider molecular diversity (11–94%) along with a broader genetic base (13–78%) specifically in the functional genic regions of wild accessions was assayed by mapped markers. It suggested their utility in monitoring introgression and transferring target trait-specific genomic (gene) regions from wild to cultivated gene pool for the genetic enhancement. Distinct species/gene pool-wise differentiation, admixed domestication pattern, and differential genome-wide recombination and LD estimates/decay observed in a six structured population of wild and cultivated accessions using mapped markers further signifies their usefulness in chickpea genetics, genomics and breeding. PMID:25222488

Directly transforming PCR-amplified DNA fragments into plant cells is a versatile system that facilitates the transient expression assay.

PubMed

Lu, Yuming; Chen, Xi; Wu, Yuxuan; Wang, Yanping; He, Yuqing; Wu, Yan

2013-01-01

A circular plasmid containing a gene coding sequence has been broadly used for studying gene regulation in cells. However, to accommodate a quick screen plasmid construction and preparation can be time consuming. Here we report a PCR amplified dsDNA fragments (PCR-fragments) based transient expression system (PCR-TES) for suiting in the study of gene regulation in plant cells. Instead of transforming plasmids into plant cells, transient expression of PCR-fragments can be applicable. The transformation efficiency and expression property of PCR-fragments are comparable to transformation using plasmids. We analyzed the transformation efficiency in PCR-TES at transcription and protein levels. Our results indicate that the PCR-TES is as versatile as the conventional transformation system using plasmid DNA. Through reconstituting PYR1-mediated ABA signaling pathway in Arabidopsis mesophyll protoplasts, we were not only validating the practicality of PCR-TES but also screening potential candidates of CDPK family members which might be involved in the ABA signaling. Moreover, we determined that phosphorylation of ABF2 by CPK4 could be mediated by ABA-induced PYR1 and ABI1, demonstrating a crucial role of CDPKs in the ABA signaling. In summary, PCR-TES can be applicable to facilitate analyzing gene regulation and for the screen of putative regulatory molecules at the high throughput level in plant cells.

High-Throughput Sequencing Reveals Diverse Sets of Conserved, Nonconserved, and Species-Specific miRNAs in Jute

PubMed Central

Islam, Md. Tariqul; Ferdous, Ahlan Sabah; Najnin, Rifat Ara; Sarker, Suprovath Kumar; Khan, Haseena

2015-01-01

MicroRNAs play a pivotal role in regulating a broad range of biological processes, acting by cleaving mRNAs or by translational repression. A group of plant microRNAs are evolutionarily conserved; however, others are expressed in a species-specific manner. Jute is an agroeconomically important fibre crop; nonetheless, no practical information is available for microRNAs in jute to date. In this study, Illumina sequencing revealed a total of 227 known microRNAs and 17 potential novel microRNA candidates in jute, of which 164 belong to 23 conserved families and the remaining 63 belong to 58 nonconserved families. Among a total of 81 identified microRNA families, 116 potential target genes were predicted for 39 families and 11 targets were predicted for 4 among the 17 identified novel microRNAs. For understanding better the functions of microRNAs, target genes were analyzed by Gene Ontology and their pathways illustrated by KEGG pathway analyses. The presence of microRNAs identified in jute was validated by stem-loop RT-PCR followed by end point PCR and qPCR for randomly selected 20 known and novel microRNAs. This study exhaustively identifies microRNAs and their target genes in jute which will ultimately pave the way for understanding their role in this crop and other crops. PMID:25861616

Long non-coding RNA expression patterns in lung tissues of chronic cigarette smoke induced COPD mouse model.

PubMed

Zhang, Haiyun; Sun, Dejun; Li, Defu; Zheng, Zeguang; Xu, Jingyi; Liang, Xue; Zhang, Chenting; Wang, Sheng; Wang, Jian; Lu, Wenju

2018-05-15

Long non-coding RNAs (lncRNAs) have critical regulatory roles in protein-coding gene expression. Aberrant expression profiles of lncRNAs have been observed in various human diseases. In this study, we investigated transcriptome profiles in lung tissues of chronic cigarette smoke (CS)-induced COPD mouse model. We found that 109 lncRNAs and 260 mRNAs were significantly differential expressed in lungs of chronic CS-induced COPD mouse model compared with control animals. GO and KEGG analyses indicated that differentially expressed lncRNAs associated protein-coding genes were mainly involved in protein processing of endoplasmic reticulum pathway, and taurine and hypotaurine metabolism pathway. The combination of high throughput data analysis and the results of qRT-PCR validation in lungs of chronic CS-induced COPD mouse model, 16HBE cells with CSE treatment and PBMC from patients with COPD revealed that NR_102714 and its associated protein-coding gene UCHL1 might be involved in the development of COPD both in mouse and human. In conclusion, our study demonstrated that aberrant expression profiles of lncRNAs and mRNAs existed in lungs of chronic CS-induced COPD mouse model. From animal models perspective, these results might provide further clues to investigate biological functions of lncRNAs and their potential target protein-coding genes in the pathogenesis of COPD.

Prediction of in vivo hepatotoxicity effects using in vitro ...

EPA Pesticide Factsheets

High-throughput in vitro transcriptomics data support molecular understanding of chemical-induced toxicity. Here, we evaluated the utility of such data to predict liver toxicity. First, in vitro gene expression data for 93 genes was generated following exposure of metabolically competent HepaRG cells to 1060 environmental chemicals from the US EPA ToxCast library. The empirical relationship between these data and rat chronic liver endpoints from animal studies in the Toxicity Reference Database (ToxRefDB) was then evaluated using machine learning techniques. Chemicals were classified as positive (242) or negative (135) based on observed hepatic histopathologic effects, and divided into three categories: hypertrophy (183), injury (112) and proliferative lesions (101). Hepatotoxicants were classified on the basis of the bioactivity of 93 genes (descriptors) using six machine learning algorithms: linear discriminant analysis, naïve Bayes, support vector classification, classification and regression trees, k-nearest neighbors, and an ensemble of classifiers. Classification performance was evaluated using 10-fold cross-validation testing, and in-loop, filter-based, feature subset selection. The best balanced accuracy for prediction of hypertrophy, injury and proliferative lesions were 0.81 ± 0.07, 0.79 ± 0.08 and 0.77 ± 0.09, respectively. Gene specific perturbation of xenobiotic metabolism enzymes (CYP7A1/2E1/4A11/1A1/4A22) and transporters (ABCG2, ABCB11, SLC22

Directly Transforming PCR-Amplified DNA Fragments into Plant Cells Is a Versatile System That Facilitates the Transient Expression Assay

PubMed Central

Lu, Yuming; Chen, Xi; Wu, Yuxuan; Wang, Yanping; He, Yuqing; Wu, Yan

2013-01-01

A circular plasmid containing a gene coding sequence has been broadly used for studying gene regulation in cells. However, to accommodate a quick screen plasmid construction and preparation can be time consuming. Here we report a PCR amplified dsDNA fragments (PCR-fragments) based transient expression system (PCR-TES) for suiting in the study of gene regulation in plant cells. Instead of transforming plasmids into plant cells, transient expression of PCR-fragments can be applicable. The transformation efficiency and expression property of PCR-fragments are comparable to transformation using plasmids. We analyzed the transformation efficiency in PCR-TES at transcription and protein levels. Our results indicate that the PCR-TES is as versatile as the conventional transformation system using plasmid DNA. Through reconstituting PYR1-mediated ABA signaling pathway in Arabidopsis mesophyll protoplasts, we were not only validating the practicality of PCR-TES but also screening potential candidates of CDPK family members which might be involved in the ABA signaling. Moreover, we determined that phosphorylation of ABF2 by CPK4 could be mediated by ABA-induced PYR1 and ABI1, demonstrating a crucial role of CDPKs in the ABA signaling. In summary, PCR-TES can be applicable to facilitate analyzing gene regulation and for the screen of putative regulatory molecules at the high throughput level in plant cells. PMID:23468926

Searching for resistance genes to Bursaphelenchus xylophilus using high throughput screening.

PubMed

Santos, Carla S; Pinheiro, Miguel; Silva, Ana I; Egas, Conceição; Vasconcelos, Marta W

2012-11-07

Pine wilt disease (PWD), caused by the pinewood nematode (PWN; Bursaphelenchus xylophilus), damages and kills pine trees and is causing serious economic damage worldwide. Although the ecological mechanism of infestation is well described, the plant's molecular response to the pathogen is not well known. This is due mainly to the lack of genomic information and the complexity of the disease. High throughput sequencing is now an efficient approach for detecting the expression of genes in non-model organisms, thus providing valuable information in spite of the lack of the genome sequence. In an attempt to unravel genes potentially involved in the pine defense against the pathogen, we hereby report the high throughput comparative sequence analysis of infested and non-infested stems of Pinus pinaster (very susceptible to PWN) and Pinus pinea (less susceptible to PWN). Four cDNA libraries from infested and non-infested stems of P. pinaster and P. pinea were sequenced in a full 454 GS FLX run, producing a total of 2,083,698 reads. The putative amino acid sequences encoded by the assembled transcripts were annotated according to Gene Ontology, to assign Pinus contigs into Biological Processes, Cellular Components and Molecular Functions categories. Most of the annotated transcripts corresponded to Picea genes-25.4-39.7%, whereas a smaller percentage, matched Pinus genes, 1.8-12.8%, probably a consequence of more public genomic information available for Picea than for Pinus. The comparative transcriptome analysis showed that when P. pinaster was infested with PWN, the genes malate dehydrogenase, ABA, water deficit stress related genes and PAR1 were highly expressed, while in PWN-infested P. pinea, the highly expressed genes were ricin B-related lectin, and genes belonging to the SNARE and high mobility group families. Quantitative PCR experiments confirmed the differential gene expression between the two pine species. Defense-related genes triggered by nematode infestation were detected in both P. pinaster and P. pinea transcriptomes utilizing 454 pyrosequencing technology. P. pinaster showed higher abundance of genes related to transcriptional regulation, terpenoid secondary metabolism (including some with nematicidal activity) and pathogen attack. P. pinea showed higher abundance of genes related to oxidative stress and higher levels of expression in general of stress responsive genes. This study provides essential information about the molecular defense mechanisms utilized by P. pinaster and P. pinea against PWN infestation and contributes to a better understanding of PWD.

Pediatric Glioblastoma Therapies Based on Patient-Derived Stem Cell Resources

DTIC Science & Technology

2014-11-01

genomic DNA and then subjected to Illumina high-throughput sequencing . In this analysis, shRNAs lost in the GSC population represent candidate gene...and genomic DNA and then subjected to Illumina high-throughput sequencing . In this analysis, shRNAs lost in the GSC population represent candidate...PRISM 7900 Sequence Detection System ( Genomics Resource, FHCRC). Relative transcript abundance was analyzed using the 2−ΔΔCt method. TRIzol (Invitrogen

Protocols and programs for high-throughput growth and aging phenotyping in yeast.

PubMed

Jung, Paul P; Christian, Nils; Kay, Daniel P; Skupin, Alexander; Linster, Carole L

2015-01-01

In microorganisms, and more particularly in yeasts, a standard phenotyping approach consists in the analysis of fitness by growth rate determination in different conditions. One growth assay that combines high throughput with high resolution involves the generation of growth curves from 96-well plate microcultivations in thermostated and shaking plate readers. To push the throughput of this method to the next level, we have adapted it in this study to the use of 384-well plates. The values of the extracted growth parameters (lag time, doubling time and yield of biomass) correlated well between experiments carried out in 384-well plates as compared to 96-well plates or batch cultures, validating the higher-throughput approach for phenotypic screens. The method is not restricted to the use of the budding yeast Saccharomyces cerevisiae, as shown by consistent results for other species selected from the Hemiascomycete class. Furthermore, we used the 384-well plate microcultivations to develop and validate a higher-throughput assay for yeast Chronological Life Span (CLS), a parameter that is still commonly determined by a cumbersome method based on counting "Colony Forming Units". To accelerate analysis of the large datasets generated by the described growth and aging assays, we developed the freely available software tools GATHODE and CATHODE. These tools allow for semi-automatic determination of growth parameters and CLS behavior from typical plate reader output files. The described protocols and programs will increase the time- and cost-efficiency of a number of yeast-based systems genetics experiments as well as various types of screens.

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

A genome-wide survey of maternal and embryonic transcripts during Xenopus tropicalis development.

PubMed

Paranjpe, Sarita S; Jacobi, Ulrike G; van Heeringen, Simon J; Veenstra, Gert Jan C

2013-11-06

Dynamics of polyadenylation vs. deadenylation determine the fate of several developmentally regulated genes. Decay of a subset of maternal mRNAs and new transcription define the maternal-to-zygotic transition, but the full complement of polyadenylated and deadenylated coding and non-coding transcripts has not yet been assessed in Xenopus embryos. To analyze the dynamics and diversity of coding and non-coding transcripts during development, both polyadenylated mRNA and ribosomal RNA-depleted total RNA were harvested across six developmental stages and subjected to high throughput sequencing. The maternally loaded transcriptome is highly diverse and consists of both polyadenylated and deadenylated transcripts. Many maternal genes show peak expression in the oocyte and include genes which are known to be the key regulators of events like oocyte maturation and fertilization. Of all the transcripts that increase in abundance between early blastula and larval stages, about 30% of the embryonic genes are induced by fourfold or more by the late blastula stage and another 35% by late gastrulation. Using a gene model validation and discovery pipeline, we identified novel transcripts and putative long non-coding RNAs (lncRNA). These lncRNA transcripts were stringently selected as spliced transcripts generated from independent promoters, with limited coding potential and a codon bias characteristic of noncoding sequences. Many lncRNAs are conserved and expressed in a developmental stage-specific fashion. These data reveal dynamics of transcriptome polyadenylation and abundance and provides a high-confidence catalogue of novel and long non-coding RNAs.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

PubMed

Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J

2013-12-08

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

Effects of spaceflight on the immunoglobulin repertoire of unimmunized C57BL/6 mice.

PubMed

Ward, Claire; Rettig, Trisha A; Hlavacek, Savannah; Bye, Bailey A; Pecaut, Michael J; Chapes, Stephen K

2018-02-01

Spaceflight has been shown to suppress the adaptive immune response, altering the distribution and function of lymphocyte populations. B lymphocytes express highly specific and highly diversified receptors, known as immunoglobulins (Ig), that directly bind and neutralize pathogens. Ig diversity is achieved through the enzymatic splicing of gene segments within the genomic DNA of each B cell in a host. The collection of Ig specificities within a host, or Ig repertoire, has been increasingly characterized in both basic research and clinical settings using high-throughput sequencing technology (HTS). We utilized HTS to test the hypothesis that spaceflight affects the B-cell repertoire. To test this hypothesis, we characterized the impact of spaceflight on the unimmunized Ig repertoire of C57BL/6 mice that were flown aboard the International Space Station (ISS) during the Rodent Research One validation flight in comparison to ground controls. Individual gene segment usage was similar between ground control and flight animals, however, gene segment combinations and the junctions in which gene segments combine was varied among animals within and between treatment groups. We also found that spontaneous somatic mutations in the IgH and Igκ gene loci were not increased. These data suggest that space flight did not affect the B cell repertoire of mice flown and housed on the ISS over a short period of time. Copyright © 2017 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

Ultrasensitive Single Fluorescence-Labeled Probe-Mediated Single Universal Primer-Multiplex-Droplet Digital Polymerase Chain Reaction for High-Throughput Genetically Modified Organism Screening.

PubMed

Niu, Chenqi; Xu, Yuancong; Zhang, Chao; Zhu, Pengyu; Huang, Kunlun; Luo, Yunbo; Xu, Wentao

2018-05-01

As genetically modified (GM) technology develops and genetically modified organisms (GMOs) become more available, GMOs face increasing regulations and pressure to adhere to strict labeling guidelines. A singleplex detection method cannot perform the high-throughput analysis necessary for optimal GMO detection. Combining the advantages of multiplex detection and droplet digital polymerase chain reaction (ddPCR), a single universal primer-multiplex-ddPCR (SUP-M-ddPCR) strategy was proposed for accurate broad-spectrum screening and quantification. The SUP increases efficiency of the primers in PCR and plays an important role in establishing a high-throughput, multiplex detection method. Emerging ddPCR technology has been used for accurate quantification of nucleic acid molecules without a standard curve. Using maize as a reference point, four heterologous sequences ( 35S, NOS, NPTII, and PAT) were selected to evaluate the feasibility and applicability of this strategy. Surprisingly, these four genes cover more than 93% of the transgenic maize lines and serve as preliminary screening sequences. All screening probes were labeled with FAM fluorescence, which allows the signals from the samples with GMO content and those without to be easily differentiated. This fiveplex screening method is a new development in GMO screening. Utilizing an optimal amplification assay, the specificity, limit of detection (LOD), and limit of quantitation (LOQ) were validated. The LOD and LOQ of this GMO screening method were 0.1% and 0.01%, respectively, with a relative standard deviation (RSD) < 25%. This method could serve as an important tool for the detection of GM maize from different processed, commercially available products. Further, this screening method could be applied to other fields that require reliable and sensitive detection of DNA targets.

Impact of Roadway Stormwater Runoff on Microbial Contamination in the Receiving Stream.

PubMed

Wyckoff, Kristen N; Chen, Si; Steinman, Andrew J; He, Qiang

2017-09-01

Stormwater runoff from roadways has increasingly become a regulatory concern for water pollution control. Recent work has suggested roadway stormwater runoff as a potential source of microbial pollutants. The objective of this study was to determine the impact of roadway runoff on the microbiological quality of receiving streams. Microbiological quality of roadway stormwater runoff and the receiving stream was monitored during storm events with both cultivation-dependent fecal bacteria enumeration and cultivation-independent high-throughput sequencing techniques. Enumeration of total coliforms as a measure of fecal microbial pollution found consistently lower total coliform counts in roadway runoff than those in the stream water, suggesting that roadway runoff was not a major contributor of microbial pollutants to the receiving stream. Further characterization of the microbial community in the stormwater samples by 16S ribosomal RNA gene-based high-throughput amplicon sequencing revealed significant differences in the microbial composition of stormwater runoff from the roadways and the receiving stream. The differences in microbial composition between the roadway runoff and stream water demonstrate that roadway runoff did not appear to have a major influence on the stream in terms of microbiological quality. Thus, results from both fecal bacteria enumeration and high-throughput amplicon sequencing techniques were consistent that roadway stormwater runoff was not the primary contributor of microbial loading to the stream. Further studies of additional watersheds with distinct characteristics are needed to validate these findings. Understanding gained in this study could support the development of more effective strategies for stormwater management in sensitive watersheds. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

A high-throughput multiplex method adapted for GMO detection.

PubMed

Chaouachi, Maher; Chupeau, Gaëlle; Berard, Aurélie; McKhann, Heather; Romaniuk, Marcel; Giancola, Sandra; Laval, Valérie; Bertheau, Yves; Brunel, Dominique

2008-12-24

A high-throughput multiplex assay for the detection of genetically modified organisms (GMO) was developed on the basis of the existing SNPlex method designed for SNP genotyping. This SNPlex assay allows the simultaneous detection of up to 48 short DNA sequences (approximately 70 bp; "signature sequences") from taxa endogenous reference genes, from GMO constructions, screening targets, construct-specific, and event-specific targets, and finally from donor organisms. This assay avoids certain shortcomings of multiplex PCR-based methods already in widespread use for GMO detection. The assay demonstrated high specificity and sensitivity. The results suggest that this assay is reliable, flexible, and cost- and time-effective for high-throughput GMO detection.

Design and construction of a first-generation high-throughput integrated molecular biology platform for production of optimized synthetic genes and improved industrial strains

USDA-ARS?s Scientific Manuscript database

The molecular biological techniques for plasmid-based assembly and cloning of synthetic assembled gene open reading frames are essential for elucidating the function of the proteins encoded by the genes. These techniques involve the production of full-length cDNA libraries as a source of plasmid-bas...

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

High throughput transcriptome analysis of coffee reveals prehaustorial resistance in response to Hemileia vastatrix infection.

PubMed

Florez, Juan Carlos; Mofatto, Luciana Souto; do Livramento Freitas-Lopes, Rejane; Ferreira, Sávio Siqueira; Zambolim, Eunize Maciel; Carazzolle, Marcelo Falsarella; Zambolim, Laércio; Caixeta, Eveline Teixeira

2017-12-01

We provide a transcriptional profile of coffee rust interaction and identified putative up regulated resistant genes Coffee rust disease, caused by the fungus Hemileia vastatrix, is one of the major diseases in coffee throughout the world. The use of resistant cultivars is considered to be the most effective control strategy for this disease. To identify candidate genes related to different mechanism defense in coffee, we present a time-course comparative gene expression profile of Caturra (susceptible) and Híbrido de Timor (HdT, resistant) in response to H. vastatrix race XXXIII infection. The main objectives were to obtain a global overview of transcriptome in both interaction, compatible and incompatible, and, specially, analyze up-regulated HdT specific genes with inducible resistant and defense signaling pathways. Using both Coffea canephora as a reference genome and de novo assembly, we obtained 43,159 transcripts. At early infection events (12 and 24 h after infection), HdT responded to the attack of H. vastatrix with a larger number of up-regulated genes than Caturra, which was related to prehaustorial resistance. The genes found in HdT at early hours were involved in receptor-like kinases, response ion fluxes, production of reactive oxygen species, protein phosphorylation, ethylene biosynthesis and callose deposition. We selected 13 up-regulated HdT-exclusive genes to validate by real-time qPCR, which most of them confirmed their higher expression in HdT than in Caturra at early stage of infection. These genes have the potential to assist the development of new coffee rust control strategies. Collectively, our results provide understanding of expression profiles in coffee-H. vastatrix interaction over a time course in susceptible and resistant coffee plants.

Identification of type 2 diabetes-associated combination of SNPs using support vector machine.

PubMed

Ban, Hyo-Jeong; Heo, Jee Yeon; Oh, Kyung-Soo; Park, Keun-Joon

2010-04-23

Type 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways. We analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method. Support Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.

Gene expression profiling of flax (Linum usitatissimum L.) under edaphic stress.

PubMed

Dmitriev, Alexey A; Kudryavtseva, Anna V; Krasnov, George S; Koroban, Nadezhda V; Speranskaya, Anna S; Krinitsina, Anastasia A; Belenikin, Maxim S; Snezhkina, Anastasiya V; Sadritdinova, Asiya F; Kishlyan, Natalya V; Rozhmina, Tatiana A; Yurkevich, Olga Yu; Muravenko, Olga V; Bolsheva, Nadezhda L; Melnikova, Nataliya V

2016-11-16

Cultivated flax (Linum usitatissimum L.) is widely used for production of textile, food, chemical and pharmaceutical products. However, various stresses decrease flax production. Search for genes, which are involved in stress response, is necessary for breeding of adaptive cultivars. Imbalanced concentration of nutrient elements in soil decrease flax yields and also results in heritable changes in some flax lines. The appearance of Linum Insertion Sequence 1 (LIS-1) is the most studied modification. However, LIS-1 function is still unclear. High-throughput sequencing of transcriptome of flax plants grown under normal (N), phosphate deficient (P), and nutrient excess (NPK) conditions was carried out using Illumina platform. The assembly of transcriptome was performed, and a total of 34924, 33797, and 33698 unique transcripts for N, P, and NPK sequencing libraries were identified, respectively. We have not revealed any LIS-1 derived mRNA in our sequencing data. The analysis of high-throughput sequencing data allowed us to identify genes with potentially differential expression under imbalanced nutrition. For further investigation with qPCR, 15 genes were chosen and their expression levels were evaluated in the extended sampling of 31 flax plants. Significant expression alterations were revealed for genes encoding WRKY and JAZ protein families under P and NPK conditions. Moreover, the alterations of WRKY family genes differed depending on LIS-1 presence in flax plant genome. Besides, we revealed slight and LIS-1 independent mRNA level changes of KRP2 and ING1 genes, which are adjacent to LIS-1, under nutrition stress. Differentially expressed genes were identified in flax plants, which were grown under phosphate deficiency and excess nutrition, on the basis of high-throughput sequencing and qPCR data. We showed that WRKY and JAS gene families participate in flax response to imbalanced nutrient content in soil. Besides, we have not identified any mRNA, which could be derived from LIS-1, in our transcriptome sequencing data. Expression of LIS-1 flanking genes, ING1 and KRP2, was suggested not to be nutrient stress-induced. Obtained results provide new insights into edaphic stress response in flax and the role of LIS-1 in these process.

Differences in DNA Binding Specificity of Floral Homeotic Protein Complexes Predict Organ-Specific Target Genes.

PubMed

Smaczniak, Cezary; Muiño, Jose M; Chen, Dijun; Angenent, Gerco C; Kaufmann, Kerstin

2017-08-01

Floral organ identities in plants are specified by the combinatorial action of homeotic master regulatory transcription factors. However, how these factors achieve their regulatory specificities is still largely unclear. Genome-wide in vivo DNA binding data show that homeotic MADS domain proteins recognize partly distinct genomic regions, suggesting that DNA binding specificity contributes to functional differences of homeotic protein complexes. We used in vitro systematic evolution of ligands by exponential enrichment followed by high-throughput DNA sequencing (SELEX-seq) on several floral MADS domain protein homo- and heterodimers to measure their DNA binding specificities. We show that specification of reproductive organs is associated with distinct binding preferences of a complex formed by SEPALLATA3 and AGAMOUS. Binding specificity is further modulated by different binding site spacing preferences. Combination of SELEX-seq and genome-wide DNA binding data allows differentiation between targets in specification of reproductive versus perianth organs in the flower. We validate the importance of DNA binding specificity for organ-specific gene regulation by modulating promoter activity through targeted mutagenesis. Our study shows that intrafamily protein interactions affect DNA binding specificity of floral MADS domain proteins. Differential DNA binding of MADS domain protein complexes plays a role in the specificity of target gene regulation. © 2017 American Society of Plant Biologists. All rights reserved.

Identification of Methylated Genes Associated with Aggressive Bladder Cancer

PubMed Central

Marsit, Carmen J.; Houseman, E. Andres; Christensen, Brock C.; Gagne, Luc; Wrensch, Margaret R.; Nelson, Heather H.; Wiemels, Joseph; Zheng, Shichun; Wiencke, John K.; Andrew, Angeline S.; Schned, Alan R.; Karagas, Margaret R.; Kelsey, Karl T.

2010-01-01

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment. PMID:20808801

Identification of methylated genes associated with aggressive bladder cancer.

PubMed

Marsit, Carmen J; Houseman, E Andres; Christensen, Brock C; Gagne, Luc; Wrensch, Margaret R; Nelson, Heather H; Wiemels, Joseph; Zheng, Shichun; Wiencke, John K; Andrew, Angeline S; Schned, Alan R; Karagas, Margaret R; Kelsey, Karl T

2010-08-23

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment.

Identification of compounds that modulate retinol signaling using a cell-based qHTS assay

PubMed Central

Chen, Yanling; Sakamuru, Srilatha; Huang, Ruili; Reese, David H.; Xia, Menghang

2016-01-01

In vertebrates, the retinol (vitamin A) signaling pathway (RSP) controls the biosynthesis and catabolism of all-trans retinoic acid (atRA), which regulates transcription of genes essential for embryonic development. Chemicals that interfere with the RSP to cause abnormal intracellular levels of atRA are potential developmental toxicants. To assess chemicals for the ability to interfere with retinol signaling, we have developed a cell-based RARE (Retinoic Acid Response Element) reporter gene assay to identify RSP disruptors. To validate this assay in a quantitative high-throughput screening (qHTS) platform, we screened the Library of Pharmacologically Active Compounds (LOPAC) in both agonist and antagonist modes. The screens detected known RSP agonists, demonstrating assay reliability, and also identified novel RSP agonists including kenpaullone, niclosamide, PD98059 and SU4312, and RSP antagonists including Bay 11-7085, LY294002, 3,4-Methylenedioxy-β-nitrostyrene, and topoisomerase inhibitors (camptothecin, topotecan, amsacrine hydrochloride, and idarubicin). When evaluated in the P19 pluripotent cell, these compounds were found to affect the expression of the Hoxa1 gene that is essential for embryo body patterning. These results show that the RARE assay is an effective qHTS approach for screening large compound libraries to identify chemicals that have the potential to adversely affect embryonic development through interference with retinol signaling. PMID:26820057

Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

PubMed Central

Klein, Allon M; Mazutis, Linas; Akartuna, Ilke; Tallapragada, Naren; Veres, Adrian; Li, Victor; Peshkin, Leonid; Weitz, David A; Kirschner, Marc W

2015-01-01

Summary It has long been the dream of biologists to map gene expression at the single cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility of these high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. PMID:26000487

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

PubMed Central

Hidalgo, Marta R.; Cubuk, Cankut; Amadoz, Alicia; Salavert, Francisco; Carbonell-Caballero, José; Dopazo, Joaquin

2017-01-01

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions. PMID:28042959

Identification of aberrantly expressed long non-coding RNAs in stomach adenocarcinoma.

PubMed

Gu, Jianbin; Li, Yong; Fan, Liqiao; Zhao, Qun; Tan, Bibo; Hua, Kelei; Wu, Guobin

2017-07-25

Stomach adenocarcinoma (STAD) is a common malignancy worldwide. This study aimed to identify the aberrantly expressed long non-coding RNAs (lncRNAs) in STAD. Total of 74 DElncRNAs and 449 DEmRNAs were identified in STAD compared with paired non-tumor tissues. The DElncRNA/DEmRNA co-expression network was constructed, which covered 519 nodes and 2993 edges. The qRT-PCR validation results of DElncRNAs were consistent with our bioinformatics analysis based on RNA-sequencing. The DEmRNAs co-expressed with DElncRNAs were significantly enriched in gastric acid secretion, complement and coagulation cascades, pancreatic secretion, cytokine-cytokine receptor interaction and Jak-STAT signaling pathway. The expression levels of the nine candidate DElncRNAs in TCGA database were compatible with our RNA-sequencing. FEZF1-AS1, HOTAIR and LINC01234 had the potential diagnosis value for STAD. The lncRNA and mRNA expression profile of 3 STAD tissues and 3 matched adjacent non-tumor tissues was obtained through high-throughput RNA-sequencing. Differentially expressed lncRNAs/mRNAs (DElncRNAs/DEmRNAs) were identified in STAD. DElncRNA/DEmRNA co-expression network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to predict the biological functions of DElncRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) was subjected to validate the expression levels of DEmRNAs and DElncRNAs. Moreover, the expression of DElncRNAs was validated through The Cancer Genome Atlas (TCGA) database. The diagnosis value of candidate DElncRNAs was accessed by receiver operating characteristic (ROC) analysis. Our work might provide useful information for exploring the tumorigenesis mechanism of STAD and pave the road for identification of diagnostic biomarkers in STAD.

A Triple-Fluorophore-Labeled Nucleic Acid pH Nanosensor to Investigate Non-viral Gene Delivery.

PubMed

Wilson, David R; Routkevitch, Denis; Rui, Yuan; Mosenia, Arman; Wahlin, Karl J; Quinones-Hinojosa, Alfredo; Zack, Donald J; Green, Jordan J

2017-07-05

There is a need for new tools to better quantify intracellular delivery barriers in high-throughput and high-content ways. Here, we synthesized a triple-fluorophore-labeled nucleic acid pH nanosensor for measuring intracellular pH of exogenous DNA at specific time points in a high-throughput manner by flow cytometry following non-viral transfection. By including two pH-sensitive fluorophores and one pH-insensitive fluorophore in the nanosensor, detection of pH was possible over the full physiological range. We further assessed possible correlation between intracellular pH of delivered DNA, cellular uptake of DNA, and DNA reporter gene expression at 24 hr post-transfection for poly-L-lysine and branched polyethylenimine polyplex nanoparticles. While successful transfection was shown to clearly depend on median cellular pH of delivered DNA at the cell population level, surprisingly, on an individual cell basis, there was no significant correlation between intracellular pH and transfection efficacy. To our knowledge, this is the first reported instance of high-throughput single-cell analysis between cellular uptake of DNA, intracellular pH of delivered DNA, and gene expression of the delivered DNA. Using the nanosensor, we demonstrate that the ability of polymeric nanoparticles to avoid an acidic environment is necessary, but not sufficient, for successful transfection. Copyright © 2017 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells.

PubMed

Yeo, Jiyoun; Crawford, Erin L; Zhang, Xiaolu; Khuder, Sadik; Chen, Tian; Levin, Albert; Blomquist, Thomas M; Willey, James C

2017-05-02

Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation. Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 61 CA cases and 59 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening. After cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96-0.99). The overall classification accuracy was 93% (95% CI 88%-98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone. The LCRT biomarker reported here displayed high accuracy and ease of implementation on a high throughput, quality-controlled targeted NGS platform. As such, it is optimized for clinical validation in specimens from the ongoing LCRT blinded prospective cohort study. Following validation, the biomarker is expected to have clinical utility by better stratifying subjects for annual lung cancer screening compared to current demographic criteria alone.

Engineering a vitamin B12 high-throughput screening system by riboswitch sensor in Sinorhizobium meliloti.

PubMed

Cai, Yingying; Xia, Miaomiao; Dong, Huina; Qian, Yuan; Zhang, Tongcun; Zhu, Beiwei; Wu, Jinchuan; Zhang, Dawei

2018-05-11

As a very important coenzyme in the cell metabolism, Vitamin B 12 (cobalamin, VB 12 ) has been widely used in food and medicine fields. The complete biosynthesis of VB 12 requires approximately 30 genes, but overexpression of these genes did not result in expected increase of VB 12 production. High-yield VB 12 -producing strains are usually obtained by mutagenesis treatments, thus developing an efficient screening approach is urgently needed. By the help of engineered strains with varied capacities of VB 12 production, a riboswitch library was constructed and screened, and the btuB element from Salmonella typhimurium was identified as the best regulatory device. A flow cytometry high-throughput screening system was developed based on the btuB riboswitch with high efficiency to identify positive mutants. Mutation of Sinorhizobium meliloti (S. meliloti) was optimized using the novel mutation technique of atmospheric and room temperature plasma (ARTP). Finally, the mutant S. meliloti MC5-2 was obtained and considered as a candidate for industrial applications. After 7 d's cultivation on a rotary shaker at 30 °C, the VB 12 titer of S. meliloti MC5-2 reached 156 ± 4.2 mg/L, which was 21.9% higher than that of the wild type strain S. meliloti 320 (128 ± 3.2 mg/L). The genome of S. meliloti MC5-2 was sequenced, and gene mutations were identified and analyzed. To our knowledge, it is the first time that a riboswitch element was used in S. meliloti. The flow cytometry high-throughput screening system was successfully developed and a high-yield VB 12 producing strain was obtained. The identified and analyzed gene mutations gave useful information for developing high-yield strains by metabolic engineering. Overall, this work provides a useful high-throughput screening method for developing high VB 12 -yield strains.

Transcriptome analysis of zebrafish embryos exposed to deltamethrin.

PubMed

Chueh, Tsung-Cheng; Hsu, Li-Sung; Kao, Chin-Ming; Hsu, Tung-Wei; Liao, Hung-Yu; Wang, Kuan-Yi; Chen, Ssu Ching

2017-05-01

Deltamethrin (DTM), a type II pyrethroid, is one of the most commonly used insecticides. The increased use of pyrethroid leads to potential adverse effects, particularly in sensitive populations such as children and pregnant women. None of the related studies was focused on the transcriptome responses in zebrafish embryos after treatment with DTM; therefore, RNA-seq, a high-throughput method, was performed to analyze the global expression of differential expressed genes (DEGs) in zebrafish embryos treated with DTM (40 and 80 μg/L) from fertilization to 48 h postfertilization (hpf) as compared with that in the control group (without DTM treatment). Two cDNA libraries were generated from treated embryos and one cDNA library from nontreated embryos, respectively. Over 92% of reads mapped to the reference in these three libraries. It was observed that many differential genes were expressed in comparison with embryos before and after DTM. The 20 most differentially expressed upregulated or downregulated genes were majorly involved in the signaling transduction. Validation of selected nine genes expression using qRT-PCR confirmed RNA-seq results. The transcriptome sequences were further subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, showing G-protein-coupled receptor signaling pathway and neuroactive ligand-receptor interaction, respectively, were most enriched. The data from this study contributed to a better understanding of the potential consequences of fish exposed to DTM, to an evaluation of the potential threat of DTM to fish populations in aquatic environments. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 1548-1557, 2017. © 2016 Wiley Periodicals, Inc.

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline*

PubMed Central

Zhu, Xun; Xie, Shangbo; Armengaud, Jean; Xie, Wen; Guo, Zhaojiang; Kang, Shi; Wu, Qingjun; Wang, Shaoli; Xia, Jixing; He, Rongjun; Zhang, Youjun

2016-01-01

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest. PMID:26902207

A Cellular High-Throughput Screening Approach for Therapeutic trans-Cleaving Ribozymes and RNAi against Arbitrary mRNA Disease Targets

PubMed Central

Yau, Edwin H.; Butler, Mark C.; Sullivan, Jack M.

2016-01-01

Major bottlenecks in development of therapeutic post transcriptional gene silencing (PTGS) agents (e.g. ribozymes, RNA interference, antisense) include the challenge of mapping rare accessible regions of the mRNA target that are open for annealing and cleavage, testing and optimization of agents in human cells to identify lead agents, testing for cellular toxicity, and preclinical evaluation in appropriate animal models of disease. Methods for rapid and reliable cellular testing of PTGS agents are needed to identify potent lead candidates for optimization. Our goal was to develop a means of rapid assessment of many RNA agents to identify a lead candidate for a given mRNA associated with a disease state. We developed a rapid human cell-based screening platform to test efficacy of hammerhead ribozyme (hhRz) or RNA interference (RNAi) constructs, using a model retinal degeneration target, human rod opsin (RHO) mRNA. The focus is on RNA Drug Discovery for diverse retinal degeneration targets. To validate the approach, candidate hhRzs were tested against NUH↓ cleavage sites (N=G,C,A,U; H=C,A,U) within the target mRNA of secreted alkaline phosphatase (SEAP), a model gene expression reporter, based upon in silico predictions of mRNA accessibility. HhRzs were embedded in a larger stable adenoviral VAI RNA scaffold for high cellular expression, cytoplasmic trafficking, and stability. Most hhRz expression plasmids exerted statistically significant knockdown of extracellular SEAP enzyme activity when readily assayed by a fluorescence enzyme assay intended for high throughput screening (HTS). Kinetics of PTGS knockdown of cellular targets is measureable in live cells with the SEAP reporter. The validated SEAP HTS platform was transposed to identify lead PTGS agents against a model hereditary retinal degeneration target, RHO mRNA. Two approaches were used to physically fuse the model retinal gene target mRNA to the SEAP reporter mRNA. The most expedient way to evaluate a large set of potential VAI-hhRz expression plasmids against diverse NUH↓ cleavage sites uses cultured human HEK293S cells stably expressing a dicistronic Target-IRES-SEAP target fusion mRNA. Broad utility of this rational RNA drug discovery approach is feasible for any ophthalmological disease-relevant mRNA targets and any disease mRNA targets in general. The approach will permit rank ordering of PTGS agents based on potency to identify a lead therapeutic compound for further optimization. PMID:27233447

Restructuring of the Aquatic Bacterial Community by Hydric Dynamics Associated with Superstorm Sandy

PubMed Central

Ulrich, Nikea; Rosenberger, Abigail; Brislawn, Colin; Wright, Justin; Kessler, Collin; Toole, David; Solomon, Caroline; Strutt, Steven; McClure, Erin

2016-01-01

ABSTRACT Bacterial community composition and longitudinal fluctuations were monitored in a riverine system during and after Superstorm Sandy to better characterize inter- and intracommunity responses associated with the disturbance associated with a 100-year storm event. High-throughput sequencing of the 16S rRNA gene was used to assess microbial community structure within water samples from Muddy Creek Run, a second-order stream in Huntingdon, PA, at 12 different time points during the storm event (29 October to 3 November 2012) and under seasonally matched baseline conditions. High-throughput sequencing of the 16S rRNA gene was used to track changes in bacterial community structure and divergence during and after Superstorm Sandy. Bacterial community dynamics were correlated to measured physicochemical parameters and fecal indicator bacteria (FIB) concentrations. Bioinformatics analyses of 2.1 million 16S rRNA gene sequences revealed a significant increase in bacterial diversity in samples taken during peak discharge of the storm. Beta-diversity analyses revealed longitudinal shifts in the bacterial community structure. Successional changes were observed, in which Betaproteobacteria and Gammaproteobacteria decreased in 16S rRNA gene relative abundance, while the relative abundance of members of the Firmicutes increased. Furthermore, 16S rRNA gene sequences matching pathogenic bacteria, including strains of Legionella, Campylobacter, Arcobacter, and Helicobacter, as well as bacteria of fecal origin (e.g., Bacteroides), exhibited an increase in abundance after peak discharge of the storm. This study revealed a significant restructuring of in-stream bacterial community structure associated with hydric dynamics of a storm event. IMPORTANCE In order to better understand the microbial risks associated with freshwater environments during a storm event, a more comprehensive understanding of the variations in aquatic bacterial diversity is warranted. This study investigated the bacterial communities during and after Superstorm Sandy to provide fine time point resolution of dynamic changes in bacterial composition. This study adds to the current literature by revealing the variation in bacterial community structure during the course of a storm. This study employed high-throughput DNA sequencing, which generated a deep analysis of inter- and intracommunity responses during a significant storm event. This study has highlighted the utility of applying high-throughput sequencing for water quality monitoring purposes, as this approach enabled a more comprehensive investigation of the bacterial community structure. Altogether, these data suggest a drastic restructuring of the stream bacterial community during a storm event and highlight the potential of high-throughput sequencing approaches for assessing the microbiological quality of our environment. PMID:27060115

NASA's Evolutionary Xenon Thruster (NEXT) Project Qualification Propellant Throughput Milestone: Performance, Erosion, and Thruster Service Life Prediction After 450 kg

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 28,500 hr of operation and processed 466 kg of xenon throughput--more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.

Metagenomic analysis revealed highly diverse microbial arsenic metabolism genes in paddy soils with low-arsenic contents.

PubMed

Xiao, Ke-Qing; Li, Li-Guan; Ma, Li-Ping; Zhang, Si-Yu; Bao, Peng; Zhang, Tong; Zhu, Yong-Guan

2016-04-01

Microbe-mediated arsenic (As) metabolism plays a critical role in global As cycle, and As metabolism involves different types of genes encoding proteins facilitating its biotransformation and transportation processes. Here, we used metagenomic analysis based on high-throughput sequencing and constructed As metabolism protein databases to analyze As metabolism genes in five paddy soils with low-As contents. The results showed that highly diverse As metabolism genes were present in these paddy soils, with varied abundances and distribution for different types and subtypes of these genes. Arsenate reduction genes (ars) dominated in all soil samples, and significant correlation existed between the abundance of arr (arsenate respiration), aio (arsenite oxidation), and arsM (arsenite methylation) genes, indicating the co-existence and close-relation of different As resistance systems of microbes in wetland environments similar to these paddy soils after long-term evolution. Among all soil parameters, pH was an important factor controlling the distribution of As metabolism gene in five paddy soils (p = 0.018). To the best of our knowledge, this is the first study using high-throughput sequencing and metagenomics approach in characterizing As metabolism genes in the five paddy soil, showing their great potential in As biotransformation, and therefore in mitigating arsenic risk to humans. Copyright © 2015 Elsevier Ltd. All rights reserved.

Picking Cell Lines for High-Throughput Transcriptomic Toxicity ...

EPA Pesticide Factsheets

High throughput, whole genome transcriptomic profiling is a promising approach to comprehensively evaluate chemicals for potential biological effects. To be useful for in vitro toxicity screening, gene expression must be quantified in a set of representative cell types that captures the diversity of potential responses across chemicals. The ideal dataset to select these cell types would consist of hundreds of cell types treated with thousands of chemicals, but does not yet exist. However, basal gene expression data may be useful as a surrogate for representing the relevant biological space necessary for cell type selection. The goal of this study was to identify a small (< 20) number of cell types that capture a large, quantifiable fraction of basal gene expression diversity. Three publicly available collections of Affymetrix U133+2.0 cellular gene expression data were used: 1) 59 cell lines from the NCI60 set; 2) 303 primary cell types from the Mabbott et al (2013) expression atlas; and 3) 1036 cell lines from the Cancer Cell Line Encyclopedia. The data were RMA normalized, log-transformed, and the probe sets mapped to HUGO gene identifiers. The results showed that <20 cell lines capture only a small fraction of the total diversity in basal gene expression when evaluated using either the entire set of 20960 HUGO genes or a subset of druggable genes likely to be chemical targets. The fraction of the total gene expression variation explained was consistent when

Gene regulation is governed by a core network in hepatocellular carcinoma.

PubMed

Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further experimental validation is worthwhile.

Validation of Reference Genes for Relative Quantitative Gene Expression Studies in Cassava (Manihot esculenta Crantz) by Using Quantitative Real-Time PCR

PubMed Central

Hu, Meizhen; Hu, Wenbin; Xia, Zhiqiang; Zhou, Xincheng; Wang, Wenquan

2016-01-01

Reverse transcription quantitative real-time polymerase chain reaction (real-time PCR, also referred to as quantitative RT-PCR or RT-qPCR) is a highly sensitive and high-throughput method used to study gene expression. Despite the numerous advantages of RT-qPCR, its accuracy is strongly influenced by the stability of internal reference genes used for normalizations. To date, few studies on the identification of reference genes have been performed on cassava (Manihot esculenta Crantz). Therefore, we selected 26 candidate reference genes mainly via the three following channels: reference genes used in previous studies on cassava, the orthologs of the most stable Arabidopsis genes, and the sequences obtained from 32 cassava transcriptome sequence data. Then, we employed ABI 7900 HT and SYBR Green PCR mix to assess the expression of these genes in 21 materials obtained from various cassava samples under different developmental and environmental conditions. The stability of gene expression was analyzed using two statistical algorithms, namely geNorm and NormFinder. geNorm software suggests the combination of cassava4.1_017977 and cassava4.1_006391 as sufficient reference genes for major cassava samples, the union of cassava4.1_014335 and cassava4.1_006884 as best choice for drought stressed samples, and the association of cassava4.1_012496 and cassava4.1_006391 as optimal choice for normally grown samples. NormFinder software recommends cassava4.1_006884 or cassava4.1_006776 as superior reference for qPCR analysis of different materials and organs of drought stressed or normally grown cassava, respectively. Results provide an important resource for cassava reference genes under specific conditions. The limitations of these findings were also discussed. Furthermore, we suggested some strategies that may be used to select candidate reference genes. PMID:27242878

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

PubMed

Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

2016-05-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

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

Green, Martin L.; Choi, C. L.; Hattrick-Simpers, J. R.

The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. Asmore » a result, a major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.« less

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

DOE PAGES

Green, Martin L.; Choi, C. L.; Hattrick-Simpers, J. R.; ...

2017-03-28

The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. Asmore » a result, a major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.« less

High-throughput interpretation of gene structure changes in human and nonhuman resequencing data, using ACE

USDA-ARS?s Scientific Manuscript database

We describe a suite of software tools for identifying possible functional changes in gene structure that may result from sequence variants. ACE (“Assessing Changes to Exons”) converts phased genotype calls to a collection of explicit haplotype sequences, maps transcript annotations onto them, detect...

Gene Expression Browser: Large-Scale and Cross-Experiment Microarray Data Management, Search & Visualization

USDA-ARS?s Scientific Manuscript database

The amount of microarray gene expression data in public repositories has been increasing exponentially for the last couple of decades. High-throughput microarray data integration and analysis has become a critical step in exploring the large amount of expression data for biological discovery. Howeve...

Using Gene Expression Biomarkers to Identify Chemicals that Induce Key Events in Cancer and Endocrine Disruption AOPs: Androgen Receptor as an Example

EPA Science Inventory

High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The development of gene expression biomarkers that accurately predict molecular and toxicological effec...

A gene expression biomarker accurately predicts estrogen receptor α modulation in a human gene expression compendium

EPA Science Inventory

The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1...

Contributions to Statistical Problems Related to Microarray Data

ERIC Educational Resources Information Center

Hong, Feng

2009-01-01

Microarray is a high throughput technology to measure the gene expression. Analysis of microarray data brings many interesting and challenging problems. This thesis consists three studies related to microarray data. First, we propose a Bayesian model for microarray data and use Bayes Factors to identify differentially expressed genes. Second, we…

MethylMix 2.0: an R package for identifying DNA methylation genes. | Office of Cancer Genomics

Cancer.gov

DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes.

Development of a rapid microarray-based DNA subtyping assay for the alleles of Shiga toxins 1 and 2 of Escherichia coli.

PubMed

Geue, Lutz; Stieber, Bettina; Monecke, Stefan; Engelmann, Ines; Gunzer, Florian; Slickers, Peter; Braun, Sascha D; Ehricht, Ralf

2014-08-01

In this study, we developed a new rapid, economic, and automated microarray-based genotyping test for the standardized subtyping of Shiga toxins 1 and 2 of Escherichia coli. The microarrays from Alere Technologies can be used in two different formats, the ArrayTube and the ArrayStrip (which enables high-throughput testing in a 96-well format). One microarray chip harbors all the gene sequences necessary to distinguish between all Stx subtypes, facilitating the identification of single and multiple subtypes within a single isolate in one experiment. Specific software was developed to automatically analyze all data obtained from the microarray. The assay was validated with 21 Shiga toxin-producing E. coli (STEC) reference strains that were previously tested by the complete set of conventional subtyping PCRs. The microarray results showed 100% concordance with the PCR results. Essentially identical results were detected when the standard DNA extraction method was replaced by a time-saving heat lysis protocol. For further validation of the microarray, we identified the Stx subtypes or combinations of the subtypes in 446 STEC field isolates of human and animal origin. In summary, this oligonucleotide array represents an excellent diagnostic tool that provides some advantages over standard PCR-based subtyping. The number of the spotted probes on the microarrays can be increased by additional probes, such as for novel alleles, species markers, or resistance genes, should the need arise. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

The hot pepper (Capsicum annuum) microRNA transcriptome reveals novel and conserved targets: a foundation for understanding MicroRNA functional roles in hot pepper.

PubMed

Hwang, Dong-Gyu; Park, June Hyun; Lim, Jae Yun; Kim, Donghyun; Choi, Yourim; Kim, Soyoung; Reeves, Gregory; Yeom, Seon-In; Lee, Jeong-Soo; Park, Minkyu; Kim, Seungill; Choi, Ik-Young; Choi, Doil; Shin, Chanseok

2013-01-01

MicroRNAs (miRNAs) are a class of non-coding RNAs approximately 21 nt in length which play important roles in regulating gene expression in plants. Although many miRNA studies have focused on a few model plants, miRNAs and their target genes remain largely unknown in hot pepper (Capsicum annuum), one of the most important crops cultivated worldwide. Here, we employed high-throughput sequencing technology to identify miRNAs in pepper extensively from 10 different libraries, including leaf, stem, root, flower, and six developmental stage fruits. Based on a bioinformatics pipeline, we successfully identified 29 and 35 families of conserved and novel miRNAs, respectively. Northern blot analysis was used to validate further the expression of representative miRNAs and to analyze their tissue-specific or developmental stage-specific expression patterns. Moreover, we computationally predicted miRNA targets, many of which were experimentally confirmed using 5' rapid amplification of cDNA ends analysis. One of the validated novel targets of miR-396 was a domain rearranged methyltransferase, the major de novo methylation enzyme, involved in RNA-directed DNA methylation in plants. This work provides the first reliable draft of the pepper miRNA transcriptome. It offers an expanded picture of pepper miRNAs in relation to other plants, providing a basis for understanding the functional roles of miRNAs in pepper.

A Mutant Mouse with a Highly Specific Contextual Fear-Conditioning Deficit Found in an N-Ethyl-N-Nitrosourea (ENU) Mutagenesis Screen

ERIC Educational Resources Information Center

Pletcher, Mathew T.; Wiltshire, Tim; Tarantino, Lisa M.; Mayford, Mark; Reijmers, Leon G.; Coats, Jennifer K.

2006-01-01

Targeted mutagenesis in mice has shown that genes from a wide variety of gene families are involved in memory formation. The efficient identification of genes involved in learning and memory could be achieved by random mutagenesis combined with high-throughput phenotyping. Here, we provide the first report of a mutagenesis screen that has…

Mining high-throughput experimental data to link gene and function

PubMed Central

Blaby-Haas, Crysten E.; de Crécy-Lagard, Valérie

2011-01-01

Nearly 2200 genomes encoding some 6 million proteins have now been sequenced. Around 40% of these proteins are of unknown function even when function is loosely and minimally defined as “belonging to a superfamily”. In addition to in silico methods, the swelling stream of high-throughput experimental data can give valuable clues for linking these “unknowns” with precise biological roles. The goal is to develop integrative data-mining platforms that allow the scientific community at large to access and utilize this rich source of experimental knowledge. To this end, we review recent advances in generating whole-genome experimental datasets, where this data can be accessed, and how it can be used to drive prediction of gene function. PMID:21310501

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

High-throughput profiling of antibiotic resistance genes in drinking water treatment plants and distribution systems.

PubMed

Xu, Like; Ouyang, Weiying; Qian, Yanyun; Su, Chao; Su, Jianqiang; Chen, Hong

2016-06-01

Antibiotic resistance genes (ARGs) are present in surface water and often cannot be completely eliminated by drinking water treatment plants (DWTPs). Improper elimination of the ARG-harboring microorganisms contaminates the water supply and would lead to animal and human disease. Therefore, it is of utmost importance to determine the most effective ways by which DWTPs can eliminate ARGs. Here, we tested water samples from two DWTPs and distribution systems and detected the presence of 285 ARGs, 8 transposases, and intI-1 by utilizing high-throughput qPCR. The prevalence of ARGs differed in the two DWTPs, one of which employed conventional water treatments while the other had advanced treatment processes. The relative abundance of ARGs increased significantly after the treatment with biological activated carbon (BAC), raising the number of detected ARGs from 76 to 150. Furthermore, the final chlorination step enhanced the relative abundance of ARGs in the finished water generated from both DWTPs. The total enrichment of ARGs varied from 6.4-to 109.2-fold in tap water compared to finished water, among which beta-lactam resistance genes displayed the highest enrichment. Six transposase genes were detected in tap water samples, with the transposase gene TnpA-04 showing the greatest enrichment (up to 124.9-fold). We observed significant positive correlations between ARGs and mobile genetic elements (MGEs) during the distribution systems, indicating that transposases and intI-1 may contribute to antibiotic resistance in drinking water. To our knowledge, this is the first study to investigate the diversity and abundance of ARGs in drinking water treatment systems utilizing high-throughput qPCR techniques in China. Copyright © 2016 Elsevier Ltd. All rights reserved.

'PACLIMS': a component LIM system for high-throughput functional genomic analysis.

PubMed

Donofrio, Nicole; Rajagopalon, Ravi; Brown, Douglas; Diener, Stephen; Windham, Donald; Nolin, Shelly; Floyd, Anna; Mitchell, Thomas; Galadima, Natalia; Tucker, Sara; Orbach, Marc J; Patel, Gayatri; Farman, Mark; Pampanwar, Vishal; Soderlund, Cari; Lee, Yong-Hwan; Dean, Ralph A

2005-04-12

Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the approximately 11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required. The platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured. Many sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors.

'PACLIMS': A component LIM system for high-throughput functional genomic analysis

PubMed Central

Donofrio, Nicole; Rajagopalon, Ravi; Brown, Douglas; Diener, Stephen; Windham, Donald; Nolin, Shelly; Floyd, Anna; Mitchell, Thomas; Galadima, Natalia; Tucker, Sara; Orbach, Marc J; Patel, Gayatri; Farman, Mark; Pampanwar, Vishal; Soderlund, Cari; Lee, Yong-Hwan; Dean, Ralph A

2005-01-01

Background Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the ~11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required. Results The platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured. Conclusion Many sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors. PMID:15826298

Identifying Stable Reference Genes for qRT-PCR Normalisation in Gene Expression Studies of Narrow-Leafed Lupin (Lupinus angustifolius L.).

PubMed

Taylor, Candy M; Jost, Ricarda; Erskine, William; Nelson, Matthew N

2016-01-01

Quantitative Reverse Transcription PCR (qRT-PCR) is currently one of the most popular, high-throughput and sensitive technologies available for quantifying gene expression. Its accurate application depends heavily upon normalisation of gene-of-interest data with reference genes that are uniformly expressed under experimental conditions. The aim of this study was to provide the first validation of reference genes for Lupinus angustifolius (narrow-leafed lupin, a significant grain legume crop) using a selection of seven genes previously trialed as reference genes for the model legume, Medicago truncatula. In a preliminary evaluation, the seven candidate reference genes were assessed on the basis of primer specificity for their respective targeted region, PCR amplification efficiency, and ability to discriminate between cDNA and gDNA. Following this assessment, expression of the three most promising candidates [Ubiquitin C (UBC), Helicase (HEL), and Polypyrimidine tract-binding protein (PTB)] was evaluated using the NormFinder and RefFinder statistical algorithms in two narrow-leafed lupin lines, both with and without vernalisation treatment, and across seven organ types (cotyledons, stem, leaves, shoot apical meristem, flowers, pods and roots) encompassing three developmental stages. UBC was consistently identified as the most stable candidate and has sufficiently uniform expression that it may be used as a sole reference gene under the experimental conditions tested here. However, as organ type and developmental stage were associated with greater variability in relative expression, it is recommended using UBC and HEL as a pair to achieve optimal normalisation. These results highlight the importance of rigorously assessing candidate reference genes for each species across a diverse range of organs and developmental stages. With emerging technologies, such as RNAseq, and the completion of valuable transcriptome data sets, it is possible that other potentially more suitable reference genes will be identified for this species in future.

Identifying Stable Reference Genes for qRT-PCR Normalisation in Gene Expression Studies of Narrow-Leafed Lupin (Lupinus angustifolius L.)

PubMed Central

Erskine, William; Nelson, Matthew N.

2016-01-01

Quantitative Reverse Transcription PCR (qRT-PCR) is currently one of the most popular, high-throughput and sensitive technologies available for quantifying gene expression. Its accurate application depends heavily upon normalisation of gene-of-interest data with reference genes that are uniformly expressed under experimental conditions. The aim of this study was to provide the first validation of reference genes for Lupinus angustifolius (narrow-leafed lupin, a significant grain legume crop) using a selection of seven genes previously trialed as reference genes for the model legume, Medicago truncatula. In a preliminary evaluation, the seven candidate reference genes were assessed on the basis of primer specificity for their respective targeted region, PCR amplification efficiency, and ability to discriminate between cDNA and gDNA. Following this assessment, expression of the three most promising candidates [Ubiquitin C (UBC), Helicase (HEL), and Polypyrimidine tract-binding protein (PTB)] was evaluated using the NormFinder and RefFinder statistical algorithms in two narrow-leafed lupin lines, both with and without vernalisation treatment, and across seven organ types (cotyledons, stem, leaves, shoot apical meristem, flowers, pods and roots) encompassing three developmental stages. UBC was consistently identified as the most stable candidate and has sufficiently uniform expression that it may be used as a sole reference gene under the experimental conditions tested here. However, as organ type and developmental stage were associated with greater variability in relative expression, it is recommended using UBC and HEL as a pair to achieve optimal normalisation. These results highlight the importance of rigorously assessing candidate reference genes for each species across a diverse range of organs and developmental stages. With emerging technologies, such as RNAseq, and the completion of valuable transcriptome data sets, it is possible that other potentially more suitable reference genes will be identified for this species in future. PMID:26872362

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

Kolker, Eugene

Our project focused primarily on analysis of different types of data produced by global high-throughput technologies, data integration of gene annotation, and gene and protein expression information, as well as on getting a better functional annotation of Shewanella genes. Specifically, four of our numerous major activities and achievements include the development of: statistical models for identification and expression proteomics, superior to currently available approaches (including our own earlier ones); approaches to improve gene annotations on the whole-organism scale; standards for annotation, transcriptomics and proteomics approaches; and generalized approaches for data integration of gene annotation, gene and protein expression information.

Validation of a Microscale Extraction and High Throughput UHPLC-QTOF-MS Analysis Method for Huperzine A in Huperzia

PubMed Central

Cuthbertson, Daniel; Piljac-Žegarac, Jasenka; Lange, Bernd Markus

2011-01-01

Herein we report on an improved method for the microscale extraction of huperzine A (HupA), an acetylcholinesterase-inhibiting alkaloid, from as little as 3 mg of tissue homogenate from the clubmoss Huperzia squarrosa (G. Forst.) Trevis with 99.95 % recovery. We also validated a novel UHPLC-QTOF-MS method for the high-throughput analysis of H. squarrosa extracts in only 6 min, which, in combination with the very low limit of detection (20 pg on column) and the wide linear range for quantification (20 to 10,000 pg on column), allow for a highly efficient screening of extracts containing varying amounts of HupA. Utilization of this methodology has the potential to conserve valuable plant resources. PMID:22275140

A simple and widely applicable hit validation strategy for protein-protein interaction inhibitors based on a quantitative ligand displacement assay.

PubMed

Sameshima, Tomoya; Miyahisa, Ikuo; Homma, Misaki; Aikawa, Katsuji; Hixon, Mark S; Matsui, Junji

2014-12-15

Identification of inhibitors for protein-protein interactions (PPIs) from high-throughput screening (HTS) is challenging due to the weak affinity of primary hits. We present a hit validation strategy of PPI inhibitors using quantitative ligand displacement assay. From an HTS for Bcl-xL/Mcl-1 inhibitors, we obtained a hit candidate, I1, which potentially forms a reactive Michael acceptor, I2, inhibiting Bcl-xL/Mcl-1 through covalent modification. We confirmed rapid reversible and competitive binding of I1 with a probe peptide, suggesting non-covalent binding. The advantages of our approach over biophysical assays include; simplicity, higher throughput, low protein consumption and universal application to PPIs including insoluble membrane proteins. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparative Transcriptomic Analyses of Vegetable and Grain Pea (Pisum sativum L.) Seed Development

PubMed Central

Liu, Na; Zhang, Guwen; Xu, Shengchun; Mao, Weihua; Hu, Qizan; Gong, Yaming

2015-01-01

Understanding the molecular mechanisms regulating pea seed developmental process is extremely important for pea breeding. In this study, we used high-throughput RNA-Seq and bioinformatics analyses to examine the changes in gene expression during seed development in vegetable pea and grain pea, and compare the gene expression profiles of these two pea types. RNA-Seq generated 18.7 G of raw data, which were then de novo assembled into 77,273 unigenes with a mean length of 930 bp. Our results illustrate that transcriptional control during pea seed development is a highly coordinated process. There were 459 and 801 genes differentially expressed at early and late seed maturation stages between vegetable pea and grain pea, respectively. Soluble sugar and starch metabolism related genes were significantly activated during the development of pea seeds coinciding with the onset of accumulation of sugar and starch in the seeds. A comparative analysis of genes involved in sugar and starch biosynthesis in vegetable pea (high seed soluble sugar and low starch) and grain pea (high seed starch and low soluble sugar) revealed that differential expression of related genes at late development stages results in a negative correlation between soluble sugar and starch biosynthetic flux in vegetable and grain pea seeds. RNA-Seq data was validated by using real-time quantitative RT-PCR analysis for 30 randomly selected genes. To our knowledge, this work represents the first report of seed development transcriptomics in pea. The obtained results provide a foundation to support future efforts to unravel the underlying mechanisms that control the developmental biology of pea seeds, and serve as a valuable resource for improving pea breeding. PMID:26635856

A High-Throughput Data Mining of Single Nucleotide Polymorphisms in Coffea Species Expressed Sequence Tags Suggests Differential Homeologous Gene Expression in the Allotetraploid Coffea arabica1[W

PubMed Central

Vidal, Ramon Oliveira; Mondego, Jorge Maurício Costa; Pot, David; Ambrósio, Alinne Batista; Andrade, Alan Carvalho; Pereira, Luiz Filipe Protasio; Colombo, Carlos Augusto; Vieira, Luiz Gonzaga Esteves; Carazzolle, Marcelo Falsarella; Pereira, Gonçalo Amarante Guimarães

2010-01-01

Polyploidization constitutes a common mode of evolution in flowering plants. This event provides the raw material for the divergence of function in homeologous genes, leading to phenotypic novelty that can contribute to the success of polyploids in nature or their selection for use in agriculture. Mounting evidence underlined the existence of homeologous expression biases in polyploid genomes; however, strategies to analyze such transcriptome regulation remained scarce. Important factors regarding homeologous expression biases remain to be explored, such as whether this phenomenon influences specific genes, how paralogs are affected by genome doubling, and what is the importance of the variability of homeologous expression bias to genotype differences. This study reports the expressed sequence tag assembly of the allopolyploid Coffea arabica and one of its direct ancestors, Coffea canephora. The assembly was used for the discovery of single nucleotide polymorphisms through the identification of high-quality discrepancies in overlapped expressed sequence tags and for gene expression information indirectly estimated by the transcript redundancy. Sequence diversity profiles were evaluated within C. arabica (Ca) and C. canephora (Cc) and used to deduce the transcript contribution of the Coffea eugenioides (Ce) ancestor. The assignment of the C. arabica haplotypes to the C. canephora (CaCc) or C. eugenioides (CaCe) ancestral genomes allowed us to analyze gene expression contributions of each subgenome in C. arabica. In silico data were validated by the quantitative polymerase chain reaction and allele-specific combination TaqMAMA-based method. The presence of differential expression of C. arabica homeologous genes and its implications in coffee gene expression, ontology, and physiology are discussed. PMID:20864545

Assessment of clinical analytical sensitivity and specificity of next-generation sequencing for detection of simple and complex mutations.

PubMed

Chin, Ephrem L H; da Silva, Cristina; Hegde, Madhuri

2013-02-19

Detecting mutations in disease genes by full gene sequence analysis is common in clinical diagnostic laboratories. Sanger dideoxy terminator sequencing allows for rapid development and implementation of sequencing assays in the clinical laboratory, but it has limited throughput, and due to cost constraints, only allows analysis of one or at most a few genes in a patient. Next-generation sequencing (NGS), on the other hand, has evolved rapidly, although to date it has mainly been used for large-scale genome sequencing projects and is beginning to be used in the clinical diagnostic testing. One advantage of NGS is that many genes can be analyzed easily at the same time, allowing for mutation detection when there are many possible causative genes for a specific phenotype. In addition, regions of a gene typically not tested for mutations, like deep intronic and promoter mutations, can also be detected. Here we use 20 previously characterized Sanger-sequenced positive controls in disease-causing genes to demonstrate the utility of NGS in a clinical setting using standard PCR based amplification to assess the analytical sensitivity and specificity of the technology for detecting all previously characterized changes (mutations and benign SNPs). The positive controls chosen for validation range from simple substitution mutations to complex deletion and insertion mutations occurring in autosomal dominant and recessive disorders. The NGS data was 100% concordant with the Sanger sequencing data identifying all 119 previously identified changes in the 20 samples. We have demonstrated that NGS technology is ready to be deployed in clinical laboratories. However, NGS and associated technologies are evolving, and clinical laboratories will need to invest significantly in staff and infrastructure to build the necessary foundation for success.

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Digital transcriptome profiling of normal and glioblastoma-derived neural stem cells identifies genes associated with patient survival

PubMed Central

2012-01-01

Background Glioblastoma multiforme, the most common type of primary brain tumor in adults, is driven by cells with neural stem (NS) cell characteristics. Using derivation methods developed for NS cells, it is possible to expand tumorigenic stem cells continuously in vitro. Although these glioblastoma-derived neural stem (GNS) cells are highly similar to normal NS cells, they harbor mutations typical of gliomas and initiate authentic tumors following orthotopic xenotransplantation. Here, we analyzed GNS and NS cell transcriptomes to identify gene expression alterations underlying the disease phenotype. Methods Sensitive measurements of gene expression were obtained by high-throughput sequencing of transcript tags (Tag-seq) on adherent GNS cell lines from three glioblastoma cases and two normal NS cell lines. Validation by quantitative real-time PCR was performed on 82 differentially expressed genes across a panel of 16 GNS and 6 NS cell lines. The molecular basis and prognostic relevance of expression differences were investigated by genetic characterization of GNS cells and comparison with public data for 867 glioma biopsies. Results Transcriptome analysis revealed major differences correlated with glioma histological grade, and identified misregulated genes of known significance in glioblastoma as well as novel candidates, including genes associated with other malignancies or glioma-related pathways. This analysis further detected several long non-coding RNAs with expression profiles similar to neighboring genes implicated in cancer. Quantitative PCR validation showed excellent agreement with Tag-seq data (median Pearson r = 0.91) and discerned a gene set robustly distinguishing GNS from NS cells across the 22 lines. These expression alterations include oncogene and tumor suppressor changes not detected by microarray profiling of tumor tissue samples, and facilitated the identification of a GNS expression signature strongly associated with patient survival (P = 1e-6, Cox model). Conclusions These results support the utility of GNS cell cultures as a model system for studying the molecular processes driving glioblastoma and the use of NS cells as reference controls. The association between a GNS expression signature and survival is consistent with the hypothesis that a cancer stem cell component drives tumor growth. We anticipate that analysis of normal and malignant stem cells will be an important complement to large-scale profiling of primary tumors. PMID:23046790

Selecting the most appropriate time points to profile in high-throughput studies

PubMed Central

Kleyman, Michael; Sefer, Emre; Nicola, Teodora; Espinoza, Celia; Chhabra, Divya; Hagood, James S; Kaminski, Naftali; Ambalavanan, Namasivayam; Bar-Joseph, Ziv

2017-01-01

Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPS DOI: http://dx.doi.org/10.7554/eLife.18541.001 PMID:28124972

Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens.

PubMed

Morgens, David W; Wainberg, Michael; Boyle, Evan A; Ursu, Oana; Araya, Carlos L; Tsui, C Kimberly; Haney, Michael S; Hess, Gaelen T; Han, Kyuho; Jeng, Edwin E; Li, Amy; Snyder, Michael P; Greenleaf, William J; Kundaje, Anshul; Bassik, Michael C

2017-05-05

CRISPR-Cas9 screens are powerful tools for high-throughput interrogation of genome function, but can be confounded by nuclease-induced toxicity at both on- and off-target sites, likely due to DNA damage. Here, to test potential solutions to this issue, we design and analyse a CRISPR-Cas9 library with 10 variable-length guides per gene and thousands of negative controls targeting non-functional, non-genic regions (termed safe-targeting guides), in addition to non-targeting controls. We find this library has excellent performance in identifying genes affecting growth and sensitivity to the ricin toxin. The safe-targeting guides allow for proper control of toxicity from on-target DNA damage. Using this toxicity as a proxy to measure off-target cutting, we demonstrate with tens of thousands of guides both the nucleotide position-dependent sensitivity to single mismatches and the reduction of off-target cutting using truncated guides. Our results demonstrate a simple strategy for high-throughput evaluation of target specificity and nuclease toxicity in Cas9 screens.

Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens

PubMed Central

Morgens, David W.; Wainberg, Michael; Boyle, Evan A.; Ursu, Oana; Araya, Carlos L.; Tsui, C. Kimberly; Haney, Michael S.; Hess, Gaelen T.; Han, Kyuho; Jeng, Edwin E.; Li, Amy; Snyder, Michael P.; Greenleaf, William J.; Kundaje, Anshul; Bassik, Michael C.

2017-01-01

CRISPR-Cas9 screens are powerful tools for high-throughput interrogation of genome function, but can be confounded by nuclease-induced toxicity at both on- and off-target sites, likely due to DNA damage. Here, to test potential solutions to this issue, we design and analyse a CRISPR-Cas9 library with 10 variable-length guides per gene and thousands of negative controls targeting non-functional, non-genic regions (termed safe-targeting guides), in addition to non-targeting controls. We find this library has excellent performance in identifying genes affecting growth and sensitivity to the ricin toxin. The safe-targeting guides allow for proper control of toxicity from on-target DNA damage. Using this toxicity as a proxy to measure off-target cutting, we demonstrate with tens of thousands of guides both the nucleotide position-dependent sensitivity to single mismatches and the reduction of off-target cutting using truncated guides. Our results demonstrate a simple strategy for high-throughput evaluation of target specificity and nuclease toxicity in Cas9 screens. PMID:28474669

High-throughput alternative splicing detection using dually constrained correspondence analysis (DCCA).

PubMed

Baty, Florent; Klingbiel, Dirk; Zappa, Francesco; Brutsche, Martin

2015-12-01

Alternative splicing is an important component of tumorigenesis. Recent advent of exon array technology enables the detection of alternative splicing at a genome-wide scale. The analysis of high-throughput alternative splicing is not yet standard and methodological developments are still needed. We propose a novel statistical approach-Dually Constrained Correspondence Analysis-for the detection of splicing changes in exon array data. Using this methodology, we investigated the genome-wide alteration of alternative splicing in patients with non-small cell lung cancer treated by bevacizumab/erlotinib. Splicing candidates reveal a series of genes related to carcinogenesis (SFTPB), cell adhesion (STAB2, PCDH15, HABP2), tumor aggressiveness (ARNTL2), apoptosis, proliferation and differentiation (PDE4D, FLT3, IL1R2), cell invasion (ETV1), as well as tumor growth (OLFM4, FGF14), tumor necrosis (AFF3) or tumor suppression (TUSC3, CSMD1, RHOBTB2, SERPINB5), with indication of known alternative splicing in a majority of genes. DCCA facilitates the identification of putative biologically relevant alternative splicing events in high-throughput exon array data. Copyright © 2015 Elsevier Inc. All rights reserved.

Raman-Activated Droplet Sorting (RADS) for Label-Free High-Throughput Screening of Microalgal Single-Cells.

PubMed

Wang, Xixian; Ren, Lihui; Su, Yetian; Ji, Yuetong; Liu, Yaoping; Li, Chunyu; Li, Xunrong; Zhang, Yi; Wang, Wei; Hu, Qiang; Han, Danxiang; Xu, Jian; Ma, Bo

2017-11-21

Raman-activated cell sorting (RACS) has attracted increasing interest, yet throughput remains one major factor limiting its broader application. Here we present an integrated Raman-activated droplet sorting (RADS) microfluidic system for functional screening of live cells in a label-free and high-throughput manner, by employing AXT-synthetic industrial microalga Haematococcus pluvialis (H. pluvialis) as a model. Raman microspectroscopy analysis of individual cells is carried out prior to their microdroplet encapsulation, which is then directly coupled to DEP-based droplet sorting. To validate the system, H. pluvialis cells containing different levels of AXT were mixed and underwent RADS. Those AXT-hyperproducing cells were sorted with an accuracy of 98.3%, an enrichment ratio of eight folds, and a throughput of ∼260 cells/min. Of the RADS-sorted cells, 92.7% remained alive and able to proliferate, which is equivalent to the unsorted cells. Thus, the RADS achieves a much higher throughput than existing RACS systems, preserves the vitality of cells, and facilitates seamless coupling with downstream manipulations such as single-cell sequencing and cultivation.

A simple cell-based high throughput screening (HTS) assay for inhibitors of Salmonella enterica RNA polymerase containing the general stress response regulator RpoS (σS).

PubMed

Campos-Gomez, Javier; Benitez, Jorge A

2018-07-01

RNA polymerase containing the stress response regulator σ S subunit (RpoS) plays a key role in bacterial survival in hostile environments in nature and during infection. Here we devise and validate a simple cell-based high throughput luminescence assay for this holoenzyme suitable for screening large chemical libraries in a robotic platform. Copyright © 2018 Elsevier B.V. All rights reserved.

Database for High Throughput Screening Hits (dHITS): a simple tool to retrieve gene specific phenotypes from systematic screens done in yeast.

PubMed

Chuartzman, Silvia G; Schuldiner, Maya

2018-03-25

In the last decade several collections of Saccharomyces cerevisiae yeast strains have been created. In these collections every gene is modified in a similar manner such as by a deletion or the addition of a protein tag. Such libraries have enabled a diversity of systematic screens, giving rise to large amounts of information regarding gene functions. However, often papers describing such screens focus on a single gene or a small set of genes and all other loci affecting the phenotype of choice ('hits') are only mentioned in tables that are provided as supplementary material and are often hard to retrieve or search. To help unify and make such data accessible, we have created a Database of High Throughput Screening Hits (dHITS). The dHITS database enables information to be obtained about screens in which genes of interest were found as well as the other genes that came up in that screen - all in a readily accessible and downloadable format. The ability to query large lists of genes at the same time provides a platform to easily analyse hits obtained from transcriptional analyses or other screens. We hope that this platform will serve as a tool to facilitate investigation of protein functions to the yeast community. © 2018 The Authors Yeast Published by John Wiley & Sons Ltd.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

PubMed

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-06-01

Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman's rank correlation coefficient and Blomqvist's measure, and compared them with Pearson's correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson's correlation, Spearman's rank correlation, and Blomqvist's coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist's coefficient was not confirmed by visual methods. Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Defined, serum/feeder-free conditions for expansion and drug screening of primary B-acute lymphoblastic leukemia.

PubMed

Jiang, Zhiwu; Wu, Di; Ye, Wei; Weng, Jianyu; Lai, Peilong; Shi, Pengcheng; Guo, Xutao; Huang, Guohua; Deng, Qiuhua; Tang, Yanlai; Zhao, Hongyu; Cui, Shuzhong; Lin, Simiao; Wang, Suna; Li, Baiheng; Wu, Qiting; Li, Yangqiu; Liu, Pentao; Pei, Duanqing; Du, Xin; Yao, Yao; Li, Peng

2017-12-05

Functional screening for compounds represents a major hurdle in the development of rational therapeutics for B-acute lymphoblastic leukemia (B-ALL). In addition, using cell lines as valid models for evaluating responses to novel drug therapies raises serious concerns, as cell lines are prone to genotypic/phenotypic drift and loss of heterogeneity in vitro . Here, we reported that OP9 cells, not OP9-derived adipocytes (OP9TA), support the growth of primary B-ALL cells in vitro . To identify the factors from OP9 cells that support the growth of primary B-ALL cells, we performed RNA-Seq to analyze the gene expression profiles of OP9 and OP9TA cells. We thus developed a defined, serum/feeder-free condition (FI76V) that can support the expansion of a range of clinically distinct primary B-ALL cells that still maintain their leukemia-initiating ability. We demonstrated the suitability of high-throughput drug screening based on our B-ALL cultured conditions. Upon screening 378 kinase inhibitors, we identified a cluster of 17 kinase inhibitors that can efficiently kill B-ALL cells in vitro . Importantly, we demonstrated the synergistic cytotoxicity of dinaciclib/BTG226 to B-ALL cells. Taken together, we developed a defined condition for the ex vivo expansion of primary B-ALL cells that is suitable for high-throughput screening of novel compounds.

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

Next generation tools for high-throughput promoter and expression analysis employing single-copy knock-ins at the Hprt1 locus.

PubMed

Yang, G S; Banks, K G; Bonaguro, R J; Wilson, G; Dreolini, L; de Leeuw, C N; Liu, L; Swanson, D J; Goldowitz, D; Holt, R A; Simpson, E M

2009-03-01

We have engineered a set of useful tools that facilitate targeted single copy knock-in (KI) at the hypoxanthine guanine phosphoribosyl transferase 1 (Hprt1) locus. We employed fine scale mapping to delineate the precise breakpoint location at the Hprt1(b-m3) locus allowing allele specific PCR assays to be established. Our suite of tools contains four targeting expression vectors and a complementing series of embryonic stem cell lines. Two of these vectors encode enhanced green fluorescent protein (EGFP) driven by the human cytomegalovirus immediate-early enhancer/modified chicken beta-actin (CAG) promoter, whereas the other two permit flexible combinations of a chosen promoter combined with a reporter and/or gene of choice. We have validated our tools as part of the Pleiades Promoter Project (http://www.pleiades.org), with the generation of brain-specific EGFP positive germline mouse strains.

Validating regulatory predictions from diverse bacteria with mutant fitness data

DOE PAGES

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.; ...

2017-05-24

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Validating regulatory predictions from diverse bacteria with mutant fitness data

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

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Use of signature-tagged mutagenesis to identify virulence determinants in Haemophilus ducreyi responsible for ulcer formation.

PubMed

Yeung, Angela; Cameron, D William; Desjardins, Marc; Lee, B Craig

2011-02-01

Elucidating the molecular mechanisms responsible for chancroid, a genital ulcer disease caused by Haemophilus ducreyi, has been hampered in part by the relative genetic intractability of the organism. A whole genome screen using signature-tagged mutagenesis in the temperature-dependent rabbit model (TDRM) of H. ducreyi infection uncovered 26 mutants with a presumptive attenuated phenotype. Insertions in two previously recognized virulence determinants, hgbA and lspA1, validated this genome scanning technique. Database interrogation allowed assignment of 24 mutants to several functional classes, including transport, metabolism, DNA repair, stress response and gene regulation. The attenuated virulence for a 3 strain with a mutation in hicB was confirmed by individual infection in the TDRM. The results from this preliminary study indicate that this high throughput strategy will further the understanding of the pathogenesis of H. ducreyi infection. Copyright © 2010 Elsevier B.V. All rights reserved.

Evaluation of the Bacterial Diversity in the Human Tongue Coating Based on Genus-Specific Primers for 16S rRNA Sequencing.

PubMed

Sun, Beili; Zhou, Dongrui; Tu, Jing; Lu, Zuhong

2017-01-01

The characteristics of tongue coating are very important symbols for disease diagnosis in traditional Chinese medicine (TCM) theory. As a habitat of oral microbiota, bacteria on the tongue dorsum have been proved to be the cause of many oral diseases. The high-throughput next-generation sequencing (NGS) platforms have been widely applied in the analysis of bacterial 16S rRNA gene. We developed a methodology based on genus-specific multiprimer amplification and ligation-based sequencing for microbiota analysis. In order to validate the efficiency of the approach, we thoroughly analyzed six tongue coating samples from lung cancer patients with different TCM types, and more than 600 genera of bacteria were detected by this platform. The results showed that ligation-based parallel sequencing combined with enzyme digestion and multiamplification could expand the effective length of sequencing reads and could be applied in the microbiota analysis.

From genes to protein mechanics on a chip.

PubMed

Otten, Marcus; Ott, Wolfgang; Jobst, Markus A; Milles, Lukas F; Verdorfer, Tobias; Pippig, Diana A; Nash, Michael A; Gaub, Hermann E

2014-11-01

Single-molecule force spectroscopy enables mechanical testing of individual proteins, but low experimental throughput limits the ability to screen constructs in parallel. We describe a microfluidic platform for on-chip expression, covalent surface attachment and measurement of single-molecule protein mechanical properties. A dockerin tag on each protein molecule allowed us to perform thousands of pulling cycles using a single cohesin-modified cantilever. The ability to synthesize and mechanically probe protein libraries enables high-throughput mechanical phenotyping.

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

PubMed

Failmezger, Henrik; Fröhlich, Holger; Tresch, Achim

2013-10-04

Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.

CRISPR-Cas9-Edited Site Sequencing (CRES-Seq): An Efficient and High-Throughput Method for the Selection of CRISPR-Cas9-Edited Clones.

PubMed

Veeranagouda, Yaligara; Debono-Lagneaux, Delphine; Fournet, Hamida; Thill, Gilbert; Didier, Michel

2018-01-16

The emergence of clustered regularly interspaced short palindromic repeats-Cas9 (CRISPR-Cas9) gene editing systems has enabled the creation of specific mutants at low cost, in a short time and with high efficiency, in eukaryotic cells. Since a CRISPR-Cas9 system typically creates an array of mutations in targeted sites, a successful gene editing project requires careful selection of edited clones. This process can be very challenging, especially when working with multiallelic genes and/or polyploid cells (such as cancer and plants cells). Here we described a next-generation sequencing method called CRISPR-Cas9 Edited Site Sequencing (CRES-Seq) for the efficient and high-throughput screening of CRISPR-Cas9-edited clones. CRES-Seq facilitates the precise genotyping up to 96 CRISPR-Cas9-edited sites (CRES) in a single MiniSeq (Illumina) run with an approximate sequencing cost of $6/clone. CRES-Seq is particularly useful when multiple genes are simultaneously targeted by CRISPR-Cas9, and also for screening of clones generated from multiallelic genes/polyploid cells. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

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.

EDdb: a web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder.

PubMed

Zhao, Min; Li, XiaoMo; Qu, Hong

2013-12-01

Eating disorder is a group of physiological and psychological disorders affecting approximately 1% of the female population worldwide. Although the genetic epidemiology of eating disorder is becoming increasingly clear with accumulated studies, the underlying molecular mechanisms are still unclear. Recently, integration of various high-throughput data expanded the range of candidate genes and started to generate hypotheses for understanding potential pathogenesis in complex diseases. This article presents EDdb (Eating Disorder database), the first evidence-based gene resource for eating disorder. Fifty-nine experimentally validated genes from the literature in relation to eating disorder were collected as the core dataset. Another four datasets with 2824 candidate genes across 601 genome regions were expanded based on the core dataset using different criteria (e.g., protein-protein interactions, shared cytobands, and related complex diseases). Based on human protein-protein interaction data, we reconstructed a potential molecular sub-network related to eating disorder. Furthermore, with an integrative pathway enrichment analysis of genes in EDdb, we identified an extended adipocytokine signaling pathway in eating disorder. Three genes in EDdb (ADIPO (adiponectin), TNF (tumor necrosis factor) and NR3C1 (nuclear receptor subfamily 3, group C, member 1)) link the KEGG (Kyoto Encyclopedia of Genes and Genomes) "adipocytokine signaling pathway" with the BioCarta "visceral fat deposits and the metabolic syndrome" pathway to form a joint pathway. In total, the joint pathway contains 43 genes, among which 39 genes are related to eating disorder. As the first comprehensive gene resource for eating disorder, EDdb ( http://eddb.cbi.pku.edu.cn ) enables the exploration of gene-disease relationships and cross-talk mechanisms between related disorders. Through pathway statistical studies, we revealed that abnormal body weight caused by eating disorder and obesity may both be related to dysregulation of the novel joint pathway of adipocytokine signaling. In addition, this joint pathway may be the common pathway for body weight regulation in complex human diseases related to unhealthy lifestyle.

From cancer genomes to cancer models: bridging the gaps

PubMed Central

Baudot, Anaïs; Real, Francisco X.; Izarzugaza, José M. G.; Valencia, Alfonso

2009-01-01

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high-throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high-throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer-evolution models will be influenced by the high-throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications. PMID:19305388

Throughput and delay analysis of IEEE 802.15.6-based CSMA/CA protocol.

PubMed

Ullah, Sana; Chen, Min; Kwak, Kyung Sup

2012-12-01

The IEEE 802.15.6 is a new communication standard on Wireless Body Area Network (WBAN) that focuses on a variety of medical, Consumer Electronics (CE) and entertainment applications. In this paper, the throughput and delay performance of the IEEE 802.15.6 is presented. Numerical formulas are derived to determine the maximum throughput and minimum delay limits of the IEEE 802.15.6 for an ideal channel with no transmission errors. These limits are derived for different frequency bands and data rates. Our analysis is validated by extensive simulations using a custom C+ + simulator. Based on analytical and simulation results, useful conclusions are derived for network provisioning and packet size optimization for different applications.

A high throughput soybean gene identification system developed using soybean yellow common mosaic virus (SYCMV)

USDA-ARS?s Scientific Manuscript database

Soybean yellow common mosaic virus (SYCMV) was recently reported from Korea, and a subsequent survey of soybean fields found that SYCMV, Soybean yellow mottle mosaic virus (SYMMV), and Soybean mosaic virus (SMV) infections were widespread. SYCMV has recently been developed into a Virus Inducing Gene...

Economic benefits of using adaptive predictive models of reproductive toxicity in the context of a tiered testing program

EPA Science Inventory

A predictive model of reproductive toxicity, as observed in rat multigeneration reproductive (MGR) studies, was previously developed using high throughput screening (HTS) data from 36 in vitro assays mapped to 8 genes or gene-sets from Phase I of USEPA ToxCast research program, t...

Searching for resistance genes to Bursaphelenchus xylophilus using high throughput screening

PubMed Central

2012-01-01

Background Pine wilt disease (PWD), caused by the pinewood nematode (PWN; Bursaphelenchus xylophilus), damages and kills pine trees and is causing serious economic damage worldwide. Although the ecological mechanism of infestation is well described, the plant’s molecular response to the pathogen is not well known. This is due mainly to the lack of genomic information and the complexity of the disease. High throughput sequencing is now an efficient approach for detecting the expression of genes in non-model organisms, thus providing valuable information in spite of the lack of the genome sequence. In an attempt to unravel genes potentially involved in the pine defense against the pathogen, we hereby report the high throughput comparative sequence analysis of infested and non-infested stems of Pinus pinaster (very susceptible to PWN) and Pinus pinea (less susceptible to PWN). Results Four cDNA libraries from infested and non-infested stems of P. pinaster and P. pinea were sequenced in a full 454 GS FLX run, producing a total of 2,083,698 reads. The putative amino acid sequences encoded by the assembled transcripts were annotated according to Gene Ontology, to assign Pinus contigs into Biological Processes, Cellular Components and Molecular Functions categories. Most of the annotated transcripts corresponded to Picea genes-25.4-39.7%, whereas a smaller percentage, matched Pinus genes, 1.8-12.8%, probably a consequence of more public genomic information available for Picea than for Pinus. The comparative transcriptome analysis showed that when P. pinaster was infested with PWN, the genes malate dehydrogenase, ABA, water deficit stress related genes and PAR1 were highly expressed, while in PWN-infested P. pinea, the highly expressed genes were ricin B-related lectin, and genes belonging to the SNARE and high mobility group families. Quantitative PCR experiments confirmed the differential gene expression between the two pine species. Conclusions Defense-related genes triggered by nematode infestation were detected in both P. pinaster and P. pinea transcriptomes utilizing 454 pyrosequencing technology. P. pinaster showed higher abundance of genes related to transcriptional regulation, terpenoid secondary metabolism (including some with nematicidal activity) and pathogen attack. P. pinea showed higher abundance of genes related to oxidative stress and higher levels of expression in general of stress responsive genes. This study provides essential information about the molecular defense mechanisms utilized by P. pinaster and P. pinea against PWN infestation and contributes to a better understanding of PWD. PMID:23134679

Identification of miRNAs Involved in Stolon Formation in Tulipa edulis by High-Throughput Sequencing

PubMed Central

Zhu, Zaibiao; Miao, Yuanyuan; Guo, Qiaosheng; Zhu, Yunhao; Yang, Xiaohua; Sun, Yuan

2016-01-01

MicroRNAs (miRNAs) are a class of endogenous, non-coding small RNAs that play an important role in transcriptional and post-transcriptional gene regulation. However, the sequence information and functions of miRNAs are still unexplored in Tulipa edulis. In this study, high-throughput sequencing was used to identify small RNAs in stolon formation stages (stage 1, 2, and 3) in T. edulis. A total of 12,890,912, 12,182,122, and 12,061,434 clean reads were obtained from stage 1, 2, and 3, respectively. Among the reads, 88 conserved miRNAs and 70 novel miRNAs were identified. Target prediction of 122 miRNAs resulted in 531 potential target genes. Nr, Swiss-Prot, GO, COG, and KEGG annotations revealed that these target genes participate in many biologic and metabolic processes. Moreover, qRT-PCR was performed to analyze the expression levels of the miRNAs and target genes in stolon formation. The results revealed that miRNAs play a key role in T. edulis stolon formation. PMID:27446103

Identification of miRNAs Involved in Stolon Formation in Tulipa edulis by High-Throughput Sequencing.

PubMed

Zhu, Zaibiao; Miao, Yuanyuan; Guo, Qiaosheng; Zhu, Yunhao; Yang, Xiaohua; Sun, Yuan

2016-01-01

MicroRNAs (miRNAs) are a class of endogenous, non-coding small RNAs that play an important role in transcriptional and post-transcriptional gene regulation. However, the sequence information and functions of miRNAs are still unexplored in Tulipa edulis. In this study, high-throughput sequencing was used to identify small RNAs in stolon formation stages (stage 1, 2, and 3) in T. edulis. A total of 12,890,912, 12,182,122, and 12,061,434 clean reads were obtained from stage 1, 2, and 3, respectively. Among the reads, 88 conserved miRNAs and 70 novel miRNAs were identified. Target prediction of 122 miRNAs resulted in 531 potential target genes. Nr, Swiss-Prot, GO, COG, and KEGG annotations revealed that these target genes participate in many biologic and metabolic processes. Moreover, qRT-PCR was performed to analyze the expression levels of the miRNAs and target genes in stolon formation. The results revealed that miRNAs play a key role in T. edulis stolon formation.

Whole Wiskott‑Aldrich syndrome protein gene deletion identified by high throughput sequencing.

PubMed

He, Xiangling; Zou, Runying; Zhang, Bing; You, Yalan; Yang, Yang; Tian, Xin

2017-11-01

Wiskott‑Aldrich syndrome (WAS) is a rare X‑linked recessive immunodeficiency disorder, characterized by thrombocytopenia, small platelets, eczema and recurrent infections associated with increased risk of autoimmunity and malignancy disorders. Mutations in the WAS protein (WASP) gene are responsible for WAS. To date, WASP mutations, including missense/nonsense, splicing, small deletions, small insertions, gross deletions, and gross insertions have been identified in patients with WAS. In addition, WASP‑interacting proteins are suspected in patients with clinical features of WAS, in whom the WASP gene sequence and mRNA levels are normal. The present study aimed to investigate the application of next generation sequencing in definitive diagnosis and clinical therapy for WAS. A 5 month‑old child with WAS who displayed symptoms of thrombocytopenia was examined. Whole exome sequence analysis of genomic DNA showed that the coverage and depth of WASP were extremely low. Quantitative polymerase chain reaction indicated total WASP gene deletion in the proband. In conclusion, high throughput sequencing is useful for the verification of WAS on the genetic profile, and has implications for family planning guidance and establishment of clinical programs.

TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.

PubMed

Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini

2018-05-14

Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.

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 .

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

PubMed Central

Yin, Zheng; Zhou, Xiaobo; Bakal, Chris; Li, Fuhai; Sun, Youxian; Perrimon, Norbert; Wong, Stephen TC

2008-01-01

Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens. PMID:18534020

Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data

PubMed Central

Hassane, Duane C.; Guzman, Monica L.; Corbett, Cheryl; Li, Xiaojie; Abboud, Ramzi; Young, Fay; Liesveld, Jane L.; Carroll, Martin

2008-01-01

Increasing evidence indicates that malignant stem cells are important for the pathogenesis of acute myelogenous leukemia (AML) and represent a reservoir of cells that drive the development of AML and relapse. Therefore, new treatment regimens are necessary to prevent relapse and improve therapeutic outcomes. Previous studies have shown that the sesquiterpene lactone, parthenolide (PTL), ablates bulk, progenitor, and stem AML cells while causing no appreciable toxicity to normal hematopoietic cells. Thus, PTL must evoke cellular responses capable of mediating AML selective cell death. Given recent advances in chemical genomics such as gene expression-based high-throughput screening (GE-HTS) and the Connectivity Map, we hypothesized that the gene expression signature resulting from treatment of primary AML with PTL could be used to search for similar signatures in publicly available gene expression profiles deposited into the Gene Expression Omnibus (GEO). We therefore devised a broad in silico screen of the GEO database using the PTL gene expression signature as a template and discovered 2 new agents, celastrol and 4-hydroxy-2-nonenal, that effectively eradicate AML at the bulk, progenitor, and stem cell level. These findings suggest the use of multicenter collections of high-throughput data to facilitate discovery of leukemia drugs and drug targets. PMID:18305216

Identification of Novel Pro-Migratory, Cancer-Associated Genes Using Quantitative, Microscopy-Based Screening

PubMed Central

Naffar-Abu-Amara, Suha; Shay, Tal; Galun, Meirav; Cohen, Naomi; Isakoff, Steven J.; Kam, Zvi; Geiger, Benjamin

2008-01-01

Background Cell migration is a highly complex process, regulated by multiple genes, signaling pathways and external stimuli. To discover genes or pharmacological agents that can modulate the migratory activity of cells, screening strategies that enable the monitoring of diverse migratory parameters in a large number of samples are necessary. Methodology In the present study, we describe the development of a quantitative, high-throughput cell migration assay, based on a modified phagokinetic tracks (PKT) procedure, and apply it for identifying novel pro-migratory genes in a cancer-related gene library. In brief, cells are seeded on fibronectin-coated 96-well plates, covered with a monolayer of carboxylated latex beads. Motile cells clear the beads, located along their migratory paths, forming tracks that are visualized using an automated, transmitted-light screening microscope. The tracks are then segmented and characterized by multi-parametric, morphometric analysis, resolving a variety of morphological and kinetic features. Conclusions In this screen we identified 4 novel genes derived from breast carcinoma related cDNA library, whose over-expression induces major alteration in the migration of the stationary MCF7 cells. This approach can serve for high throughput screening for novel ways to modulate cellular migration in pathological states such as tumor metastasis and invasion. PMID:18213366

Leishmania genome analysis and high-throughput immunological screening identifies tuzin as a novel vaccine candidate against visceral leishmaniasis.

PubMed

Lakshmi, Bhavana Sethu; Wang, Ruobing; Madhubala, Rentala

2014-06-24

Leishmaniasis is a neglected tropical disease caused by Leishmania species. It is a major health concern affecting 88 countries and threatening 350 million people globally. Unfortunately, there are no vaccines and there are limitations associated with the current therapeutic regimens for leishmaniasis. The emerging cases of drug-resistance further aggravate the situation, demanding rapid drug and vaccine development. The genome sequence of Leishmania, provides access to novel genes that hold potential as chemotherapeutic targets or vaccine candidates. In this study, we selected 19 antigenic genes from about 8000 common Leishmania genes based on the Leishmania major and Leishmania infantum genome information available in the pathogen databases. Potential vaccine candidates thus identified were screened using an in vitro high throughput immunological platform developed in the laboratory. Four candidate genes coding for tuzin, flagellar glycoprotein-like protein (FGP), phospholipase A1-like protein (PLA1) and potassium voltage-gated channel protein (K VOLT) showed a predominant protective Th1 response over disease exacerbating Th2. We report the immunogenic properties and protective efficacy of one of the four antigens, tuzin, as a DNA vaccine against Leishmania donovani challenge. Our results show that administration of tuzin DNA protected BALB/c mice against L. donovani challenge and that protective immunity was associated with higher levels of IFN-γ and IL-12 production in comparison to IL-4 and IL-10. Our study presents a simple approach to rapidly identify potential vaccine candidates using the exhaustive information stored in the genome and an in vitro high-throughput immunological platform. Copyright © 2014. Published by Elsevier Ltd.

Rapid Quantification of Mutant Fitness in Diverse Bacteria by Sequencing Randomly Bar-Coded Transposons

PubMed Central

Wetmore, Kelly M.; Price, Morgan N.; Waters, Robert J.; Lamson, Jacob S.; He, Jennifer; Hoover, Cindi A.; Blow, Matthew J.; Bristow, James; Butland, Gareth

2015-01-01

ABSTRACT Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative d-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. PMID:25968644

High Diversity of Myocyanophage in Various Aquatic Environments Revealed by High-Throughput Sequencing of Major Capsid Protein Gene With a New Set of Primers.

PubMed

Hou, Weiguo; Wang, Shang; Briggs, Brandon R; Li, Gaoyuan; Xie, Wei; Dong, Hailiang

2018-01-01

Myocyanophages, a group of viruses infecting cyanobacteria, are abundant and play important roles in elemental cycling. Here we investigated the particle-associated viral communities retained on 0.2 μm filters and in sediment samples (representing ancient cyanophage communities) from four ocean and three lake locations, using high-throughput sequencing and a newly designed primer pair targeting a gene fragment (∼145-bp in length) encoding the cyanophage gp23 major capsid protein (MCP). Diverse viral communities were detected in all samples. The fragments of 142-, 145-, and 148-bp in length were most abundant in the amplicons, and most sequences (>92%) belonged to cyanophages. Additionally, different sequencing depths resulted in different diversity estimates of the viral community. Operational taxonomic units obtained from deep sequencing of the MCP gene covered the majority of those obtained from shallow sequencing, suggesting that deep sequencing exhibited a more complete picture of cyanophage community than shallow sequencing. Our results also revealed a wide geographic distribution of marine myocyanophages, i.e., higher dissimilarities of the myocyanophage communities corresponded with the larger distances between the sampling sites. Collectively, this study suggests that the newly designed primer pair can be effectively used to study the community and diversity of myocyanophage from different environments, and the high-throughput sequencing represents a good method to understand viral diversity.

High Diversity of Myocyanophage in Various Aquatic Environments Revealed by High-Throughput Sequencing of Major Capsid Protein Gene With a New Set of Primers

PubMed Central

Hou, Weiguo; Wang, Shang; Briggs, Brandon R.; Li, Gaoyuan; Xie, Wei; Dong, Hailiang

2018-01-01

Myocyanophages, a group of viruses infecting cyanobacteria, are abundant and play important roles in elemental cycling. Here we investigated the particle-associated viral communities retained on 0.2 μm filters and in sediment samples (representing ancient cyanophage communities) from four ocean and three lake locations, using high-throughput sequencing and a newly designed primer pair targeting a gene fragment (∼145-bp in length) encoding the cyanophage gp23 major capsid protein (MCP). Diverse viral communities were detected in all samples. The fragments of 142-, 145-, and 148-bp in length were most abundant in the amplicons, and most sequences (>92%) belonged to cyanophages. Additionally, different sequencing depths resulted in different diversity estimates of the viral community. Operational taxonomic units obtained from deep sequencing of the MCP gene covered the majority of those obtained from shallow sequencing, suggesting that deep sequencing exhibited a more complete picture of cyanophage community than shallow sequencing. Our results also revealed a wide geographic distribution of marine myocyanophages, i.e., higher dissimilarities of the myocyanophage communities corresponded with the larger distances between the sampling sites. Collectively, this study suggests that the newly designed primer pair can be effectively used to study the community and diversity of myocyanophage from different environments, and the high-throughput sequencing represents a good method to understand viral diversity.

Multiplex enrichment quantitative PCR (ME-qPCR): a high-throughput, highly sensitive detection method for GMO identification.

PubMed

Fu, Wei; Zhu, Pengyu; Wei, Shuang; Zhixin, Du; Wang, Chenguang; Wu, Xiyang; Li, Feiwu; Zhu, Shuifang

2017-04-01

Among all of the high-throughput detection methods, PCR-based methodologies are regarded as the most cost-efficient and feasible methodologies compared with the next-generation sequencing or ChIP-based methods. However, the PCR-based methods can only achieve multiplex detection up to 15-plex due to limitations imposed by the multiplex primer interactions. The detection throughput cannot meet the demands of high-throughput detection, such as SNP or gene expression analysis. Therefore, in our study, we have developed a new high-throughput PCR-based detection method, multiplex enrichment quantitative PCR (ME-qPCR), which is a combination of qPCR and nested PCR. The GMO content detection results in our study showed that ME-qPCR could achieve high-throughput detection up to 26-plex. Compared to the original qPCR, the Ct values of ME-qPCR were lower for the same group, which showed that ME-qPCR sensitivity is higher than the original qPCR. The absolute limit of detection for ME-qPCR could achieve levels as low as a single copy of the plant genome. Moreover, the specificity results showed that no cross-amplification occurred for irrelevant GMO events. After evaluation of all of the parameters, a practical evaluation was performed with different foods. The more stable amplification results, compared to qPCR, showed that ME-qPCR was suitable for GMO detection in foods. In conclusion, ME-qPCR achieved sensitive, high-throughput GMO detection in complex substrates, such as crops or food samples. In the future, ME-qPCR-based GMO content identification may positively impact SNP analysis or multiplex gene expression of food or agricultural samples. Graphical abstract For the first-step amplification, four primers (A, B, C, and D) have been added into the reaction volume. In this manner, four kinds of amplicons have been generated. All of these four amplicons could be regarded as the target of second-step PCR. For the second-step amplification, three parallels have been taken for the final evaluation. After the second evaluation, the final amplification curves and melting curves have been achieved.

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.

PubMed

Gong, Ting; Szustakowski, Joseph D

2013-04-15

For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are confounded by relative proportions of cell types involved. In this note, we introduce an efficient pipeline: DeconRNASeq, an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It adopts a globally optimized non-negative decomposition algorithm through quadratic programming for estimating the mixing proportions of distinctive tissue types in next-generation sequencing data. We demonstrated the feasibility and validity of DeconRNASeq across a range of mixing levels and sources using mRNA-Seq data mixed in silico at known concentrations. We validated our computational approach for various benchmark data, with high correlation between our predicted cell proportions and the real fractions of tissues. Our study provides a rigorous, quantitative and high-resolution tool as a prerequisite to use mRNA-Seq data. The modularity of package design allows an easy deployment of custom analytical pipelines for data from other high-throughput platforms. DeconRNASeq is written in R, and is freely available at http://bioconductor.org/packages. Supplementary data are available at Bioinformatics online.

Small RNA Transcriptome of Hibiscus Syriacus Provides Insights into the Potential Influence of microRNAs in Flower Development and Terpene Synthesis.

PubMed

Kim, Taewook; Park, June Hyun; Lee, Sang-Gil; Kim, Soyoung; Kim, Jihyun; Lee, Jungho; Shin, Chanseok

2017-08-01

MicroRNAs (miRNAs) are essential small RNA molecules that regulate the expression of target mRNAs in plants and animals. Here, we aimed to identify miRNAs and their putative targets in Hibiscus syriacus , the national flower of South Korea. We employed high-throughput sequencing of small RNAs obtained from four different tissues ( i.e. , leaf, root, flower, and ovary) and identified 33 conserved and 30 novel miRNA families, many of which showed differential tissue-specific expressions. In addition, we computationally predicted novel targets of miRNAs and validated some of them using 5' rapid amplification of cDNA ends analysis. One of the validated novel targets of miR477 was a terpene synthase, the primary gene involved in the formation of disease-resistant terpene metabolites such as sterols and phytoalexins. In addition, a predicted target of conserved miRNAs, miR396, is SHORT VEGETATIVE PHASE , which is involved in flower initiation and is duplicated in H. syriacus . Collectively, this study provides the first reliable draft of the H. syriacus miRNA transcriptome that should constitute a basis for understanding the biological roles of miRNAs in H. syriacus.

Application of Large-Scale Aptamer-Based Proteomic Profiling to Planned Myocardial Infarctions.

PubMed

Jacob, Jaison; Ngo, Debby; Finkel, Nancy; Pitts, Rebecca; Gleim, Scott; Benson, Mark D; Keyes, Michelle J; Farrell, Laurie A; Morgan, Thomas; Jennings, Lori L; Gerszten, Robert E

2018-03-20

Emerging proteomic technologies using novel affinity-based reagents allow for efficient multiplexing with high-sample throughput. To identify early biomarkers of myocardial injury, we recently applied an aptamer-based proteomic profiling platform that measures 1129 proteins to samples from patients undergoing septal alcohol ablation for hypertrophic cardiomyopathy, a human model of planned myocardial injury. Here, we examined the scalability of this approach using a markedly expanded platform to study a far broader range of human proteins in the context of myocardial injury. We applied a highly multiplexed, expanded proteomic technique that uses single-stranded DNA aptamers to assay 4783 human proteins (4137 distinct human gene targets) to derivation and validation cohorts of planned myocardial injury, individuals with spontaneous myocardial infarction, and at-risk controls. We found 376 target proteins that significantly changed in the blood after planned myocardial injury in a derivation cohort (n=20; P <1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold). Two hundred forty-seven of these proteins were validated in an independent planned myocardial injury cohort (n=15; P <1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consistent and reached nominal significance in the validation cohort. Among the validated proteins that were increased within 1 hour after planned myocardial injury, 29 were also elevated in patients with spontaneous myocardial infarction (n=63; P <6.17E-04). Many of the novel markers identified in our study are intracellular proteins not previously identified in the peripheral circulation or have functional roles relevant to myocardial injury. For example, the cardiac LIM protein, cysteine- and glycine-rich protein 3, is thought to mediate cardiac mechanotransduction and stress responses, whereas the mitochondrial ATP synthase F 0 subunit component is a vasoactive peptide on its release from cells. Last, we performed aptamer-affinity enrichment coupled with mass spectrometry to technically verify aptamer specificity for a subset of the new biomarkers. Our results demonstrate the feasibility of large-scale aptamer multiplexing at a level that has not previously been reported and with sample throughput that greatly exceeds other existing proteomic methods. The expanded aptamer-based proteomic platform provides a unique opportunity for biomarker and pathway discovery after myocardial injury. © 2017 American Heart Association, Inc.

WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

PubMed Central

Yi, Ming; Horton, Jay D; Cohen, Jonathan C; Hobbs, Helen H; Stephens, Robert M

2006-01-01

Background Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data. Result WPS extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. WPS incorporates well-known biological pathways from KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta, GO (Gene Ontology) terms as well as user-defined pathways or relevant gene clusters or groups, and explores gene-term relationships within the derived gene-term association networks (GTANs). WPS simultaneously compares multiple datasets within biological contexts either as pathways or as association networks. WPS also integrates Genetic Association Database and Partial MedGene Database for disease-association information. We have used this program to analyze and compare microarray and proteomics datasets derived from a variety of biological systems. Application examples demonstrated the capacity of WPS to significantly facilitate the analysis of HTP data for integrative discovery. Conclusion This tool represents a pathway-based platform for discovery integration to maximize analysis power. The tool is freely available at . PMID:16423281

Detoxification of Atrazine by Low Molecular Weight Thiols in Alfalfa (Medicago sativa).

PubMed

Zhang, Jing Jing; Xu, Jiang Yan; Lu, Feng Fan; Jin, She Feng; Yang, Hong

2017-10-16

Low molecular weight (LMW) thiols in higher plants are a group of sulfur-rich nonprotein compounds and play primary and multiple roles in cellular redox homeostasis, enzyme activities, and xenobiotics detoxification. This study focused on identifying thiols-related protein genes from the legume alfalfa exposed to the herbicide atrazine (ATZ) residues in environment. Using high-throughput RNA-sequencing, a set of ATZ-responsive thiols-related protein genes highly up-regulated and differentially expressed in alfalfa was identified. Most of the differentially expressed genes (DEGs) were involved in regulation of biotic and abiotic stress responses. By analyzing the genes involved in thiols-mediated redox homeostasis, we found that many of them were thiols-synthetic enzymes such as γ-glutamylcysteine synthase (γECS), homoglutathione synthetase (hGSHS), and glutathione synthetase (GSHS). Using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS), we further characterized a group of ATZ-thiols conjugates, which are the detoxified forms of ATZ in plants. Cysteine S-conjugate ATZ-HCl+Cys was the most important metabolite detected by MS. Several other ATZ-conjugates were also examined as ATZ-detoxified metabolites. Such results were validated by characterizing their analogs in rice. Our data showed that some conjugates under ATZ stress were detected in both plants, indicating that some detoxified mechanisms and pathways can be shared by the two plant species. Overall, these results indicate that LMW thiols play critical roles in detoxification of ATZ in the plants.

Biomedical discovery acceleration, with applications to craniofacial development.

PubMed

Leach, Sonia M; Tipney, Hannah; Feng, Weiguo; Baumgartner, William A; Kasliwal, Priyanka; Schuyler, Ronald P; Williams, Trevor; Spritz, Richard A; Hunter, Lawrence

2009-03-01

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.