UNCOVER CONTEXT-SPECIFIC GENE REGULATION BY TRANSCRIPTION FACTORS AND microRNAs USING BAYESIAN SPARSE NONNEGATIVE FACTOR REGRESSION

2012 ◽  
Vol 20 (04) ◽  
pp. 377-402
Author(s):  
JIA MENG ◽  
YIDONG CHEN ◽  
YUFEI HUANG
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Gibbs Sampling ◽  
Regulatory Network ◽  
Messenger Rna ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Data Types ◽  
Gene Regulatory ◽  
Factor Regression

In multicellular organisms, transcription factors (TFs) and microRNAs (miRNA) embody two largest families of molecules that modulate messenger RNA (mRNA) expressions through transcriptional and post-transcriptional regulations. While mRNA and microRNA expressions can be measured by microarray technique, the activities of transcription factors manifested by their protein expression are still difficult to observe, making it usually a complex problem to reconstruct a collaborative gene regulatory network (GRN) by TFs and miRNAs from expression data. In this paper, a novel Bayesian sparse non-negative factor regression (BSNFR) model is proposed for modeling the joint regulations of mRNAs by TFs and miRNAs and integration of multiple data types including gene expressions, microRNA expressions, TF targeted genes, and microRNA targets. Powered by a Gibbs sampling solution, BSNFR can infer both the TF/microRNA-mediated mRNA regulations and the unknown TF activities. Additionally, since BSNFR directly models the non-negative activities of TFs, it avoids the common problem of sign ambiguity with factor models and is capable of accurate prediction of the types (up or down) of regulations as well. BSNFR also includes a nonparametric Bayesian model for the latent factor activities, which enables the discovery of the clustering effects among samples due to (disease) subtypes. The proposed BSNFR model and the developed Gibbs sampling solution were validated on simulated systems and applied to real data of glioblastoma multiforme (GBM) patients from The Cancer Genome Atlas (TCGA). A GBM specific gene regulatory network by TFs and miRNAs was reconstructed. This GBM network includes 107 regulations recorded in the existing databases and 16 new regulations. Functional analysis suggests that the regulated genes are enriched in cell cycle and P53 pathways. In addition, BSNFR also identified 3 clusters among GBM patient samples, two of which demonstrates significant survival differences (p=0.004). Finally, the estimated TF activities imply that EGR-1 is significantly correlated with patient survivals (p=0.004) and may be used as a prognostic biomarker. The data and matlab code are available at: http://compgenomics.cbi.utsa.edu/BSNFR .

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

2019 ◽  
Vol 180 (3) ◽  
pp. 1740-1755 ◽  
Author(s):  
Philippa Borrill ◽  
Sophie A. Harrington ◽  
James Simmonds ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Modelling ◽  
Gene Regulatory

scholarly journals The identification of transcription factors expressed in the notochord of Ciona intestinalis adds new potential players to the brachyury gene regulatory network

2011 ◽  
Vol 240 (9) ◽  
pp. spcone-spcone
Author(s):  
Diana S. José-Edwards ◽  
Pierre Kerner ◽  
Jamie E. Kugler ◽  
Wei Deng ◽  
Di Jiang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Ciona Intestinalis ◽  
Gene Regulatory

scholarly journals Identification of transcription factors regulating senescence in wheat through gene regulatory network modelling

2018 ◽  
Author(s):  
Philippa Borrill ◽  
Sophie A. Harrington ◽  
James Simmonds ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Nutrient Content ◽  
Series Data ◽  
Network Modelling ◽  
Polyploid Wheat ◽  
Gene Regulatory ◽  
Developmental Programme

AbstractSenescence is a tightly regulated developmental programme which is coordinated by transcription factors. Identifying these transcription factors in crops will provide opportunities to tailor the senescence process to different environmental conditions and regulate the balance between yield and grain nutrient content. Here we use ten time points of gene expression data alongside gene network modelling to identify transcription factors regulating senescence in polyploid wheat. We observe two main phases of transcription changes during senescence: early downregulation of housekeeping and metabolic processes followed by upregulation of transport and hormone related genes. We have identified transcription factor families associated with these early and later waves of differential expression. Using gene regulatory network modelling alongside complementary publicly available datasets we identified candidate transcription factors for controlling senescence. We validated the function of one of these candidate transcription factors in senescence using wheat chemically-induced mutants. This study lays the ground work to understand the transcription factors which regulate senescence in polyploid wheat and exemplifies the integration of time-series data with publicly available expression atlases and networks to identify candidate regulatory genes.

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Gene regulatory network analysis of Trichoderma reesei grown on glucose-lactose mixtures suggests links between development and cellulase production

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Batch Mode ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact

Abstract Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network.Results: Experimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using the BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth.Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1 , clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

scholarly journals Tbx4 function during hindlimb development reveals a novel mechanism to explain the origins of proximal limb defects

Development ◽  
2021 ◽  
Author(s):  
Veronique Duboc ◽  
Fatima Sulaiman ◽  
Eleanor Feneck ◽  
Anna Kucharska ◽  
Donald Bell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Micromass Culture ◽  
Limb Defects ◽  
Limb Mesenchyme ◽  
Gene Regulatory ◽  
Live Image ◽  
Mesenchyme Cells

We dissect genetically a gene regulatory network, including the transcription factors Tbx4, Pitx1 and Isl1 that act cooperatively to establish the hindlimb bud and identify key differences in the pathways that initiate formation of the hindlimb and forelimb. Using live image analysis of limb mesenchyme cells undergoing chondrogenesis in micromass culture, we distinguish a series of changes in cellular behaviours and cohesiveness that are required for chondrogenic precursors to undergo differentiation. Furthermore, we provide evidence that the proximal hindlimb defects in the Tbx4 mutant result from a failure in the early differentiation step of chondroprogenitors into chondrocytes, providing a novel explanation for the origins of proximally-biased limb defects.

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