scholarly journals An estimation method for a cellular-state-specific gene regulatory network along tree-structured gene expression profiles

Gene ◽  
2013 ◽  
Vol 518 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Ryo Araki ◽  
Shigeto Seno ◽  
Yoichi Takenaka ◽  
Hideo Matsuda
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Estimation Method ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Gene Regulatory

scholarly journals Modeling Gene Networks inSaccharomyces cerevisiaeBased on Gene Expression Profiles

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yulin Zhang ◽  
Kebo Lv ◽  
Shudong Wang ◽  
Jionglong Su ◽  
Dazhi Meng
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clustering Coefficient ◽  
Anaerobic Conditions ◽  
Gene Regulatory ◽  
Aerobic And Anaerobic ◽  
Aerobic And Anaerobic Conditions

Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network inSaccharomyces cerevisiaecan differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.

scholarly journals DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

2019 ◽  
Author(s):  
Heeju Noh ◽  
Ziyi Hua ◽  
Panagiotis Chrysinas ◽  
Jason E. Shoemaker ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Direct Gene ◽  
Gene Regulatory

AbstractBackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

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 DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Heeju Noh ◽  
Ziyi Hua ◽  
Panagiotis Chrysinas ◽  
Jason E. Shoemaker ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Direct Gene ◽  
Gene Regulatory

Abstract Background Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. Results In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. Conclusion DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

scholarly journals Inference of Gene Regulatory Network Uncovers the Linkage Between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

2019 ◽  
Author(s):  
Robert C. Moseley ◽  
Francis Motta ◽  
Gerald A. Tuskan ◽  
Steve Haase ◽  
Xiaohan Yang
Keyword(s):  
Gene Expression ◽  
Circadian Clock ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Crassulacean Acid Metabolism ◽  
Acid Metabolism ◽  
Clock Genes ◽  
Specific Gene ◽  
Cam Plants ◽  
Gene Regulatory

AbstractThe circadian clock drives time-specific gene expression, allowing for associated biological processes to be active during certain times of the 24 h day. Crassulacean acid metabolism (CAM) photosynthetic plants represent an interesting case of circadian regulation of gene expression as CO2 fixation and stomatal movement in CAM plants display strong circadian dynamics. The molecular mechanisms behind how the circadian clock enabled these physiological differences is not well understood. Therefore, we set out to investigate whether core circadian elements in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply connected to different core elements. We utilized a new metric for identifying candidate core genes of a periodic gene network and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between the candidate core clock genes and orthologs of known core clock genes in K. fedtschenkoi. We also used LEM to identify stomata-related gene targets for K. fedtschenkoi core clock genes and constructed a subsequent gene regulatory network. Our results provide new insights into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

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