scholarly journals The gene expression network regulating queen brain remodeling after insemination and its parallel use in ants with reproductive workers

2020 ◽  
Vol 6 (38) ◽  
pp. eaaz5772 ◽  
Author(s):  
Manuel Nagel ◽  
Bitao Qiu ◽  
Lisa Eigil Brandenborg ◽  
Rasmus Stenbak Larsen ◽  
Dongdong Ning ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Brain Volume ◽  
Neurosecretory Cells ◽  
Caste Differentiation ◽  
Specific Gene ◽  
Brain Anatomy ◽  
Brain Growth ◽  
Role Differentiation ◽  
Gene Regulatory ◽  
Worker Caste

Caste differentiation happens early in development to produce gynes as future colony germlines and workers as present colony soma. However, gynes need insemination to become functional queens, a transition that initiates reproductive role differentiation relative to unmated gynes. Here, we analyze the anatomy and transcriptomes of brains during this differentiation process within the reproductive caste of Monomorium pharaonis. Insemination terminated brain growth, whereas unmated control gynes continued to increase brain volume. Transcriptomes revealed a specific gene regulatory network (GRN) mediating both brain anatomy changes and behavioral modifications. This reproductive role differentiation GRN hardly overlapped with the gyne-worker caste differentiation GRN, but appears to be also used by distantly related ants where workers became germline individuals after the queen caste was entirely or partially lost. The genes corazonin and neuroparsin A in the anterior neurosecretory cells were overexpressed in individuals with reduced or nonreproductive roles across all four ant species investigated.

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 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 Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

scholarly journals Interpreting ruminant specific conserved non-coding elements by developmental gene regulatory network

2021 ◽  
Author(s):  
Xiangyu Pan ◽  
Zhaoxia Ma ◽  
Xinqi Sun ◽  
Hui Li ◽  
Tingting Zhang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Systematic Approach ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Developmental Gene ◽  
Gene Regulatory ◽  
Evo Devo ◽  
Interpretation Method

Biologists long recognized that the genetic information encoded in DNA leads to trait innovation via gene regulatory network (GRN) in development. Here, we generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep and revealed 1,601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we developed a Conserved Non-coding Element interpretation method by gene Regulatory network (CNEReg) to define toolkit transcription factors (TTF) and model its regulation on rumen specific gene via batteries of active-RSCNEs during development. Our developmental GRN reveals 18 TTFs and 313 active-RSCNEs regulating the functional modules of the rumen and identifies OTX1, SOX21, HOXC8, SOX2, TP63, PPARG and 16 active-RSCNEs that functionally distinguish the rumen from the esophagus. We argue that CNEReg is an attractive systematic approach to integrate evo-devo concepts with omics data to understand how gene regulation evolves and shapes complex traits.

scholarly journals Single-Cell Analysis of Alternative Splicing and Gene Regulatory Network Reveals Remarkable Expression and Regulation Dynamics During Early Embryonic Development

2021 ◽  
Author(s):  
Jiwei Chen ◽  
Yunjin Li ◽  
Geng Chen ◽  
Tieliu Shi
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Embryonic Development ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Early Embryonic Development ◽  
Expression Level ◽  
Specific Gene ◽  
Isoform Switching ◽  
Gene Regulatory

Abstract BackgroundSingle-cell RNA-seq (scRNA-seq) technologies greatly revolutionized our understanding of cell-to-cell variability of gene expression. Although several studies investigated the expression profile of early embryos, they mainly focused on the expression changes at gene level. Here we systematically explored the gene expression dynamics of human early embryonic development from expression level, alternative splicing, isoform switching and expression regulatory network. ResultsWe found that the genes involved in significant changes of these three aspects are all gradually decreased along embryonic development from E3 to E7 stage. Moreover, these three types of variations are complementary for profiling expression dynamics and they vary greatly across embryonic development as well as between different sexes. Strikingly, only a small number of genes exhibited prominent expression level changes between male and female embryos in E3 stage, whereas many more genes showed variations in alternative splicing and major isoform switching. Additionally, we identified functionally important specific gene regulatory modules for each stage and revealed dynamic usage of transcription factor binding motifs (TFBMs). ConclusionsCollectively, our study gain insights into the expression dynamics of early embryonic development from expression level, alternative splicing, isoform switching and gene regulatory networks, which could benefit the understanding of underlying mechanism of embryonic development.

scholarly journals Identification of biomarker in brain-specific gene regulatory network using structural controllability analysis

2021 ◽  
Author(s):  
Zhihua Chen ◽  
Siyuan Chen ◽  
Xiaoli Qiang
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Specific Gene ◽  
Topological Properties ◽  
Related Gene ◽  
Critical Nodes ◽  
Cancer Related Gene ◽  
Structural Controllability ◽  
Gene Regulatory ◽  
Node Classification

Abstract Background: Brain tumor research has been stapled for human health while brain network research is crucial for us to understand brain activity. Methods: Here the structural controllability theory is applied to study three human brain-specific gene regulatory networks, including forebrain gene regulatory network, hindbrain gene regulatory network and neuron associated cells cancer related gene regulatory network, hose nodes are neural genes and the edges represent the gene expression regulation among the genes. The nodes are classified into two classes: critical nodes and ordinary nodes, based on the change of the number of driver nodes upon its removal. Eight topological properties out-degree DO, in-degree DI, degree D, betweenness B, closeness CA, in-closeness CI, out-closeness CO and clustering coefficient CC) are calculated in this paper and the results prove that the critical genes have higher score of topological properties than the ordinary genes. Then two bioinformatic analysis are used to explore the biologic significance of the critical genes. Results: On the one hand, the enrichment scores in several kinds of gene databases are calculated and reveal that the critical nodes are richer in essential genes, cancer genes and the neuron related disease genes than the ordinary nodes, which indicates that the critical nodes may be the biomarker in brain-specific gene regulatory network. On the other hand, GO analysis and KEGG pathway analysis are applied on them and the results show that the critical genes mainly take part in 14 KEGG pathways that are transcriptional misregulation in cancer, pathways in cancer and so on, which indicates that the critical genes are related to the brain tumor. Finally, by deleting the edges or routines in the network, the robustness analysis of node classification is realized, and the robustness of node classification is proved. Conclusion: The comparison of neuron associated cells cancer related GRN and normal brain-specific GRNs (including forebrain and hindbrain GRN) shows that the neuron-related cell cancer-related gene regulatory network is more robust than other types.

scholarly journals GRAND: a database of gene regulatory network models across human conditions

2021 ◽  
Author(s):  
Marouen Ben Guebila ◽  
Camila M Lopes-Ramos ◽  
Deborah Weighill ◽  
Abhijeet Rajendra Sonawane ◽  
Rebekka Burkholz ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Small Molecule ◽  
Regulatory Network ◽  
Web Application ◽  
Target Genes ◽  
Network Models ◽  
Response To Therapy ◽  
Specific Gene ◽  
Phenotypic Information ◽  
Gene Regulatory

Abstract Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Targeting the germline-specific gene regulatory network in testicular germ cell tumours

2019 ◽  
Vol 18 (4) ◽  
pp. 13
Author(s):  
W.W.C. Tang ◽  
J.P. Alves-Lopes ◽  
A.C. Venzor ◽  
F.C.K. Wong ◽  
A. Kristian ◽  
...  
Keyword(s):  
Germ Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Specific Gene ◽  
Germ Cell Tumours ◽  
Testicular Germ Cell ◽  
Testicular Germ Cell Tumours ◽  
Gene Regulatory

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 .

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