scholarly journals Parent and offspring genotypes influence gene expression in early life

2018 ◽  
Author(s):  
Daniel J. Newhouse ◽  
Margarida Barcelo-Serra ◽  
Elaina M. Tuttle ◽  
Rusty A. Gonser ◽  
Christopher N. Balakrishnan
Keyword(s):  
Gene Expression ◽  
Parental Care ◽  
Regulatory Networks ◽  
White Male ◽  
White Female ◽  
Pair Type ◽  
Specific Gene ◽  
Hub Genes ◽  
Zonotrichia Albicollis ◽  
Natural Systems

AbstractParents can have profound effects on offspring fitness. Little, however, is known about the mechanisms through which parental care variation influences offspring physiology in natural systems. White-throated sparrows Zonotrichia albicollis (WTSPs) exist in two genetic morphs, tan and white, controlled by a large polymorphic supergene. Morphs mate disassortatively, resulting in two pair types: tan male x white female (TxW) pairs, which provide biparental care and white male x tan female (WxT) pairs, which provide female-biased care. To investigate the effects of parental care variation, we performed RNA-seq on WTSP nestlings sampled from nests of both pair types. Pair type had the largest effect on nestling gene expression, with 881 genes differentially expressed (DE) and seven correlated gene co-expression modules. The DE genes and modules up-regulated in nests with female-biased parental care primarily function in metabolism and stress-related pathways resulting from the overrepresentation of stress response and proteolysis genes. These results show that parental genotypes, a proxy for parental care in this system, alter nestling physiology and highlight avenues of further research investigating the ultimate implications of alternative parental care strategies. Nestlings also exhibited morph-specific gene expression, driven by innate immunity genes and co-expression of genes located in the supergene. Remarkably, we identified the same regulatory hub genes in these blood-derived expression networks as were previously identified in WTSP brains (EPM2A, BPNT1, TAF5L). These hub genes were located within the supergene, highlighting the importance of this gene complex in structuring regulatory networks across diverse tissues.

scholarly journals Convergent evolution of venom gland transcriptomes across Metazoa

2021 ◽  
Author(s):  
Giulia Zancolli ◽  
Maarten Reijnders ◽  
Robert Waterhouse ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venom Gland ◽  
Bioactive Molecules ◽  
Specific Gene ◽  
Animal Kingdom ◽  
Venom Glands

Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

scholarly journals Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

2019 ◽  
Vol 36 (1) ◽  
pp. 197-204 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Structural Equation ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Specific Gene ◽  
Joint Inference ◽  
Perturbation Data ◽  
Gene Regulatory ◽  
The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

scholarly journals Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

2011 ◽  
Vol 28 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Geert Geeven ◽  
Ronald E. van Kesteren ◽  
August B. Smit ◽  
Mathisca C. M. de Gunst
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Context Specific

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 Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

Data mining of differentially expressed genes in epithelial ovarian carcinoma: Implications in precise medicine

2021 ◽  
Vol 21 ◽  
Author(s):  
Jie Zhao ◽  
Hongjie Gao ◽  
Yun He
Keyword(s):  
Gene Expression ◽  
Ovarian Carcinoma ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Epithelial Ovarian Carcinoma ◽  
Hub Genes ◽  
Diagnosis And Prognosis

Background: Epithelial ovarian carcinoma (EOC) is a ubiquitous gynecological malignancy with complicated pathogenesis. Genetic risk factors and pathways involved in the prognosis of this cancer are not yet understood completely. Determining genetic markers with diagnostic and prognostic values would pave the way for efficient management of cancer. Objective: This study aimed to investigate the genes and the regulatory networks involved in the occurrence and prognosis of EOC through different bioinformatics analysis tools. In addition, recent advances in using bioinformatic analysis approach based on the genes and regulatory networks, particularly differentially expressed genes (DEGs), in improving the diagnosis and prognosis of EOC are discussed. Methods: The gene expression profiles of GSE18520, GSE54388, and GSE27651 were downloaded from the Gene Expression Omnibus (GEO) database and further analyzed with different analyses in R language. Current literature on using bioinformatics based on DEGs and associated regulatory networks to improve the diagnosis and prognosis of EOC were reviewed. Results: Analyses of the gene expression levels between the malignant tissue against normal tissue unveiled 163 DEGs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the target genes using clusterProfiler package, and Cytoscape package was employed to assess the protein interaction network of these genes. The protein-protein interaction network was analyzed using the CytoHubba plug-in to identify 20 hub genes. In addition, we analyzed the prognosis of the hub genes using the Kaplan-Meier survival analysis that revealed evident differences in the prognosis of 13 genes. The malignant tissues exhibited a differential expression of 12 genes against healthy tissues, as shown by Gene Expression Profiling Interactive Analysis (GEPIA) analysis. Conclusion: Findings of this study revealed 12 genes to be significantly up-regulated, and the prognosis was significantly different, which could be employed to potentially target EOC in clinical practice.

scholarly journals Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression

2020 ◽  
Vol 96 (11) ◽  
Author(s):  
Sophie de Vries ◽  
Jan de Vries ◽  
John M Archibald ◽  
Claudio H Slamovits
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reference Points ◽  
Global Perspective ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Specific Array

ABSTRACT Oomycetes include many devastating plant pathogens. Across oomycete diversity, plant-infecting lineages are interspersed by non-pathogenic ones. Unfortunately, our understanding of the evolution of lifestyle switches is hampered by a scarcity of data on the molecular biology of saprotrophic oomycetes, ecologically important primary colonizers of dead tissue that can serve as informative reference points for understanding the evolution of pathogens. Here, we established Salisapilia sapeloensis as a tractable system for the study of saprotrophic oomycetes. We generated multiple transcriptomes from S. sapeloensis and compared them with (i) 22 oomycete genomes and (ii) the transcriptomes of eight pathogenic oomycetes grown under 13 conditions. We obtained a global perspective on gene expression signatures of oomycete lifestyles. Our data reveal that oomycete saprotrophs and pathogens use similar molecular mechanisms for colonization but exhibit distinct expression patterns. We identify a S. sapeloensis-specific array and expression of carbohydrate-active enzymes and putative regulatory differences, highlighted by distinct expression levels of transcription factors. Salisapilia sapeloensis expresses only a small repertoire of candidates for virulence-associated genes. Our analyses suggest lifestyle-specific gene regulatory signatures and that, in addition to variation in gene content, shifts in gene regulatory networks underpin the evolution of oomycete lifestyles.

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 the regulatory networks and hub genes controlling alfalfa floral pigmentation variation using RNA-sequencing analysis

2020 ◽  
Author(s):  
Hui-Rong Duan ◽  
Li-Rong Wang ◽  
Guang-Xin Cui ◽  
Xue-Hui Zhou ◽  
Xiao-Rong Duan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Flower Color ◽  
Molecular Networks ◽  
Sequencing Analysis ◽  
Hub Genes ◽  
Color Formation ◽  
Network Analyses

Abstract Background: To understand the gene expression networks controlling flower color formation in alfalfa, flowers anthocyanins were identified using two materials with contrasting flower colors, namely Defu and Zhongtian No. 3, and transcriptome analyses of PacBio full-length sequencing combined with RNA sequencing were performed, across four flower developmental stages. Results: Malvidin and petunidin glycoside derivatives were the major anthocyanins in the flowers of Defu, which were lacking in the flowers of Zhongtian No. 3. The two transcriptomic datasets provided a comprehensive and systems-level view on the dynamic gene expression networks underpinning alfalfa flower color formation. By weighted gene coexpression network analyses, we identified candidate genes and hub genes from the modules closely related to floral developmental stages. PAL , 4CL , CHS , CHR , F3’H , DFR , and UFGT were enriched in the important modules. Additionally, PAL6 , PAL9 , 4CL18 , CHS2 , 4 and 8 were identified as hub genes. Thus, a hypothesis explaining the lack of purple color in the flower of Zhongtian No. 3 was proposed. Conclusions: These analyses identified a large number of potential key regulators controlling flower color pigmentation, thereby providing new insights into the molecular networks underlying alfalfa flower development.

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