MicroRNAs organize intrinsic variation into stem cell states

Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in part from cooption of fluctuations in the gene regulatory network. Here we find ESCs exhibit intrinsic heterogeneity in the absence of external gradients by forming interconverting cell states. States vary in developmental gene expression programs and display distinct activity of microRNAs (miRNAs). Notably, miRNAs act on neighborhoods of pluripotency genes to increase variation of target genes and cell states. Loss of miRNAs that vary across states reduces target variation and delays state transitions, suggesting variable miRNAs organize and propagate variation to promote state transitions. Together these findings provide insight into how a gene regulatory network can coopt variation intrinsic to cell systems to form robust gene expression states. Interactions between intrinsic heterogeneity and environmental signals may help achieve developmental outcomes.

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Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽

10.7554/elife.51254 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 16

Author(s):

Christopher A Jackson ◽

Dayanne M Castro ◽

Giuseppe-Antonio Saldi ◽

Richard Bonneau ◽

David Gresham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Target Genes ◽

Single Cell Rna Sequencing ◽

Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

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Transcriptional control by Sall4 in blastocysts facilitates lineage commitment of inner cell mass cells

10.1101/194852 ◽

2017 ◽

Cited By ~ 4

Author(s):

Anzy Miller ◽

Sarah Gharbi ◽

Charles Etienne-Dumeau ◽

Ryuichi Nishinakamura ◽

Brian Hendrich

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Transcriptional Activation ◽

Regulatory Network ◽

Transcriptional Control ◽

Inner Cell Mass ◽

Cell Mass ◽

Embryonic Stem ◽

Lineage Commitment ◽

Gene Regulatory

AbstractThe enhancer-binding zinc finger transcription factor Sall4 is essential for early mammalian postimplantation development and plays important roles in lineage commitment of embryonic stem cells. Enhancer binding by Sall4 results in transcriptional activation of some genes, but repression of others. Exactly how cells in preimplantation stage embryos use this transcriptional modulatory activity of Sall4 during early developmental transitions has not been determined. Using single cell gene expression analyses we show that Sall4 is required to maintain the gene regulatory network in inner cell mass (ICM) cells prior to lineage commitment. Although Sall4 is not required for ICM cells to adopt a correct epiblast or primitive endoderm gene expression profile, in the absence of Sall4 early ICM cells commit to either lineage at reduced frequency. We propose a model whereby Sall4 activity sets the stage for efficient progression from the uncommitted ICM progenitor state by modulating the gene regulatory network in early ICM cells.

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Study on the Relationship between the miRNA-centered ceRNA Regulatory Network and Fatigue

Journal of Molecular Neuroscience ◽

10.1007/s12031-021-01845-3 ◽

2021 ◽

Author(s):

Xingzhe Yang ◽

Feng Li ◽

Jie Ma ◽

Yan Liu ◽

Xuejiao Wang ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Hot Spot ◽

Genetic Research ◽

Noncoding Rnas ◽

Messenger Rnas ◽

Effective Prevention ◽

Gene Regulatory ◽

The Relationship

AbstractIn recent years, the incidence of fatigue has been increasing, and the effective prevention and treatment of fatigue has become an urgent problem. As a result, the genetic research of fatigue has become a hot spot. Transcriptome-level regulation is the key link in the gene regulatory network. The transcriptome includes messenger RNAs (mRNAs) and noncoding RNAs (ncRNAs). MRNAs are common research targets in gene expression profiling. Noncoding RNAs, including miRNAs, lncRNAs, circRNAs and so on, have been developed rapidly. Studies have shown that miRNAs are closely related to the occurrence and development of fatigue. MiRNAs can regulate the immune inflammatory reaction in the central nervous system (CNS), regulate the transmission of nerve impulses and gene expression, regulate brain development and brain function, and participate in the occurrence and development of fatigue by regulating mitochondrial function and energy metabolism. LncRNAs can regulate dopaminergic neurons to participate in the occurrence and development of fatigue. This has certain value in the diagnosis of chronic fatigue syndrome (CFS). CircRNAs can participate in the occurrence and development of fatigue by regulating the NF-κB pathway, TNF-α and IL-1β. The ceRNA hypothesis posits that in addition to the function of miRNAs in unidirectional regulation, mRNAs, lncRNAs and circRNAs can regulate gene expression by competitive binding with miRNAs, forming a ceRNA regulatory network with miRNAs. Therefore, we suggest that the miRNA-centered ceRNA regulatory network is closely related to fatigue. At present, there are few studies on fatigue-related ncRNA genes, and most of these limited studies are on miRNAs in ncRNAs. However, there are a few studies on the relationship between lncRNAs, cirRNAs and fatigue. Less research is available on the pathogenesis of fatigue based on the ceRNA regulatory network. Therefore, exploring the complex mechanism of fatigue based on the ceRNA regulatory network is of great significance. In this review, we summarize the relationship between miRNAs, lncRNAs and circRNAs in ncRNAs and fatigue, and focus on exploring the regulatory role of the miRNA-centered ceRNA regulatory network in the occurrence and development of fatigue, in order to gain a comprehensive, in-depth and new understanding of the essence of the fatigue gene regulatory network.

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Transcriptional regulation of gene expression inCorynebacterium glutamicum: the role of global, master and local regulators in the modular and hierarchical gene regulatory network

FEMS Microbiology Reviews ◽

10.1111/j.1574-6976.2010.00228.x ◽

2010 ◽

Vol 34 (5) ◽

pp. 685-737 ◽

Cited By ~ 70

Author(s):

Jasmin Schröder ◽

Andreas Tauch

Keyword(s):

Gene Expression ◽

Transcriptional Regulation ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulation Of Gene Expression ◽

Gene Regulatory

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

10.1101/2021.12.08.471841 ◽

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.

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Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽

10.7554/elife.18215 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 31

Author(s):

Erik Clark ◽

Michael Akam

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Frequency Doubling ◽

Drosophila Embryo ◽

Anteroposterior Patterning ◽

Regulatory Interactions ◽

Pair Rule Gene ◽

Gene Regulatory ◽

Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

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PAGeneRN

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch055 ◽

2020 ◽

pp. 1052-1075 ◽

Cited By ~ 1

Author(s):

Dina Elsayad ◽

A. Ali ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Network Analysis ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Expression Data Analysis ◽

Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019500355 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950035

Author(s):

Huiqing Wang ◽

Yuanyuan Lian ◽

Chun Li ◽

Yue Ma ◽

Zhiliang Yan ◽

...

Keyword(s):

Gene Expression ◽

Steady State ◽

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Knockout ◽

Cell Heterogeneity ◽

State Information ◽

Gene Regulatory ◽

Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

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BCL2 and hsa-miR-181a-5p are potential biomarkers associated with papillary thyroid cancer based on bioinformatics analysis

World Journal of Surgical Oncology ◽

10.1186/s12957-019-1755-9 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

Cong Zhang ◽

Chunrui Bo ◽

Lunhua Guo ◽

Pingyang Yu ◽

Susheng Miao ◽

...

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Gene Regulatory Network ◽

Papillary Thyroid Cancer ◽

Regulatory Network ◽

Mirna Gene ◽

Cancer Type ◽

Papillary Thyroid ◽

Ppi Network ◽

Gene Regulatory

Abstract Background The morbidity of thyroid carcinoma has been rising worldwide and increasing faster than any other cancer type. The most common subtype with the best prognosis is papillary thyroid cancer (PTC); however, the exact molecular pathogenesis of PTC is still not completely understood. Methods In the current study, 3 gene expression datasets (GSE3678, GSE3467, and GSE33630) and 2 miRNA expression datasets (GSE113629 and GSE73182) of PTC were selected from the Gene Expression Omnibus (GEO) database and were further used to identify differentially expressed genes (DEGs) and deregulated miRNAs between normal thyroid tissue samples and PTC samples. Then, Gene Ontology (GO) and pathway enrichment analyses were conducted, and a protein-protein interaction (PPI) network was constructed to explore the potential mechanism of PTC carcinogenesis. The hub gene detection was performed using the CentiScaPe v2.0 plugin, and significant modules were discovered using the MCODE plugin for Cytoscape. In addition, a miRNA-gene regulatory network in PTC was constructed using common deregulated miRNAs and DEGs. Results A total of 263 common DEGs and 12 common deregulated miRNAs were identified. Then, 6 significant KEGG pathways (P < 0.05) and 82 significant GO terms were found to be enriched, indicating that PTC was closely related to amino acid metabolism, development, immune system, and endocrine system. In addition, by constructing a PPI network and miRNA-gene regulatory network, we found that hsa-miR-181a-5p regulated the most DEGs, while BCL2 was targeted by the most miRNAs. Conclusions The results of this study suggested that hsa-miR-181a-5p and BCL2 and their regulatory networks may play important roles in the pathogenesis of PTC.

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