Transcription factor expression is the main determinant of variability in gene co‐activity

Duin, Lucas van; Krautz, Robert; Rennie, Sarah; Andersson, Robin

doi:10.15252/msb.202211392

Cited by 8 publications

(3 citation statements)

References 63 publications

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“…This observation is also consistent with the notion that cmQTLs frequently disrupt the binding sites of cell-type specific TFs, which then could affect histone modification deposition and cooperation between CREs within a CM in a cell type-specific manner. This finding complements recent observations showing that genetic variants impact CRE interactions relevant for establishing gene expression through direct perturbation of TF binding to enhancers [ 85 ] and that TFs are the main drivers of gene expression cooperativity [ 86 ].…”

Section: Discussionsupporting

confidence: 87%

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

Pushkarev,

van Mierlo,

Kribelbauer

et al. 2024

Genome Biol

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Background Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes. Results We comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia. Conclusions Our study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.

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Section: Discussionsupporting

confidence: 87%

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

Pushkarev,

van Mierlo,

Kribelbauer

et al. 2024

Genome Biol

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“…In our study, we observed large differences of DNA-binding characteristic of TF Irp between the C48 strain and the DC3000 strain. The functional diversity of TFs may arise from the large difference in the contents of target genes and TFs, which are regarded as the main determinant of transcriptional regulatory(van Duin, Krautz, Rennie, & Andersson, 2023), although Irp display high homology in these pathovars.…”

Section: Discussionmentioning

confidence: 99%

Architecture of genome-wide transcriptional regulatory network reveals dynamic functions and evolutionary trajectories inPseudomonas syringae

Sun,

Li,

Huang

et al. 2024

Preprint

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The model Gram-negative plant pathogen Pseudomonas syringae utilises hundreds of transcription factors (TFs) to regulate its functional processes, including virulence and metabolic pathways that control its ability to infect host plants. Although the molecular mechanisms of regulators have been studied for decades, a comprehensive understanding of genome-wide TFs in P. syringae remains limited. Here, we investigated the binding characteristics of 170 of 301 annotated TFs through ChIP-seq. Fifty-four TFs, 62 TFs and 147 TFs were identified in top-level, middle-level and bottom-level, reflecting multiple higher-order network structures and direction of information-flow. More than forty thousand TF-pairs were classified into 13 three-node submodules which revealed the regulatory diversity of TFs in P. syringae regulatory network. We found that bottom-level TFs performed high co-associated scores to their target genes. Functional categories of TFs at three levels encompassed various regulatory pathways. Three and 25 master TFs were identified to involve in virulence and metabolic regulation, respectively. Evolutionary analysis and topological modularity network revealed functional variability and various conservation of TFs in P. syringae. Overall, our findings demonstrated the global transcriptional regulatory network of genome-wide TFs in P. syringae. This knowledge can advance the development of effective treatment and prevention strategies for related infectious diseases.

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“…The identification of regulatory mechanisms that act on individual genes can provide context-specific understanding of transcriptional regulation and shed light on the role of dysregulation in disease [26][27][28] . Here, we sought to identify genes that are putative targets of significantly associated SCZ genes in the cell type models.…”

Section: Post-twas Analysis: Identifying Regulatory Network For Scz A...mentioning

confidence: 99%

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Abe,

Lin,

Zhou

et al. 2023

Preprint

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Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in dopaminergic neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.

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Transcription factor expression is the main determinant of variability in gene co‐activity

Cited by 8 publications

References 63 publications

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

Architecture of genome-wide transcriptional regulatory network reveals dynamic functions and evolutionary trajectories inPseudomonas syringae

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

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