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GRaNIE 和 GRaNPA：增强子介导的基因调控网络的推断和评估。

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks.

机构信息

European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany.

Faculty of Biosciences, Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Heidelberg, Germany.

出版信息

Mol Syst Biol. 2023 Jun 12;19(6):e11627. doi: 10.15252/msb.202311627. Epub 2023 Apr 19.

DOI:10.15252/msb.202311627
PMID:37073532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10258561/
Abstract

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

摘要

增强子在基因调控中起着至关重要的作用，对于介导与复杂性状相关的非编码遗传变异的影响至关重要。增强子活性是一个受转录因子（TFs）、表观遗传机制和遗传变异调控的细胞类型特异性过程。尽管 TFs 和增强子之间存在很强的机制联系，但我们目前缺乏在细胞类型特异性基因调控网络（GRN）中联合分析它们的框架。同样重要的是，由于不存在完整的真实情况，我们缺乏评估推断的 GRN 生物学意义的无偏方法。为了解决这些差距，我们提出了 GRaNIE（包括增强子的基因调控网络推断）和 GRaNPA（基因调控网络性能分析）。GRaNIE（https://git.embl.de/grp-zaugg/GRaNIE）基于染色质可及性和 RNA-seq 在样本（例如个体）中的共变构建增强子介导的 GRN，而 GRaNPA（https://git.embl.de/grp-zaugg/GRaNPA）评估 GRN 预测细胞类型特异性差异表达的性能。我们通过研究巨噬细胞对感染、癌症和常见遗传特征（包括自身免疫性疾病）的反应的基因调控机制来证明它们的强大功能。最后，我们的方法确定 TF PURA 是促炎巨噬细胞极化的潜在调节因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/05ec4052ce7a/MSB-19-e11627-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/9359cec71a56/MSB-19-e11627-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/2f9e1716180c/MSB-19-e11627-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/bc07a1c3f1e6/MSB-19-e11627-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/08963466dc01/MSB-19-e11627-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/05ec4052ce7a/MSB-19-e11627-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/9359cec71a56/MSB-19-e11627-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/2f9e1716180c/MSB-19-e11627-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/bc07a1c3f1e6/MSB-19-e11627-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/08963466dc01/MSB-19-e11627-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755f/10258561/05ec4052ce7a/MSB-19-e11627-g006.jpg

相似文献

[1]
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引用本文的文献

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Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states.

Nat Commun. 2025-8-2

[2]
Leveraging transcription factor physical proximity for enhancing gene regulation inference.

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[3]
Single-cell ultra-high-throughput multiplexed chromatin and RNA profiling reveals gene regulatory dynamics.

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[4]
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[5]
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[6]
Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.

Mol Syst Biol. 2025-3

[7]
Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation.

Life Med. 2024-4-26

[8]
Chromatin enables precise and scalable gene regulation with factors of limited specificity.

Proc Natl Acad Sci U S A. 2025-1-7

[9]
Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.

Nat Commun. 2024-11-9

[10]
Single-cell multi-modal integrative analyses highlight functional dynamic gene regulatory networks directing human cardiac development.

Cell Genom. 2024-11-13

本文引用的文献

[1]
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks.

Nat Methods. 2023-9

[2]
Dissecting cell identity via network inference and in silico gene perturbation.

Nature. 2023-2

[3]
Inferring and perturbing cell fate regulomes in human brain organoids.

Nature. 2023-9

[4]
Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks.

Nat Genet. 2022-8

[5]
CDK7/12/13 inhibition targets an oscillating leukemia stem cell network and synergizes with venetoclax in acute myeloid leukemia.

EMBO Mol Med. 2022-4-7

[6]
Decoding gene regulation in the fly brain.

Nature. 2022-1

[7]
ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments.

Nucleic Acids Res. 2022-1-7

[8]
Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors.

Cell Rep. 2021-11-2

[9]
Defining the Role of Nuclear Factor (NF)-κB p105 Subunit in Human Macrophage by Transcriptomic Analysis of Knockout THP1 Cells.

Front Immunol. 2021

[10]
Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Nat Genet. 2021-9

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