Pacheco Maria Pires, Gerard Déborah, Mangan Riley J, Chapman Alec R, Hecker Dennis, Kellis Manolis, Schulz Marcel H, Sinkkonen Lasse, Sauter Thomas
Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
bioRxiv. 2024 Dec 17:2024.07.24.604914. doi: 10.1101/2024.07.24.604914.
Constraint-based network modeling is a powerful genomic-scale approach for analyzing cellular metabolism, capturing metabolic variations across tissues and cell types, and defining the metabolic identity essential for identifying disease-associated transcriptional states.
Using RNA-seq and epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we reconstructed metabolic networks for 1,555 samples spanning 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell-type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression.
This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.
基于约束的网络建模是一种强大的基因组规模方法，用于分析细胞代谢、捕捉不同组织和细胞类型间的代谢差异，以及定义识别疾病相关转录状态所必需的代谢特征。
利用国际人类表观基因组联盟（IHEC）的EpiATLAS资源中的RNA测序和表观基因组数据，我们重建了涵盖58种组织和细胞类型的1555个样本的代谢网络。对这些网络的分析揭示了人类细胞类型间代谢功能的分布，并提供了一份人类代谢活动概要。这种综合方法使我们能够在不同组织和细胞类型中定义：i）实现基本代谢过程（核心代谢）的反应，以及ii）塑造细胞或组织代谢特征的细胞类型特异性功能（独特代谢）。与EpiATLAS衍生的细胞类型特异性基因水平染色质状态及增强子-基因相互作用相结合，确定了控制核心代谢和独特代谢的增强子、转录因子及关键节点。在途径不活跃的细胞类型中，途径的转运和起始反应富集了高表达、活跃染色质状态及多梳介导的抑制，这表明关键节点是抑制的靶点。
这种综合分析为识别控制人类细胞代谢特征的调控点奠定了基础。
