Modeling and Experimental Methods to Probe the Link between Global Transcription and Spatial Organization of Chromosomes

PLoS One

Albert Libchaber¹  Shovamayee Maharana¹  G. V. Shivashankar¹  Soumya Gupta²  K. Venkatesan Iyer²  Tsvi Tlusty² 

[1] Mechanobiology Institute and Department of Biological Sciences, National University of Singapore, Singapore, Singapore;National Centre for Biological Sciences, Tata Institute for Fundamental Research, Bangalore, Karnataka, India

关键词: Fibroblasts;    Gene expression;    T cells;    Chromosome pairs;    Genetic networks;    Chromosomes;    Microarrays;    Nuclear matrix;   

DOI  :  10.1371/journal.pone.0046628

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Genomes are spatially assembled into chromosome territories (CT) within the nucleus of living cells. Recent evidences have suggested associations between three-dimensional organization of CTs and the active gene clusters within neighboring CTs. These gene clusters are part of signaling networks sharing similar transcription factor or other downstream transcription machineries. Hence, presence of such gene clusters of active signaling networks in a cell type may regulate the spatial organization of chromosomes in the nucleus. However, given the probabilistic nature of chromosome positions and complex transcription factor networks (TFNs), quantitative methods to establish their correlation is lacking. In this paper, we use chromosome positions and gene expression profiles in interphase fibroblasts and describe methods to capture the correspondence between their spatial position and expression. In addition, numerical simulations designed to incorporate the interacting TFNs, reveal that the chromosome positions are also optimized for the activity of these networks. These methods were validated for specific chromosome pairs mapped in two distinct transcriptional states of T-Cells (naïve and activated). Taken together, our methods highlight the functional coupling between topology of chromosomes and their respective gene expression patterns.

【 授权许可】

CC BY