Hierarchical Markov Random Field model captures spatial dependency in gene expression, demonstrating regulation via the 3D genome

Abstract

HiC technology has revealed many details about the eukaryotic genome's complex 3D architecture. It has been shown that the genome is separated into organizational structures which are associated with gene expression. However, to the best of our knowledge, no studies have quantitatively measured the level of gene expression in the context of the 3D genome.Here we present a novel model that integrates data from RNA-seq and HiC experiments, and determines how much of the variation in gene expression can be accounted for by the genes' spatial locations. We used Poisson hierarchical Markov Random Field (PhiMRF), to estimate the level of spatial dependency among protein-coding genes in two different human cell lines. The inference of PhiMRF follows a Bayesian framework, and we introduce the Spatial Interaction Estimate (SIE) to measure the strength of spatial dependency in gene expression.We find that the quantitative expression of genes in some chromosomes show meaningful positive intra-chromosomal spatial dependency. Interestingly, the spatial dependency is much stronger than the dependency based on linear gene neighborhoods, suggesting that 3D chromosome structures such as chromatin loops and Topologically Associating Domains (TADs) are strongly associated with gene expression levels. In some chromosomes the spatial dependency in gene expression is only detectable when the spatial neighborhoods are confined within TADs, suggesting TAD boundaries serve as insulating barriers for spatial gene regulation in the genome. We also report high inter-chromosomal spatial correlations in the majority of chromosome pairs, as well as the whole genome. Some functional groups of genes show strong spatial dependency in gene expression as well, providing new insights into the regulation mechanisms of these molecular functions. This study both confirms and quantifies widespread spatial correlation in gene expression. We propose that, with the growing influx of HiC data complementing gene expression data, the use of spatial dependence should be an integral part of the toolkit in the computational analysis of the relationship between chromosome structure and gene expression.Keywords HiC · nuclear organization · gene expression · Markov random field · RNA-seq 1 1 Introduction 2The 3D genome organization plays an important role in gene expression through various mechanisms [1, 2, 3, 4]. Of 3 special interest is how genes in close spatial proximity coordinate expression. Several molecular models involving 4 different organizational hierarchies have been proposed to explain this phenomenon [4]. One such hypothesis is that of 5 transcription factories, where RNA polymerase II is significantly enriched to allow efficient transcription of multiple 6 genes at the same foci [5, 6, 7]. 7 2 Another molecular model for spatial gene clusters hypothesizes that the spatial cluster are brought together to allow 8 their promoters to interact with enhancers [8, 9]. The TNFα-induced multigene complex regulated by NF-κB is 9 disrupted once the chromatin l...

Computational Inference of DNA Folding Principles: From Data Management to Machine Learning

“…Given the high complexity of Hi-C data and the difficult definition of gene coexpression networks [27], the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene coexpression and which physical interaction patterns are most predictive of coexpression relationships [28,29].…”