Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale).     

Study of Integrated Heterogeneous Data Reveals Prognostic Power of Gene Expression for Breast Cancer Survival

BackgroundStudies show that thousands of genes are associated with prognosis of breast cancer. Towards utilizing available genetic data, efforts have been made to predict outcomes using gene expression data, and a number of commercial products have been developed. These products have the following shortcomings: 1) They use the Cox model for prediction. However, the RSF model has been shown to significantly outperform the Cox model. 2) Testing was not done to see if a complete set of clinical predictors could predict as well as the gene expression signatures.Methodology/FindingsWe address these shortcomings. The METABRIC data set concerns 1981 breast cancer tumors. Features include 21 clinical features, expression levels for 16,384 genes, and survival. We compare the survival prediction performance of the Cox model and the RSF model using the clinical data and the gene expression data to their performance using only the clinical data. We obtain significantly better results when we used both clinical data and gene expression data for 5 year, 10 year, and 15 year survival prediction. When we replace the gene expression data by PAM50 subtype, our results are significant only for 5 year and 15 year prediction. We obtain significantly better results using the RSF model over the Cox model. Finally, our results indicate that gene expression data alone may predict long-term survival.Conclusions/SignificanceOur results indicate that we can obtain improved survival prediction using clinical data and gene expression data compared to prediction using only clinical data. We further conclude that we can obtain improved survival prediction using the RSF model instead of the Cox model. These results are significant because by incorporating more gene expression data with clinical features and using the RSF model, we could develop decision support systems that better utilize heterogeneous information to improve outcome prediction and decision making.     

Faster-X Evolution of Gene Expression in Drosophila

DNA sequences on X chromosomes often have a faster rate of evolution when compared to similar loci on the autosomes, and well articulated models provide reasons why the X-linked mode of inheritance may be responsible for the faster evolution of X-linked genes. We analyzed microarray and RNA–seq data collected from females and males of six Drosophila species and found that the expression levels of X-linked genes also diverge faster than autosomal gene expression, similar to the “faster-X” effect often observed in DNA sequence evolution. Faster-X evolution of gene expression was recently described in mammals, but it was limited to the evolutionary lineages shortly following the creation of the therian X chromosome. In contrast, we detect a faster-X effect along both deep lineages and those on the tips of the Drosophila phylogeny. In Drosophila males, the dosage compensation complex (DCC) binds the X chromosome, creating a unique chromatin environment that promotes the hyper-expression of X-linked genes. We find that DCC binding, chromatin environment, and breadth of expression are all predictive of the rate of gene expression evolution. In addition, estimates of the intraspecific genetic polymorphism underlying gene expression variation suggest that X-linked expression levels are not under relaxed selective constraints. We therefore hypothesize that the faster-X evolution of gene expression is the result of the adaptive fixation of beneficial mutations at X-linked loci that change expression level in cis. This adaptive faster-X evolution of gene expression is limited to genes that are narrowly expressed in a single tissue, suggesting that relaxed pleiotropic constraints permit a faster response to selection. Finally, we present a conceptional framework to explain faster-X expression evolution, and we use this framework to examine differences in the faster-X effect between Drosophila and mammals.     

Analysis of cell-type-specific chromatin modifications and gene expression in Drosophila neurons that direct reproductive behavior

Examining the role of chromatin modifications and gene expression in neurons is critical for understanding how the potential for behaviors are established and maintained. We investigate this question by examining Drosophila melanogaster fru P1 neurons that underlie reproductive behaviors in both sexes. We developed a method to purify cell-type-specific chromatin (Chromatag), using a tagged histone H2B variant that is expressed using the versatile Gal4/UAS gene expression system. Here, we use Chromatag to evaluate five chromatin modifications, at three life stages in both sexes. We find substantial changes in chromatin modification profiles across development and fewer differences between males and females. Additionally, we find chromatin modifications that persist in different sets of genes from pupal to adult stages, which may point to genes important for cell fate determination in fru P1 neurons. We generated cell-type-specific RNA-seq data sets, using translating ribosome affinity purification (TRAP). We identify actively translated genes in fru P1 neurons, revealing novel stage- and sex-differences in gene expression. We also find chromatin modification enrichment patterns that are associated with gene expression. Next, we use the chromatin modification data to identify cell-type-specific super-enhancer-containing genes. We show that genes with super-enhancers in fru P1 neurons differ across development and between the sexes. We validated that a set of genes are expressed in fru P1 neurons, which were chosen based on having a super-enhancer and TRAP-enriched expression in fru P1 neurons.     

Fundamental features of chromatin structure

Which mechanisms regulate nuclear plasticity? Part of the answer to that question lies in understanding how genes are expressed and regulated in the context of chromatin structure. It is now clear that the genes are regulated in discrete and controlled stages, from packaging into chromatin to their localization within the nucleus. Whereas the genetic information provides the framework for the manufacture of all proteins necessary to create a living cell, chromatin structure controls how, where, and when the genetic information should be used. In this minireview, I summarize the main characteristics of chromatin structure and highlight some of the modifications usually associated with the regulation of gene expression.