Sharing and specificity of co-expression networks across 35 human tissues

Sharing and specificity of co-expression networks across 35 human tissues
Emma Pierson, GTEx Consortium, Daphne Koller, Alexis Battle, Sara Mostafavi

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue-specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue-specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool which allows exploration of gene function and regulation in a tissue-specific manner.

Genome-wide characterization of RNA editing in chicken: lack of evidence for non-A-to-I events

Genome-wide characterization of RNA editing in chicken: lack of evidence for non-A-to-I events

Laure Frésard, Sophie Leroux, Pierre-François Roux, C Klopp, Stéphane Fabre, Diane Esquerré, Patrice Dehais, Anis Djari, David Gourichon, Sandrine Lagarrigue, Frédérique Pitel

RNA editing corresponds to a post-transcriptional nucleotide change in the RNA sequence, creating an alternative nucleotide, not present in the DNA sequence. This leads to a diversification of transcription products with potential functional consequences. Two nucleotide substitutions are mainly described in animals, from adenosine to inosine (A-to-I) and from cytidine to uridine (C-to-U). This phenomenon is more and more described in mammals, notably since the availability of next generation sequencing technologies allowing a whole genome screening of RNA-DNA differences. The number of studies recording RNA editing in other vertebrates like chicken are still limited. We chose to use high throughput sequencing technologies to search for RNA editing in chicken, to understand to what extent this phenomenon is conserved in vertebrates. We performed RNA and DNA sequencing from 8 embryos. Being aware of common pitfalls inherent to sequence analyses leading to false positive discovery, we stringently filtered our datasets and found less than 40 reliable candidates. Conservation of particular sites of RNA editing was attested by the presence of 3 edited sites previously detected in mammals. We then characterized editing levels for selected candidates in several tissues and at different time points, from 4.5 days of embryonic development to adults, and observed a clear tissue-specificity and a gradual editing level increase with time. By characterizing the RNA editing landscape in chicken, our results highlight the extent of evolutionary conservation of this phenomenon within vertebrates, and provide support of an absence of non A-to-I events from the chicken transcriptome.

Functional analysis and co-evolutionary model of chromatin and DNA methylation networks in embryonic stem cells

Functional analysis and co-evolutionary model of chromatin and DNA methylation networks in embryonic stem cells
Enrique Carrillo de Santa Pau, Juliane Perner, David Juan, Simone Marsili, David Ochoa, Ho-Ryun Chung, Daniel Rico, Martin Vingron, Alfonso Valencia
We have analyzed publicly available epigenomic data of mouse embryonic stem cells (ESCs) combining diverse next-generation sequencing (NGS) studies (139 experiments from 30 datasets with a total of 77 epigenomic features) into a homogeneous dataset comprising various cytosine modifications (5mC, 5hmC and 5fC), histone marks and Chromatin related Proteins (CrPs). We applied a set of newly developed statistical analysis methods with the goal of understanding the associations between chromatin states, detecting co-occurrence of DNA-protein binding and epigenetic modification events, as well as detecting coevolution of core CrPs. The resulting networks reveal the complex relations between cytosine modifications and protein complexes and their dependence on defined ESC chromatin contexts. A detailed analysis allows us to detect proteins associated to particular chromatin states whose functions are related to the different cytosine modifications, i.e. RYBP with 5fC and 5hmC, NIPBL with 5hmC and OGT with 5hmC. Moreover, in a co-evolutionary analysis suggesting a central role of the Cohesin complex in the evolution of the epigenomic network, as well as strong co-evolutionary links between proteins that co-locate in the ESC epigenome with DNA methylation (MBD2 and CBX3) and hydroxymethylation (TET1 and KDM2A). In summary, the new application of computational methodologies reveals the complex network of relations between cytosine modifications and epigenomic players that is essential in shaping the molecular state of ESCs.

Cross-population Meta-analysis of eQTLs: Fine Mapping and Functional Study

Cross-population Meta-analysis of eQTLs: Fine Mapping and Functional Study

Xiaoquan Wen, Francesca Luca, Roger Pique-Regi

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at the molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistently presented across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL. ii) many genes harbor multiple independent eQTLs in their cis regions iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 × 10-22).

Conservation of expression regulation throughout the animal kingdom

Conservation of expression regulation throughout the animal kingdom

Michael Kuhn, Andreas Beyer

Gene expression programs have been found to be highly conserved between closely related species, especially when comparing the same tissue types between species. Such analysis is, however, much more challenging over larger evolutionary distances when complementary tissues cannot readily be defined. Here, we present the first cross-species mapping of tissue-specific and developmental gene expression patterns across a wide range of animals, including many non-model species. Importantly, our approach does not require the definition of homologous tissues. In our survey of 32 datasets across 23 species, we detected conserved expression programs on all taxonomic levels, both within animals and between the animals and their closest unicellular relatives, the choanoflagellates. We found that the rate of change in tissue expression patterns is a property of gene families. Subsequently, we used the conservation of expression programs as a means to identify neofunctionalization of gene duplication products. We found 1206 duplication events where one of the two genes kept the expression program of the original gene, whereas the other copy adopted a novel expression program. We corroborated such potential neofunctionalizations using independent network information: the duplication product with the more conserved expression pattern shared more interaction partners with the non-duplicated reference gene than the more divergent duplication product. Our findings open new avenues of study for the comparison and transfer of knowledge between different species.

Nuclear stability and transcriptional directionality separate functionally distinct RNA species

Nuclear stability and transcriptional directionality separate functionally distinct RNA species

Robin Andersson, Peter Refsing Andersen, Eivind Valen, Leighton Core, Jette Bornholdt, Mette Boyd, Torben Heick Jensen, Albin Sandelin

Mammalian genomes are pervasively transcribed, yielding a complex transcriptome with high variability in composition and cellular abundance. While recent efforts have identified thousands of new long non-coding (lnc) RNAs and demonstrated a complex transcriptional repertoire produced by protein-coding (pc) genes, limited progress has been made in distinguishing functional RNA from spurious transcription events. This is partly due to present RNA classification, which is typically based on technical rather than biochemical criteria. Here we devise a strategy to systematically categorize human RNAs by their sensitivity to the ribonucleolytic RNA exosome complex and by the nature of their transcription initiation. These measures are surprisingly effective at correctly classifying annotated transcripts, including lncRNAs of known function. The approach also identifies uncharacterized stable lncRNAs, hidden among a vast majority of unstable transcripts. The predictive power of the approach promises to streamline the functional analysis of known and novel RNAs.

The Genetic Architecture of Gene Expression Levels in Wild Baboons

The Genetic Architecture of Gene Expression Levels in Wild Baboons

Jenny Tung, Xiang Zhou, Susan C Alberts, Matthew Stephens, Yoav Gilad

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.