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The most prevalent post-transcriptional mRNA modification, N6-methyladenosine (m6A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here we report 129 transcriptome-wide m6A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with interindividual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs), which are modestly enriched in, but mostly orthogonal to, expression QTLs. We integrate m6A QTLs with disease genetics, identifying 184 GWAS-colocalized m6A QTL, including brain m6A QTLs underlying neuroticism, depression, schizophrenia and anxiety; lung m6A QTLs underlying expiratory flow and asthma; and muscle/heart m6A QTLs underlying coronary artery disease. Last, we predict novel m6A regulators that show preferential binding in m6A QTLs, protein interactions with known m6A regulators and expression correlation with the m6A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their interindividual variation and their roles in human disease. Analysis of 129 N6-methyladenosine (m6A) profiles across 4 tissues (brain, lung, muscle and heart) identifies 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs). Of these, 184 m6A QTLs colocalize with GWAS signals.

The most prevalent post-transcriptional mRNA modification, N(6)-Methyladenosine (m(6)A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here, we report 129 transcriptome-wide m(6)A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with inter-individual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m(6)A-QTLs, which are modestly enriched in but mostly orthogonal to eQTLs. We integrate m(6)A-QTLs with disease genetics, identifying 184 GWAS-colocalized m(6)A-QTL loci, including brain m(6)A-QTLs underlying neuroticism, depression, schizophrenia, and anxiety; lung m(6)A-QTLs underlying expiratory flow and asthma; and heart/muscle m(6)A-QTLs underlying coronary artery disease. Lastly, we predict novel m(6)A regulators that show preferential binding in m(6)A-QTLs, protein interactions with known m(6)A regulators, and expression correlation with m(6)A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their inter-individual variation, and their roles in human disease.

Noncoding RNA helps protein binding Besides reading the coding regions of genes, RNA polymerase generates RNA at promoter-proximal and -distal DNA elements, but the function of these molecules is largely unknown. Sigova et al. show that these RNAs facilitate interactions between gene regulators and the regulatory elements they occupy. Nascent RNA associates with the transcription factor YY1 and increases its ability to bind DNA. Thus, transcription at active regulatory elements may provide a positive feedback loop that reinforces regulatory elements contributing to the stability of gene expression programs. Science , this issue p. 978

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

SummaryThis study brings together the expanding fields of RNA modifications and circular (circ) RNAs. We find that cells express thousands of m6A methylated circRNAs, with cell-type specificity observed between human embryonic stem cells and HeLa cells. m6A-circRNAs were identified by RNA sequencing of total RNA following ribosome depletion and m6A immunoprecipitation. The presence of m6A-circRNAs is corroborated by the identification of complexes between circRNAs and YTHDF1 and YTHDF2, proteins that “read” m6A sites in mRNAs.Furthermore, m6A modifications on non-linear RNAs depend on METTL3 and METTL14, the known m6A methyltransferase “writer” complex components, suggesting that circRNAs are methylated by the same complexes responsible for m6A modification of linear RNAs. Despite sharing m6A readers and writers, m6A-circRNAs are frequently derived from exons not methylated in mRNAs. Nevertheless, m6A-mRNAs that are methylated on the same exons as those composing m6A-circRNAs exhibit less stability than other m6A-mRNA, and this circRNA-mRNA cross-talk is regulated by YTHDF2. Thus, our results expand the m6A regulatory code through identification of the first circRNA epitranscriptome.

Summary N6-methyl-adenosine (m 6 A) is the most abundant modification on messenger RNAs and is linked to human diseases, but its functions in mammalian development are poorly understood. Here we reveal the evolutionary conservation and function of m 6 A by mapping the m 6 A methylome in mouse and human embryonic stem cells. Thousands of messenger and long noncoding RNAs show conserved m 6 A modification, including transcripts encoding core pluripotency transcription factors. m 6 A is enriched over 3′ untranslated regions at defined sequence motifs and marks unstable transcripts, including transcripts turned over upon differentiation. Genetic inactivation or depletion of mouse and human Mettl3 , one of the m 6 A methylases, led to m 6 A erasure on select target genes, prolonged Nanog expression upon differentiation, and impaired ESC exit from self-renewal toward differentiation into several lineages in vitro and in vivo. Thus, m 6 A is a mark of transcriptome flexibility required for stem cells to differentiate to specific lineages.

N6-methyladenosine–sequencing (m6A-seq) is a critical tool to obtain an unbiased genome-wide picture of m6A sites of modification at high resolution. It allows the study of the impact of various perturbations on m6A modification distribution and the study of m6A functions. Herein, we describe the m6A-seq protocol, which entails RNA immunoprecipitation (RIP) performed on fragmented poly(A) RNA utilizing anti-m6A antibodies. The captured/enriched m6A positive RNA fragments are subsequently sequenced by RNA-seq in parallel with background control non-immunoprecipitated input RNA fragments. Analyses reveals peaks of m6A enrichment containing sites of modifications analogous to chromatin modification immunoprecipitation experiments.

Profiling studies of mRNA and microRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as the Encyclopedia of DNA Elements have demonstrated the benefit of coupling RNA sequencing analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including noncoding RNAs. As a result, we have established the Immunogene database, representing an integrated EST road map of gene expression in human immune cells, which can be used to further investigate the function of coding and noncoding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus, we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes.

Many long noncoding RNA (lncRNA) species have been identified in mammalian cells, but the genomic origin and regulation of these molecules in individual cell types is poorly understood. We have generated catalogs of lncRNA species expressed in human and murine embryonic stem cells and mapped their genomic origin. A surprisingly large fraction of these transcripts (>60%) originate from divergent transcription at promoters of active protein-coding genes. The divergently transcribed lncRNA/mRNA gene pairs exhibit coordinated changes in transcription when embryonic stem cells are differentiated into endoderm. Our results reveal that transcription of most lncRNA genes is coordinated with transcription of protein-coding genes.

SummaryWhereas chromosomal translocations are common pathogenetic events in cancer, mechanisms that promote them are poorly understood. To elucidate translocation mechanisms in mammalian cells, we developed high-throughput, genome-wide translocation sequencing (HTGTS). We employed HTGTS to identify tens of thousands of independent translocation junctions involving fixed I-SceI meganuclease-generated DNA double-strand breaks (DSBs) within the c-myc oncogene or IgH locus of B lymphocytes induced for activation-induced cytidine deaminase (AID)-dependent IgH class switching. DSBs translocated widely across the genome but were preferentially targeted to transcribed chromosomal regions. Additionally, numerous AID-dependent and AID-independent hot spots were targeted, with the latter comprising mainly cryptic I-SceI targets. Comparison of translocation junctions with genome-wide nuclear run-ons revealed a marked association between transcription start sites and translocation targeting. The majority of translocation junctions were formed via end-joining with short microhomologies. Our findings have implications for diverse fields, including gene therapy and cancer genomics.

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.