BackgroundAdenosine-to-inosine (A-to-I) editing of dsRNA by ADAR proteins is a pervasive epitranscriptome feature. Tens of thousands of A-to-I editing events are defined in the mouse, yet the functional impact of most is unknown. Editing causing protein recoding is the essential function of ADAR2, but an essential role for recoding by ADAR1 has not been demonstrated. ADAR1 has been proposed to have editing-dependent and editing-independent functions. The relative contribution of these in vivo has not been clearly defined. A critical function of ADAR1 is editing of endogenous RNA to prevent activation of the dsRNA sensor MDA5 (Ifih1). Outside of this, how ADAR1 editing contributes to normal development and homeostasis is uncertain.ResultsWe describe the consequences of ADAR1 editing deficiency on murine homeostasis. Adar1 E861A/E861A Ifih1 -/- mice are strikingly normal, including their lifespan. There is a mild, non-pathogenic innate immune activation signature in the Adar1 E861A/E861A Ifih1 -/- mice. Assessing A-to-I editing across adult tissues demonstrates that outside of the brain, ADAR1 performs the majority of editing and that ADAR2 cannot compensate in its absence. Direct comparison of the Adar1 -/- and Adar1 E861A/E861A alleles demonstrates a high degree of concordance on both Ifih1 +/+ and Ifih1 -/- backgrounds, suggesting no substantial contribution from ADAR1 editing-independent functions.ConclusionsThese analyses demonstrate that the lifetime absence of ADAR1-editing is well tolerated in the absence of MDA5. We conclude that protein recoding arising from ADAR1-mediated editing is not essential for organismal homeostasis. Additionally, the phenotypes associated with loss of ADAR1 are the result of RNA editing and MDA5-dependent functions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1301-4) contains supplementary material, which is available to authorized users.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. 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 interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Highlights d BioID identifies novel ADAR interactors, including regulators of A-to-I RNA editing d Interactors include all DZF-domain-containing proteins: ILF2, ILF3, STRBP, and ZFR d DZF-domain-containing proteins bind ADAR in an RNAdependent manner d ILF3 is a broadly influential negative regulator of editing

SummaryOne major obstacle to the application of stem cell-derived cardiomyocytes (CMs) for disease modeling and clinical therapies is the inability to identify the developmental stage of these cells without the need for genetic manipulation or utilization of exogenous markers. In this study, we demonstrate that Raman microspectroscopy can non-invasively identify embryonic stem cell (ESC)-derived chamber-specific CMs and monitor cell maturation. Using this marker-free approach, Raman peaks were identified for atrial and ventricular CMs, ESCs were successfully discriminated from their cardiac derivatives, a distinct phenotypic spectrum for ESC-derived CMs was confirmed, and unique spectral differences between fetal versus adult CMs were detected. The real-time identification and characterization of CMs, their progenitors, and subpopulations by Raman microspectroscopy strongly correlated to the phenotypical features of these cells. Due to its high molecular resolution, Raman microspectroscopy offers distinct analytical characterization for differentiating cardiovascular cell populations.