The advent of 16S RNA profiling and shotgun metagenomics has enabled a holistic approach to the study of the skin microbiome composition. Despite the interesting findings in this rapidly developing scientific area, the big question remains: What role does the microbiome play in skin physiology? To begin answering this question, we employed an integrative methodology for microbiome and metabolome analysis of skin surface samples collected from the volar forearm of healthy infants aged 3–6-months. Whereas the infant skin metabolome was dominated by amino acids, lipids, and xenobiotics, the primary phyla of the microbiome were Firmicutes, Actinobacteria, and Proteobacteria. Zooming in on the species level revealed a large contribution of commensals belonging to the Cutibacterium and Staphylococcus genera, including Cutibacterium acnes, Staphylococcus epidermidis, and S. aureus. This heterogeneity was further highlighted when combining the microbiome with metabolome data. Integrative analyses delineated the coexistence of three distinct metabolite‒microbe clusters: one dominated by Cutibacterium linked to hydrophobic elements of the skin barrier, one associating Staphylococcus genus with amino acids relevant to the water holding capacity and pH regulation of the skin surface, and one characterized by Streptococcus and independent of any particular metabolomic profile.

Senescent cells affect many physiological and pathophysiological processes. While select genetic and epigenetic elements for senescence induction have been identified, the dynamics, epigenetic mechanisms and regulatory networks defining senescence competence, induction and maintenance remain poorly understood, precluding the deliberate therapeutic targeting of senescence for health benefits. Here, we examined the possibility that the epigenetic state of enhancers determines senescent cell fate. We explored this by generating time-resolved transcriptomes and epigenome profiles during oncogenic RAS-induced senescence and validating central findings in different cell biology and disease models of senescence. Through integrative analysis and functional validation, we reveal links between enhancer chromatin, transcription factor recruitment and senescence competence. We demonstrate that activator protein 1 (AP-1) ‘pioneers’ the senescence enhancer landscape and defines the organizational principles of the transcription factor network that drives the transcriptional programme of senescent cells. Together, our findings enabled us to manipulate the senescence phenotype with potential therapeutic implications.

Primary liver cancer represents a major health problem. It comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which differ markedly with regards to their morphology, metastatic potential and responses to therapy. However, the regulatory molecules and tissue context that commit transformed hepatic cells towards HCC or ICC are largely unknown. Here we show that the hepatic microenvironment epigenetically shapes lineage commitment in mosaic mouse models of liver tumorigenesis. Whereas a necroptosis-associated hepatic cytokine microenvironment determines ICC outgrowth from oncogenically transformed hepatocytes, hepatocytes containing identical oncogenic drivers give rise to HCC if they are surrounded by apoptotic hepatocytes. Epigenome and transcriptome profiling of mouse HCC and ICC singled out Tbx3 and Prdm5 as major microenvironment-dependent and epigenetically regulated lineage-commitment factors, a function that is conserved in humans. Together, our results provide insight into lineage commitment in liver tumorigenesis, and explain molecularly why common liver-damaging risk factors can lead to either HCC or ICC.

Cellular senescence is a fundamental cell fate, important both in physiological and pathophysiological processes. This SnapShot focuses on the role of cellular senescence in health, disease, and aging.

Cellular senescence is a fundamental cell fate, playing important physiological and pathophysiological roles. This SnapShot focuses on major signaling pathways and transcriptional control mechanisms that consolidate the senescence phenotype.

RNA editing is a posttranscriptional process leading to differences between genomic DNA and transcript sequences, potentially enhancing transcriptome diversity. With recent advances in high-throughput sequencing, many efforts have been made to describe mRNA editing at the transcriptome scale, especially in mammals, yielding contradictory conclusions regarding the extent of this phenomenon. We show, by detailed description of the 25 studies focusing so far on mRNA editing at the whole-transcriptome scale, that systematic sequencing artifacts are considered in most studies whereas biological replication is often neglected and multi-alignment not properly evaluated, which ultimately impairs the legitimacy of results. We recently developed a rigorous strategy to identify mRNA editing using mRNA and genomic DNA sequencing, taking into account sequencing and mapping artifacts, and biological replicates. We applied this method to screen for mRNA editing in liver and white adipose tissue from eight chickens and confirm the small extent of mRNA recoding in this species. Among the 25 unique edited sites identified, three events were previously described in mammals, attesting that this phenomenon is conserved throughout evolution. Deeper investigations on five sites revealed the impact of tissular context, genotype, age, feeding conditions, and sex on mRNA editing levels. More specifically, this analysis highlighted that the editing level at the site located on COG3 was strongly regulated by four of these factors. By comprehensively characterizing the mRNA editing landscape in chickens, our results highlight how this phenomenon is limited and suggest regulation of editing levels by various genetic and environmental factors.

Very few causal genes have been identified by quantitative trait loci (QTL) mapping because of the large size of QTL, and most of them were identified thanks to functional links already known with the targeted phenotype. Here, we propose to combine selection signature detection, coding SNP annotation, and cis-expression QTL analyses to identify potential causal genes underlying QTL identified in divergent line designs. As a model, we chose experimental chicken lines divergently selected for only one trait, the abdominal fat weight, in which several QTL were previously mapped. Using new haplotype-based statistics exploiting the very high SNP density generated through whole-genome resequencing, we found 129 significant selective sweeps. Most of the QTL colocalized with at least one sweep, which markedly narrowed candidate region size. Some of those sweeps contained only one gene, therefore making them strong positional causal candidates with no presupposed function. We then focused on two of these QTL/sweeps. The absence of nonsynonymous SNPs in their coding regions strongly suggests the existence of causal mutations acting in cis on their expression, confirmed by cis-eQTL identification using either allele-specific expression or genetic mapping analyses. Additional expression analyses of those two genes in the chicken and mice contrasted for adiposity reinforces their link with this phenotype. This study shows for the first time the interest of combining selective sweeps mapping, coding SNP annotation and cis-eQTL analyses for identifying causative genes for a complex trait, in the context of divergent lines selected for this specific trait. Moreover, it highlights two genes, JAG2 and PARK2, as new potential negative and positive key regulators of adiposity in chicken and mice.

We report an analysis of allele-specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data.