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Background Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We address the need for an intuitive and interactive web application to explore the transcriptional signatures of heart failure with this work. Methods We reanalysed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts (NFD) from organ donors and patient characteristics that allowed us to model confounding factors. We analyse differential gene expression, associated pathway signatures and reconstruct signaling networks based on inferred transcription factor activities through integer linear programming. We additionally focus, for the first time, on differential RNA transcript isoform usage (DTU) changes and predict RNA-binding protein (RBP) to target transcript interactions using a Global test approach. We report results for all pairwise comparisons (DCM, HCM, NFD). Results Focusing on the DCM versus HCM contrast (DCMvsHCM), we identified 201 differentially expressed genes, some of which can be clearly associated with changes in ERK1 and ERK2 signaling. Interestingly, the signs of the predicted activity for these two kinases have been inferred to be opposite to each other: In the DCMvsHCM contrast, we predict ERK1 to be consistently less activated in DCM while ERK2 was more activated in DCM. In the DCMvsHCM contrast, we identified 149 differently used transcripts. One of the top candidates is the O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), which catalyzes a common post-translational modification known for its role in heart arrhythmias and heart hypertrophy. Moreover, we reconstruct RBP – target interaction networks and showcase the examples of CPEB1, which is differentially expressed in the DCMvsHCM contrast. Conclusion Magnetique (https://shiny.dieterichlab.org/app/magnetique) is the first online application to provide an interactive view of the HF transcriptome at the RNA isoform level and to include transcription factor signaling and RBP:RNA interaction networks. The source code for both the analyses (https://github.com/dieterich-lab/magnetiqueCode2022) and the web application (https://github.com/AnnekathrinSilvia/magnetique) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

A whole series of high-throughput antibody-free methods for RNA modification detection from sequencing data emerged lately. We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. Importantly, JACUSA2 can integrate information from multiple experiments (e.g. replicates and different conditions) and different library types (e.g. first- or secondstrand libraries). We demonstrate its utility by example, showing three analysis workflows for m6A detection on published data sets: 1) MazF m6a-sensitive RNA digestion (FTO+ vs FTO-), 2) DART-seq (YTHwt vs YTHmut) and 3) Nanopore profiling (METTL3 +/+ vs -/-). All assays have been conducted in HEK293 cells and complement one another.

Catecholamines stimulate the first step of lipolysis by PKA-dependent release of the lipid droplet-associated protein ABHD5 from perilipin to co-activate the lipase ATGL. Here, we unmask a yet unrecognized proteolytic and cardioprotective function of ABHD5. ABHD5 acts in vivo and in vitro as a serine protease cleaving HDAC4. Through the production of an N-terminal polypeptide of HDAC4 (HDAC4-NT), ABHD5 inhibits MEF2-dependent gene expression and thereby controls glucose handling. ABHD5-deficiency leads to neutral lipid storage disease in mice. Cardiac-specific gene therapy of HDAC4-NT does not protect from intra-cardiomyocyte lipid accumulation but strikingly from heart failure, thereby challenging the concept of lipotoxicity-induced heart failure. ABHD5 levels are reduced in failing human hearts and murine transgenic ABHD5 expression protects from pressure-overload induced heart failure. These findings represent a conceptual advance by connecting lipid with glucose metabolism through HDAC4 proteolysis and enable new translational approaches to treat cardiometabolic disease.

Coronavirus disease 2019 (COVID-19) spawned a global health crisis in late 2019 and is caused by the novel coronavirus SARS-CoV-2. SARS-CoV-2 infection can lead to elevated markers of endothelial dysfunction associated with higher risk of mortality. It is unclear whether endothelial dysfunction is caused by direct infection of endothelial cells or is mainly secondary to inflammation. Here, we investigate whether different types of endothelial cells are susceptible to SARS-CoV-2. Human endothelial cells from different vascular beds including umbilical vein endothelial cells, coronary artery endothelial cells (HCAEC), cardiac and lung microvascular endothelial cells, or pulmonary arterial cells were inoculated in vitro with SARS-CoV-2. Viral spike protein was only detected in HCAECs after SARS-CoV-2 infection but not in the other endothelial cells tested. Consistently, only HCAEC expressed the SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2), required for virus infection. Infection with the SARS-CoV-2 variants B.1.1.7, B.1.351, and P.2 resulted in significantly higher levels of viral spike protein. Despite this, no intracellular double-stranded viral RNA was detected and the supernatant did not contain infectious virus. Analysis of the cellular distribution of the spike protein revealed that it co-localized with endosomal calnexin. SARS-CoV-2 infection did induce the ER stress gene EDEM1, which is responsible for clearance of misfolded proteins from the ER. Whereas the wild type of SARS-CoV-2 did not induce cytotoxic or pro-inflammatory effects, the variant B.1.1.7 reduced the HCAEC cell number. Of the different tested endothelial cells, HCAECs showed highest viral uptake but did not promote virus replication. Effects on cell number were only observed after infection with the variant B.1.1.7, suggesting that endothelial protection may be particularly important in patients infected with this variant. Supplementary Information The online version contains supplementary material available at 10.1007/s00395-021-00882-8.

Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcripts with elusive functions in metabolism. Here we show that a high fraction of lncRNAs, but not protein-coding mRNAs, are repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation induced lncRNAs in mouse liver. Similarly, lncRNAs are lost in diabetic humans. LncRNA promoter analyses, global cistrome and gain-of-function analyses confirm that increased MAFG signaling during DIO curbs lncRNA expression. Silencing Mafg in mouse hepatocytes and obese mice elicits a fasting-like gene expression profile, improves glucose metabolism, de-represses lncRNAs and impairs mammalian target of rapamycin (mTOR) activation. We find that obesity-repressed LincIRS2 is controlled by MAFG and observe that genetic and RNAi-mediated LincIRS2 loss causes elevated blood glucose, insulin resistance and aberrant glucose output in lean mice. Taken together, we identify a MAFG-lncRNA axis controlling hepatic glucose metabolism in health and metabolic disease., Despite widespread transcription of LncRNA in mammalian systems, their contribution to metabolic homeostasis at the cellular and tissue level remains elusive. Here Pradas-Juni et al. describe a transcription factor–LncRNA pathway that couples hepatocyte nutrient sensing to regulation of glucose metabolism in mice.

Macroautophagy, a key player in protein quality control, is proposed to be systematically impaired in distinct tissues and causes coordinated disruption of protein homeostasis and ageing throughout the body. Although tissue-specific changes in autophagy and ageing have been extensively explored, the mechanism underlying the inter-tissue regulation of autophagy with ageing is poorly understood. Here, we show that a secreted microRNA, mir-83/miR-29, controls the age-related decrease in macroautophagy across tissues in Caenorhabditis elegans. Upregulated in the intestine by hsf-1/HSF1 with age, mir-83 is transported across tissues potentially via extracellular vesicles and disrupts macroautophagy by suppressing CUP-5/MCOLN, a vital autophagy regulator, autonomously in the intestine as well as non-autonomously in body wall muscle. Mutating mir-83 thereby enhances macroautophagy in different tissues, promoting protein homeostasis and longevity. These findings thus identify a microRNA-based mechanism to coordinate the decreasing macroautophagy in various tissues with age., Decreased autophagy is a hallmark of ageing, but its inter-tissue regulation is poorly understood. Here, Zhou et al. identify mir-83 in C. elegans, which is transported across tissues and suppresses autophagy, contributing to age-related decline.

Levels of residual structure in disordered interaction domains determine in vitro binding affinities, but whether they exert similar roles in cells is not known. Here, we show that increasing residual p53 helicity results in stronger Mdm2 binding, altered p53 dynamics, impaired target gene expression and failure to induce cell cycle arrest upon DNA damage. These results establish that residual structure is an important determinant of signaling fidelity in cells.

Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors, which typically bind to specific, short DNA sequence motifs usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the ab initio identification of these cis-regulatory motifs. The method we present integrates several elements: human-mouse comparison, statistical analysis of genomic sequences and the concept of coregulation. We apply it to a complete scan of the human genome. By using the catalogue of conserved upstream sequences collected in the CORG database we construct sets of genes sharing the same overrepresented motif (short DNA sequence) in their upstream regions both in human and in mouse. We perform this construction for all possible motifs from 5 to 8 nucleotides in length and then filter the resulting sets looking for two types of evidence of coregulation: first, we analyze the Gene Ontology annotation of the genes in the set, searching for statistically significant common annotations; second, we analyze the expression profiles of the genes in the set as measured by microarray experiments, searching for evidence of coexpression. The sets which pass one or both filters are conjectured to contain a significant fraction of coregulated genes, and the upstream motifs characterizing the sets are thus good candidates to be the binding sites of the TF's involved in such regulation. In this way we find various known motifs and also some new candidate binding sites. We have discussed a new integrated algorithm for the "ab initio" identification of transcription factor binding sites in the human genome. The method is based on three ingredients: comparative genomics, overrepresentation, different types of coregulation. The method is applied to a full-scan of the human genome, giving satisfactory results.