Explore the vast range of titles available.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function 1 , 2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts 3 , 4 , but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

SNARE protein-driven secretion of neurotransmitters from synaptic vesicles is at the center of neuronal communication. In the absence of the cytosolic protein Munc18-1, synaptic secretion comes to a halt. Although it is believed that Munc18-1 orchestrates SNARE complexes, its mode of action is still a matter of debate. In particular, it has been challenging to clarify the role of a tight Munc18/syntaxin 1 complex, because this interaction interferes strongly with syntaxin's ability to form a SNARE complex. In this complex, two regions of syntaxin, the N-peptide and the remainder in closed conformation, bind to Munc18 simultaneously. Until now, this binary complex has been reported for neuronal tissues only, leading to the hypothesis that it might be a specialization of the neuronal secretion apparatus. Here we aimed, by comparing the core secretion machinery of the unicellular choanoflagellate Monosiga brevicollis with that of animals, to reconstruct the ancestral function of the Munc18/syntaxin1 complex. We found that the Munc18/syntaxin 1 complex from M. brevicollis is structurally and functionally highly similar to the vertebrate complex, suggesting that it constitutes a fundamental step in the reaction pathway toward SNARE assembly. We thus propose that the primordial secretion machinery of the common ancestor of choanoflagellates and animals has been co-opted for synaptic roles during the rise of animals.

Since the late 1980s, mutations in the RAS genes have been recognized as major oncogenes with a high occurrence rate in human cancers. Such mutations reduce the ability of the small GTPase RAS to hydrolyze GTP, keeping this molecular switch in a constitutively active GTP-bound form that drives, unchecked, oncogenic downstream signaling. One strategy to reduce the levels of active RAS is to target guanine nucleotide exchange factors, which allow RAS to cycle from the inactive GDP-bound state to the active GTP-bound form. Here, we describe the identification of potent and cell-active small-molecule inhibitors which efficiently disrupt the interaction between KRAS and its exchange factor SOS1, a mode of action confirmed by a series of biophysical techniques. The binding sites, mode of action, and selectivity were elucidated using crystal structures of KRASG12C–SOS1, SOS1, and SOS2. By preventing formation of the KRAS–SOS1 complex, these inhibitors block reloading of KRAS with GTP, leading to antiproliferative activity. The final compound 23 (BAY-293) selectively inhibits the KRAS–SOS1 interaction with an IC50 of 21 nM and is a valuable chemical probe for future investigations.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL , which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32–1.54, P = 3.3 × 10 −20 ). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images. Genome-wide association analyses identify variants associated with thoracic aortic diameter. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in independent samples.

The removal of introns from pre-mRNA is carried out by a large macromolecular machine called the spliceosome. The peptidyl-prolyl cis/trans isomerase PPIL1 is a component of the human spliceosome and binds to the spliceosomal SKIP protein via a binding site distinct from its active site. Here, we have studied the PPIL1 protein and its interaction with SKIP biochemically and by X-ray crystallography. A minimal linear binding epitope derived from the SKIP protein could be determined using a peptide array. A 36-residue region of SKIP centred on an eight-residue epitope suffices to bind PPIL1 in pull-down experiments. The crystal structure of PPIL1 in complex with the inhibitor cyclosporine A (CsA) was obtained at a resolution of 1.15 A and exhibited two bound Cd(2+) ions that enabled SAD phasing. PPIL1 residues that have previously been implicated in binding of SKIP are involved in the coordination of Cd(2+) ions in the present crystal structure. Employing the present crystal structure, the determined minimal binding epitope and previously published NMR data, a molecular docking study was performed. In the docked model of the PPIL1.SKIP interaction, a proline residue of SKIP is buried in a hydrophobic pocket of PPIL1. This hydrophobic contact is encircled by several hydrogen bonds between the SKIP peptide and PPIL1. We characterized a short, linear epitope of SKIP that is sufficient to bind the PPIL1 protein. Our data indicate that this SKIP peptide could function in recruiting PPIL1 into the core of the spliceosome. We present a molecular model for the binding mode of SKIP to PPIL1 which emphasizes the versatility of cyclophilin-type PPIases to engage in additional interactions with other proteins apart from active site contacts despite their limited surface area.

Heart failure is a growing public health concern which encompasses a heterogenous set of clinical features, converging on impaired cardiac contractile function.1,2 Previous work has highlighted changes in both transcription and protein expression in failing hearts,3,4 but may overlook molecular changes in less prevalent cell types. Here, we identify extensive molecular alterations present in failing hearts at single-cell resolution by performing single-nuclei RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 dilated cardiomyopathy, 15 hypertrophic cardiomyopathy and 16 non-failing hearts. Broadly, the transcriptional profiles of dilated and hypertrophic cardiomyopathy patients converged at the tissue and cell type level. Further, a subset of cardiomyopathy patients harbor a unique population of activated fibroblasts nearly entirely absent from non-failing samples. A CRISPR knockout screen was performed in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition. After knocking out genes associated with fibroblast transition in vivo, we observed a reduction in myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide novel insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32-1.54, P = 3.3 × 10-20). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.

Despite the critical role of the cardiovascular system, our understanding of its cellular and transcriptional diversity remains limited. We therefore sought to characterize the cellular composition, phenotypes, molecular pathways, and communication networks between cell types at the tissue and sub-tissue level across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We obtained high quality tissue samples under controlled conditions that reveal a level of cellular detail so far inaccessible in human studies. We performed single nucleus RNA-sequencing in 78 samples in 10 distinct regions including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins (PV), which produced an aggregate map of 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, including a number of rare cell types such as PV cardiomyocytes and non-myelinating Schwann cells (NMSCs), and unique groups of vascular smooth muscle cells (VSMCs), endothelial cells (ECs) and fibroblasts (FBs), which gave rise to a detailed cell type distribution across tissues. We demonstrated differences in the cellular composition across different cardiac regions and tissue-specific differences in transcription for each cell type, highlighting the molecular diversity and complex tissue architecture of the cardiovascular system. Specifically, we observed great transcriptional heterogeneities among ECs and FBs. Importantly, several cell subtypes had a unique regional localization such as a subtype of VSMCs enriched in the large vasculature. We found the cellular makeup of PV tissue is closer to heart tissue than to the large arteries. We further explored the ligand-receptor repertoire across cell clusters and tissues, and observed tissue-enriched cellular communication networks, including heightened - / / signaling in the sinoatrial node. Through a large single nucleus sequencing effort encompassing over 500,000 nuclei, we broadened our understanding of cellular transcription in the healthy cardiovascular system. The existence of tissue-restricted cellular phenotypes suggests regional regulation of cardiovascular physiology. The overall conservation in gene expression and molecular pathways across rat and human cell types, together with our detailed transcriptional characterization of each cell type, offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibody's variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the model's training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets.Competing Interest StatementThe authors are current or former employees, contractors, interns, or executives of Absci Corporation and may hold shares in Absci Corporation.Footnotes* Open sourcing data and code. Analysis of self-consistency RMSD (scRMSD) as a metric for predicting binding.* https://github.com/AbSciBio/igdesign/tree/main