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.

Although the spatial, cellular and molecular landscapes of resected pancreatic ductal adenocarcinoma (PDAC) are well documented, the characteristics of its metastatic ecology remain elusive. By applying spatially resolved transcriptomics to matched primary and metastatic PDAC samples, we discovered a conserved continuum of fibrotic, metabolic and immunosuppressive spatial ecotypes across anatomical regions. We observed spatial tumor microenvironment heterogeneity spanning beyond that previously appreciated in PDAC. Through comparative analysis, we show that the spatial ecotypes exhibit distinct enrichment between primary and metastatic sites, implying adaptability to the local environment for survival and progression. The invasive border ecotype exhibits both pro-tumorigenic and anti-tumorigenic cell-type enrichment, suggesting a potential immunotherapy target. The ecotype heterogeneity across patients emphasizes the need to map individual patient landscapes to develop personalized treatment strategies. Collectively, our findings provide critical insights into metastatic PDAC biology and serve as a valuable resource for future therapeutic exploration and molecular investigations. Spot-based spatial transcriptomic analysis of paired primary and metastatic pancreatic cancers identifies cellular, metabolic and fibrotic changes in ecotypes associated with progression, highlighting the contribution of the tumor microenvironment.

Plasma IL-6 is elevated after myocardial infarction (MI) and is associated with increased morbidity and mortality. Which cardiac cell type preferentially contributes to IL-6 expression and how its production is regulated are largely unknown. Here, we studied the cellular source and purinergic regulation of IL-6 formation in a murine MI model. We found that IL-6, measured in various cell types in post-MI hearts at the protein level and by quantitative PCR and RNAscope, was preferentially formed by cardiac fibroblasts (CFs). Single-cell RNA-Seq (scRNA-Seq) in infarcted mouse and human hearts confirmed this finding. We found that adenosine stimulated fibroblast IL-6 formation via the adenosine receptor A2bR in a Gq-dependent manner. CFs highly expressed Adora2b and rapidly degraded extracellular ATP to AMP but lacked CD73. In mice and humans, scRNA-Seq revealed that Adora2B was also mainly expressed by fibroblasts. We assessed global IL-6 production in isolated hearts from mice lacking CD73 on T cells (CD4-CD73-/-), a condition known to be associated with adverse cardiac remodeling. The ischemia-induced release of IL-6 was strongly attenuated in CD4-CD73-/- mice, suggesting adenosine-mediated modulation. Together, these findings demonstrate that post-MI IL-6 was mainly derived from activated CFs and was controlled by T cell-derived adenosine. We show that purinergic metabolic cooperation between CFs and T cells is a mechanism that modulates IL-6 formation by the heart and has therapeutic potential.

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning–based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification.

Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell–cell communication (CCC) is mediated by underlying gene–gene networks, however most current methods are unable to account for complex interactions within the cell as well as incorporate the effect of pathway and protein complexes on interactions. This results in the inability to infer overarching signalling patterns within a dataset as well as limit the ability to successfully explore other data types such as spatial cell dimension. Therefore, to represent transcriptomic data as intricate networks, complementing gene expression with information from cells to ligands and receptors for relevant CCC inference, we present GraphComm—a new graph-based deep learning method for predicting CCC in single-cell RNAseq datasets. GraphComm improves CCC inference by capturing detailed information such as cell location and intracellular signalling patterns from a database of more than 30,000 protein interaction pairs. With this framework, GraphComm is able to predict biologically relevant results in datasets previously validated for CCC, datasets that have undergone chemical or genetic perturbations and datasets with spatial cell information.

Mitochondria fulfill central functions in cellular energetics, metabolism, and signaling. The outer membrane translocator complex (the TOM complex) imports most mitochondrial proteins, but its architecture is unknown. Using a cross-linking approach, we mapped the active translocator down to single amino acid residues, revealing different transport paths for preproteins through the Tom40 channel. An N-terminal segment of Tom40 passes from the cytosol through the channel to recruit chaperones from the intermembrane space that guide the transfer of hydrophobic preproteins. The translocator contains three Tom40 β-barrel channels sandwiched between a central α-helical Tom22 receptor cluster and external regulatory Tom proteins. The preprotein-translocating trimeric complex exchanges with a dimeric isoform to assemble new TOM complexes. Dynamic coupling of α-helical receptors, β-barrel chennels, and chaperones generates a versatile machinery that transports about 1000 different proteins.

In this study, the thermoluminescence characteristics of naturally occurring salt (NaCl) were assessed for the development of a radiation dosimeter. For this purpose, mined crystalline samples of salt were procured directly from Khewra salt mines in Pakistan. The samples were hand crushed, sieved, and compressed to pellets comparable in size to standard TLD chips, and irradiated to gamma radiation doses in the range of 5 mGy and 5000 mGy. Thermoluminescence (TL) response showed three main peaks in the glow curve around 115–130 °C, 150–170 °C, and 220–240 °C. A linear TL response was observed for the dose range of 5–100 mGy. The TL response became supra-linear for the dose ranges of 100–1000 mGy and 1000–5000 mGy. The Tm-Tstop method was applied to identify the overlapping peaks of the glow curve. Computerized glow curve deconvolution (CGCD) was then employed for the characterization of electron trap parameters such as frequency factor (s), activation energy (E), and the kinetic order (b), using General Order (GO) kinetics. The figure-of-merit (FOM) was found to be 1.08%, 0.94%, 0.77%, and 0.75%, at 500 mGy, 1 Gy, 2 Gy, and 5 Gy, respectively. The TL intensity faded by 20% within the first 24 h after irradiation and finally stabilized after two weeks. In addition, structural, morphological, and elemental analyses, were also performed using various analytical techniques. X-ray diffraction (XRD) showed that the salt crystallizes in a face-centered cubic structure. Scanning electron microscope (SEM) micrographs indicated that the crystallites are closely packed and cubic-shaped with non-uniform size, and mostly found in the agglomerated form. Similarly, the elemental analysis confirmed the presence of impurities such as Mg, Sr, S, K, O, and Ca, in the samples. The present study concludes that the pellets made from salt samples from Khewra mines have a potential for use as radiation dosimeters.

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.

Significance EVfold_bb predicts all-atom 3D models of large transmembrane β-barrel proteins that are notoriously hard to determine experimentally. In some cases the algorithm can identify interactions between large extracellular loops, plugs, and the transmembrane β-strands. The major advance is the combination of evolutionary couplings between amino acid residues from protein family sequence alignments with prediction of β-strands to ascertain residue pairs that are hydrogen bonded between adjacent β-strands. The method will enable biological research into the function and druggability of outer-membrane proteins. Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand–strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

Fibrosis represents the uncontrolled replacement of parenchymal tissue with extracellular matrix (ECM) produced by myofibroblasts. While genetic fate-tracing and single-cell RNA-Seq technologies have helped elucidate fibroblast heterogeneity and ontogeny beyond fibroblast to myofibroblast differentiation, newly identified fibroblast populations remain ill defined, with respect to both the molecular cues driving their differentiation and their subsequent role in fibrosis. Using an unbiased approach, we identified the metalloprotease ADAMTS12 as a fibroblast-specific gene that is strongly upregulated during active fibrogenesis in humans and mice. Functional in vivo KO studies in mice confirmed that Adamts12 was critical during fibrogenesis in both heart and kidney. Mechanistically, using a combination of spatial transcriptomics and expression of catalytically active or inactive ADAMTS12, we demonstrated that the active protease of ADAMTS12 shaped ECM composition and cleaved hemicentin 1 (HMCN1) to enable the activation and migration of a distinct injury-responsive fibroblast subset defined by aberrant high JAK/STAT signaling.