Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the Rho pathway.

Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease. Exome-wide associations identify a novel variant in ARHGEF18, a Rho guanine exchange factor specifically expressed in glomeruli. Overexpression of ARHGEF18 in human podocytes leads to impairments in focal adhesion architecture, cytoskeletal dynamics, cellular motility, and RhoA/Rac1 activation. Mutant GEF18 is resistant to ubiquitin mediated degradation leading to pathologically increased protein levels. Our findings uncover the first known disease-causing genetic variant that affects protein stability of a cytoskeletal regulator through impaired degradation, a potentially novel class of expression quantitative trait loci that can be therapeutically targeted.

Megakaryopoiesis impairment through acute innate immune signaling activation by azacitidine.

Thrombocytopenia, prevalent in the majority of patients with myeloid malignancies, such as myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML), is an independent adverse prognostic factor. Azacitidine (AZA), a mainstay therapeutic agent for stem cell transplant-ineligible patients with MDS/AML, often transiently induces or further aggravates disease-associated thrombocytopenia by an unknown mechanism. Here, we uncover the critical role of an acute type-I interferon (IFN-I) signaling activation in suppressing megakaryopoiesis in AZA-mediated thrombocytopenia. We demonstrate that megakaryocytic lineage-primed progenitors present IFN-I receptors and, upon AZA exposure, engage STAT1/SOCS1-dependent downstream signaling prematurely attenuating thrombopoietin receptor (TPO-R) signaling and constraining megakaryocytic progenitor cell growth and differentiation following TPO-R stimulation. Our findings directly implicate RNA demethylation and IFN-I signal activation as a root cause for AZA-mediated thrombocytopenia and suggest mitigation of TPO-R inhibitory innate immune signaling as a suitable therapeutic strategy to support platelet production, particularly during the early phases of AZA therapy.

Plexin-B2 orchestrates collective stem cell dynamics via actomyosin contractility, cytoskeletal tension and adhesion.

During morphogenesis, molecular mechanisms that orchestrate biomechanical dynamics across cells remain unclear. Here, we show a role of guidance receptor Plexin-B2 in organizing actomyosin network and adhesion complexes during multicellular development of human embryonic stem cells and neuroprogenitor cells. Plexin-B2 manipulations affect actomyosin contractility, leading to changes in cell stiffness and cytoskeletal tension, as well as cell-cell and cell-matrix adhesion. We have delineated the functional domains of Plexin-B2, RAP1/2 effectors, and the signaling association with ERK1/2, calcium activation, and YAP mechanosensor, thus providing a mechanistic link between Plexin-B2-mediated cytoskeletal tension and stem cell physiology. Plexin-B2-deficient stem cells exhibit premature lineage commitment, and a balanced level of Plexin-B2 activity is critical for maintaining cytoarchitectural integrity of the developing neuroepithelium, as modeled in cerebral organoids. Our studies thus establish a significant function of Plexin-B2 in orchestrating cytoskeletal tension and cell-cell/cell-matrix adhesion, therefore solidifying the importance of collective cell mechanics in governing stem cell physiology and tissue morphogenesis.