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

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.

SenePy: Unveiling the Cell-Type Specific Landscape of Cellular Senescence through Single-Cell Analysis in Living Organisms (preprint)

Senescent cells accumulate in tissues with organismal age and contribute causally to multiple chronic diseases. In vivo senescent cell phenotypes are heterogeneous because cellular context and stressors vary by cell type and tissue. Due to the variability of senescence programs, there is no universal method to identify senescent cells and even widely used markers, such as CDKN2A, are not ubiquitous. Therefore, we interrogated the Tabula Muris Senis mouse single-cell aging atlas and an array of single-cell datasets from human donors that spanned many ages to find cell-specific signatures of cellular senescence. We derived 75 mouse and 65 human senescence signatures from individual cell populations. CDKN2A and other markers of senescence were overrepresented in these signatures but there were many novel senescence genes present at higher rates. Within individual cell populations, we observed multiple programs of senescence with distinct temporal and transcriptional characteristics. We packaged the signatures along with a single-cell scoring method into an open-source package: SenePy. SenePy signatures better recapitulate cellular senescence than available methods when tested on multiple in vivo RNA-seq datasets and a p16ink4a reporter single-cell dataset. We used SenePy to map the kinetics of senescent cell accumulation across 97 cell types from humans and mice. SenePy also generalizes to disease-associate senescence and we used it to identify an increased burden of senescent cells in COVID-19 and myocardial infarction. This work provides a significant advancement towards our ability to identify and characterize in vivo cellular senescence.

Systematic temporal analysis of peripheral blood transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as a hallmark of severe COVID-19 (preprint)

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher ( https://github.com/jaleesr/TrendCatcher ), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk RNA-seq and scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS- CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.