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.

Human pleural fluid triggers global changes in the transcriptional landscape of Acinetobacter baumannii as an adaptive response to stress.

Acinetobacter baumannii is a feared, drug-resistant pathogen, characterized by its ability to resist extreme environmental and nutrient-deprived conditions. Previously, we showed that human serum albumin (HSA) can increase foreign DNA acquisition specifically and alter the expression of genes associated with pathogenicity. Moreover, in a recent genome-wide transcriptomic study, we observed that pleural fluid (PF), an HSA-containing fluid, increases DNA acquisition, can modulate cytotoxicity, and control immune responses by eliciting changes in the A. baumannii metabolic profile. In the present work, using more stringent criteria and focusing on the analysis of genes related to pathogenicity and response to stress, we analyzed our previous RNA-seq data and performed phenotypic assays to further explore the impact of PF on A. baumannii's microbial behavior and the strategies used to overcome environmental stress. We observed that PF triggered differential expression of genes associated with motility, efflux pumps, antimicrobial resistance, biofilm formation, two-component systems (TCSs), capsule synthesis, osmotic stress, and DNA-damage response, among other categories. Phenotypic assays of A. baumannii A118 and two other clinical A. baumannii strains, revealed differences in their responses to PF in motility, biofilm formation, antibiotic susceptibility, osmotic stress, and outer membrane vesicle (OMV) production, suggesting that these changes are strain specific. We conclude that A. baumannii's pathoadaptive responses is induced by HSA-containing fluids and must be part of this bacterium armamentarium to persist in hostile environments.

Human Pleural Fluid Elicits Pyruvate and Phenylalanine Metabolism in Acinetobacter baumannii to Enhance Cytotoxicity and Immune Evasion.

Acinetobacter baumannii (Ab) is one of the most treacherous pathogens among those causing hospital-acquired pneumonia (HAP). A. baumannii possesses an adaptable physiology, seen not only in its antibiotic resistance and virulence phenotypes but also in its metabolic versatility. In this study, we observed that A. baumannii undergoes global transcriptional changes in response to human pleural fluid (PF), a key host-derived environmental signal. Differential gene expression analyses combined with experimental approaches revealed changes in A. baumannii metabolism, affecting cytotoxicity, persistence, bacterial killing, and chemotaxis. Over 1,220 genes representing 55% of the differentially expressed transcriptomic data corresponded to metabolic processes, including the upregulation of glutamate, short chain fatty acid, and styrene metabolism. We observed an upregulation by 1.83- and 2.61-fold of the pyruvate dehydrogenase complex subunits E3 and E2, respectively. We also found that pyruvate (PYR), in conjunction with PF, triggers an A. baumannii pathogenic behavior that adversely impacts human epithelial cell viability. Interestingly, PF also amplified A. baumannii cytotoxicity against murine macrophages, suggesting an immune evasion strategy implemented by A. baumannii. Moreover, we uncovered opposing metabolic strategies dependent on the degree of pathogenicity of the strains, where less pathogenic strains demonstrated greater utilization of PYR to promote persister formation in the presence of PF. Additionally, our transcriptomic analysis and growth studies of A. baumannii suggest the existence of an alternative phenylalanine (PA) catabolic route independent of the phenylacetic acid pathway, which converts PA to phenylpyruvate (PP) and shuttles intermediates into styrene metabolism. This alternative route promoted a neutrophil-evasive state, as PF-induced degradation of PP significantly reduced overall human neutrophil chemotaxis in ex vivo chemotactic assays. Taken together, these data highlight A. baumannii pathoadaptabililty in response to host signals and provide further insight into the role of bacterial metabolism in virulence traits, antibiotic persistence strategies, and host innate immune evasion.