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.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection (preprint)

Background. Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods. In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells (PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared the functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results. Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we found that 164/2635 (6.2%) of the significantly differentiated genes were associated with overall decrease in long-term kidney function. The strongest associations were autophagy, renal impairment via fibrosis and cardiac structure/function. Conclusions. We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function, and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures indicating generalizability in therapeutic approaches.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection (preprint)

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-Cindicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers (preprint)

Importance Passive and non-invasive identification of SARS-CoV-2 infection remains a challenge. Widespread use of wearable devices represents an opportunity to leverage physiological metrics and fill this knowledge gap. Objective To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Design A multicenter observational study enrolling health care workers with remote follow-up. Setting Seven hospitals from the Mount Sinai Health System in New York City Participants Eligibility criteria included health care workers who were ≥18 years, employees of one of the participating hospitals, with at least an iPhone series 6, and willing to wear an Apple Watch Series 4 or higher. We excluded participants with underlying autoimmune/inflammatory diseases, and medications known to interfere with autonomic function. We enrolled participants between April 29 th , 2020, and March 2 nd , 2021, and followed them for a median of 73 days (range, 3-253 days). Participants provided patient-reported outcome measures through a custom smartphone application and wore an Apple Watch, collecting heart rate variability and heart rate data, throughout the follow-up period. Exposure Participants were exposed to SARS-CoV-2 infection over time due to ongoing community spread. Main Outcome and Measure The primary outcome was SARS-CoV-2 infection, defined as ±7 days from a self-reported positive SARS-CoV-2 nasal PCR test. Results We enrolled 407 participants with 49 (12%) having a positive SARS-CoV-2 test during follow-up. We examined five machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable 10-CV performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC)=85% (Confidence Interval 83-88%). The model was calibrated to improve sensitivity over specificity, achieving an average sensitivity of 76% (CI ±∼4%) and specificity of 84% (CI ±∼0.4%). The most important predictors included parameters describing the circadian HRV mean (MESOR) and peak-timing (acrophase), and age. Conclusions and Relevance We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV2 infection. Utilizing physiological metrics from wearable devices may improve screening methods and infection tracking.

Neonatal outcomes during the COVID-19 pandemic in New York City (preprint)

ABSTRACT We explored rates of premature births and neonatal intensive care unit (NICU) admissions at the Mount Sinai Hospital after the implementation of COVID-19 lockdown measures (March 16, 2020) and phase one reopening (June 8, 2020), comparing them to those of the same time periods from 2012-2019. Mount Sinai Hospital is in New York City (NYC), an early epicenter of COVID-19 in the United States, which was heavily impacted by the pandemic during the study period. Among 43,963 singleton births, we observed no difference in either outcome after the implementation of lockdown measures when compared to the same trends in prior years (p=0.09-0.35). Of interest, we observed a statistically significant decrease in premature births after NYC phase one reopening compared to those of the same time period in 2012-2019 across all time windows (p=0.0028-0.049), and a statistically significant decrease in NICU admissions over the largest time window (2.75 months) compared to prior years (p=0.0011).

Longitudinal Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis (preprint)

Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms. Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study App which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Surveys assessing infection and symptom related questions were obtained daily. Findings: Using a mixed-effect COSINOR model the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (p=0.01). Significant changes in the mean MESOR and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19 related symptom compared to all other symptom free days (p=0.01). Interpretation: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.

Intestinal inflammation modulates the expression of ACE2 and TMPRSS2 and potentially overlaps with the pathogenesis of SARS-CoV-2 related disease (preprint)

Immune dysregulation and cytokine release syndrome have emerged as pathological hallmarks of severe Coronavirus Disease 2019 (COVID-19), leading to the evaluation of cytokine antagonists as therapeutic agents. A number of immune-directed therapies being considered for COVID-19 patients are already in clinical use in chronic inflammatory conditions like inflammatory bowel disease (IBD). These considerations led us to systematically examine the intersections between COVID-19 and the GI tract during health and intestinal inflammation. We have observed that IBD medications, both biologic and non-biologic, do not significantly impact ACE2 and TMPRSS2 expression in the uninflamed intestines. Additionally, by comparing SARS CoV2-induced epithelial gene signatures with IBD-associated genes, we have identified a shared molecular subnetwork between COVID-19 and IBD. These data generate a novel appreciation of the confluence of COVID-19- and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19.