Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and multiple organ involvement in individuals under 21 years following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To identify genes, pathways and cell types driving MIS-C, we sequenced the blood transcriptomes of MIS-C cases, pediatric cases of coronavirus disease 2019, and healthy controls. We define a MIS-C transcriptional signature partially shared with the transcriptional response to SARS-CoV-2 infection and with the signature of Kawasaki disease, a clinically similar condition. By projecting the MIS-C signature onto a co-expression network, we identified disease gene modules and found genes downregulated in MIS-C clustered in a module enriched for the transcriptional signatures of exhausted CD8+ T-cells and CD56dimCD57+ NK cells. Bayesian network analyses revealed nine key regulators of this module, including TBX21, a central coordinator of exhausted CD8+ T-cell differentiation. Together, these findings suggest dysregulated cytotoxic lymphocyte response to SARS-Cov-2 infection in MIS-C.

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

The presence of gastrointestinal symptoms and high levels of viral RNA in the stool suggest active Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) replication within enterocytes. Here, in multiple, large cohorts of patients with inflammatory bowel disease (IBD), we have studied the intersections between Coronavirus Disease 2019 (COVID-19), intestinal inflammation and IBD treatment. A striking expression of ACE2 on the small bowel enterocyte brush border supports intestinal infectivity by SARS-CoV-2. Commonly used IBD medications, both biologic and non-biologic, do not significantly impact ACE2 and TMPRSS2 receptor expression in the uninflamed intestines. Additionally, we have defined molecular responses to COVID-19 infection that are also enriched in IBD, pointing to shared molecular networks 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.

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions. One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

Introductions and early spread of SARS-CoV-2 in the New York City area

New York City (NYC) has emerged as one of the epicenters of the current SARS-CoV2 pandemic. To identify the early events underlying the rapid spread of the virus in the NYC metropolitan area, we sequenced the virus causing COVID19 in patients seeking care at the Mount Sinai Health System. Phylogenetic analysis of 84 distinct SARS-CoV2 genomes indicates multiple, independent but isolated introductions mainly from Europe and other parts of the United States. Moreover, we find evidence for community transmission of SARS-CoV2 as suggested by clusters of related viruses found in patients living in different neighborhoods of the city.