Using electronic health records to understand COVID-19 risks

On December 31, 2019, a new disease, which would in due time would come to be identified as COVID-19, was reported to the World Health Organization. During the two and a half years since the emergence of COVID-19 and the more than two years since the start of the COVID-19 pandemic, which is caused by infection of SARS-CoV-2, more than 500 million cases have been reported around the world with more than six million deaths attributed it with than 85 million cases and more than one million deaths from the United States of America. This novel disease has had profound economic, political, public health and social impact in the United States and around the world. Subsequent research, both concurrent and ongoing, throughout the pandemic has been necessary to identify population at risk of SARS-CoV-2 infection, severe disease, beneficial treatments, death and long-term complications. Clinical data, sourced from electronic health records, had been paramount to identifying these risks. The novelty of SARS-CoV-2 and COVID-19 brought uncertainty as to who was at risk of infection, who was at risk for death, how should patients be treated and what are the long-term effects. At the start of the pandemic, there was a focus on public health measures, such as proper hygiene, quarantining when sick and reducing close contacts. As the number of cases continued to rise and hospitals became inundated with patients, researchers set out to identify patients at risk for severe disease and death and to identify existing treatment options that may benefit patients who were hospitalized and suffering from severe disease. Clinical trials and on-going retrospective analysis of patients helped to identify beneficial treatments for patients as well as rule out treatments that were not beneficial or associated with negative outcomes. In one of our studies were identified patients who had a history of macular degeneration and coagulation disorders were at increased risk for severe disease and death as a result of COIVD-19 and identified variants in gene underpinning the inflammatory response as associated with altered risk. In another study using retrospective analysis, we utilized clinical data to identify patients who were intubation and investigated the effect of steroid hormone exposure on the survival of these patients. Our analysis indicated that exposure to melatonin between intubation and extubation was significantly associated with survival in COVID-19 patients and in mechanically ventilated COVID-19 patients. This association was observed when accounting for patient demographics and previous clinical history. As multiple vaccines have been developed and distributed and therapeutics have become widely available, surges in case counts have not been associated with a proportional rise in hospitalizations and death. Research has shifted to trying to understand the long-term impact of COVID-19 on the health of patients. While viral infections are not uncommon, some can have lasting impacts on patients. With more than 500 million cases reported worldwide long-term analysis of COVID-19 patients and their health after COVID-19 will remain important. Additionally, the incomplete success of vaccination campaigns also highlights the need to monitor any future endemic spikes. While clinical data has been important for conducting studies, they are incomplete and lead to challenges as we transition to an endemic state. To that end, we trained a random forest classifier to assign a probability of a patient having had COVID-19 during each of their visits and utilized these probabilities to identify clinical phenotypes that are associated with patients who had COVID-19. Within one year, our analysis identified myocardial infarction, urinary tract infection, type 2 diabetes and acute renal failure as being associated with higher probabilities of COVID-19. The projects presented here demonstrate how to use electronic health records to identify patients at risk for severe disease and death, monitor drug exposure and evaluate its effect on survival of patients with severe COVID-19, how to use machine learning to circumvent the limitations of using clinical data and sets a foundation for further work in identifying the effects of COVID-19. Moreover, these projects also show methods that can be applied to any future emerging disease

Robust adaptive immune response against Babesia microti infection marked by low parasitemia in a murine model of sickle cell disease.

The intraerythrocytic parasite Babesia microti is the number 1 cause of transfusion-transmitted infection and can induce serious, often life-threatening complications in immunocompromised individuals including transfusion-dependent patients with sickle cell disease (SCD). Despite the existence of strong long-lasting immunological protection against a second infection in mouse models, little is known about the cell types or the kinetics of protective adaptive immunity mounted following Babesia infection, especially in infection-prone SCD that are thought to have an impaired immune system. Here, we show, using a mouse B microti infection model, that infected wild-type (WT) mice mount a very strong adaptive immune response, characterized by (1) coordinated induction of a robust germinal center (GC) reaction; (2) development of follicular helper T (TFH) cells that comprise ∼30% of splenic CD4+ T cells at peak expansion by 10 days postinfection; and (3) high levels of effector T-cell cytokines, including interleukin 21 and interferon γ, with an increase in the secretion of antigen (Ag)-specific antibodies (Abs). Strikingly, the Townes SCD mouse model had significantly lower levels of parasitemia. Despite a highly disorganized splenic architecture before infection, these mice elicited a surprisingly robust adaptive immune response (including comparable levels of GC B cells, TFH cells, and effector cytokines as control and sickle trait mice), but higher immunoglobulin G responses against 2 Babesia-specific proteins, which may contain potential immunogenic epitopes. Together, these studies establish the robust emergence of adaptive immunity to Babesia even in immunologically compromised SCD mice. Identification of potentially immunogenic epitopes has implications to identify long-term carriers, and aid Ag-specific vaccine development. © 2018 by The American Society of Hematology

Selective deployment of transcription factor paralogs with submaximal strength facilitates gene regulation in the immune system

In multicellular organisms, duplicated genes can diverge through tissue-specific gene expression patterns, as exemplified by highly regulated expression of Runx transcription factor paralogs with apparent functional redundancy. Here we asked what cell type-specific biologies might be supported by the selective expression of Runx paralogs during Langerhans cell and inducible regulatory T cell differentiation. We uncovered functional non-equivalence between Runx paralogs. Selective expression of native paralogs allowed integration of transcription factor activity with extrinsic signals, while non-native paralogs enforced differentiation even in the absence of exogenous inducers. DNA-binding affinity was controlled by divergent amino acids within the otherwise highly conserved RUNT domain, and evolutionary reconstruction suggested convergence of RUNT domain residues towards sub-maximal strength. Hence, the selective expression of gene duplicates in specialized cell types can synergize with the acquisition of functional differences to enable appropriate gene expression, lineage choice and differentiation in the mammalian immune system

Using machine learning probabilities to identify effects of COVID-19

<p>These notebooks accompany the journal article published is <i>Patterns </i>titled Using machine learning probabilities to identify effects of COVID-19 by Vijendra Ramlall, Undina Gisladottir, Jenna Kefeli, Yutaro Tanaka, Benjamin May, and Nicholas Tatonetti. </p><p>Summary:</p><p>COVID-19, the disease caused by the SARS-CoV-2 virus, has had extensive economic, social and public health impacts in the United States and around the world. To date, there have been more than 600 million reported infections worldwide with more than 6 million reported deaths. Retrospective analysis, which identified comorbidities, risk factors and treatments, has underpinned the response. As the situation transitions to an endemic, retrospective analyses using electronic health records will be important to identify long-term effects of COVID-19. However, these analyses can be complicated by incomplete records, which makes it difficult to differentiate visits where the patient had COVID-19. To address this, we trained a random forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each. Using these probabilities, we found that higher COVID-19 probabilities were associated with future diagnosis of myocardial infarction, urinary tract infection, acute renal failure and type 2 diabetes. </p&gt

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies