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There is increasing evidence that coronavirus disease 2019 (COVID-19) produces more severe symptoms and higher mortality among men than among women1-5. However, whether immune responses against severe acute respiratory syndrome coronavirus (SARS-CoV-2) differ between sexes, and whether such differences correlate with the sex difference in the disease course of COVID-19, is currently unknown. Here we examined sex differences in viral loads, SARS-CoV-2-specific antibody titres, plasma cytokines and blood-cell phenotyping in patients with moderate COVID-19 who had not received immunomodulatory medications. Male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. By contrast, female patients had more robust T cell activation than male patients during SARS-CoV-2 infection. Notably, we found that a poor T cell response negatively correlated with patients' age and was associated with worse disease outcome in male patients, but not in female patients. By contrast, higher levels of innate immune cytokines were associated with worse disease progression in female patients, but not in male patients. These findings provide a possible explanation for the observed sex biases in COVID-19, and provide an important basis for the development of a sex-based approach to the treatment and care of male and female patients with COVID-19.

Infection with SARS-CoV-2 results in a broad spectrum of immunological and clinical outcomes for patients with COVID-19. While dysregulated innate immunity has been extensively explored in severe disease, autoantibody responses and their impact on disease trajectories remain uncharted. Here, we enrolled a cohort of 194 SARS-CoV-2 infected patients and uninfected individuals, and employed a high-throughput autoantibody discovery platform, Rapid Extracellular Antigen Profiling (REAP), to assess the prevalence of autoantibodies against 2,770 extracellular and secreted proteins in COVID-19 disease. We found that COVID-19 patients harbor dramatically expanded autoantibody repertoires compared to uninfected controls. We further established that immunomodulatory proteins including cytokines, chemokines, complements, and cell surface receptors, are frequent targets of autoantibodies. Longitudinal analysis revealed the existence of both pre-existing autoantibodies, as well as a broad subset of autoantibodies induced upon SARS-CoV-2 infection. We uncovered distinct mechanisms by which immune-targeting autoantibodies perturb immunological functions, including interference of immunoreceptor signaling, depletion of circulating leukocytes, and modulation of antiviral antibody responses. Concordantly, murine surrogates of these autoantibodies hinder immune activation and exacerbate disease severity in a mouse model of SARS-CoV-2 infection. In summary, through the lens of an unbiased proteome-scale survey for autoantibody targets, these findings implicate humoral immunopathology as an integral aspect of COVID-19 pathogenesis with diverse impacts on immune functionality and clinical outcomes.

Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computational healthcare applications built on such a platform. Materials and Methods: A data science platform based on several open source technologies was deployed to support real-time, big data workloads. Data acquisition workflows for Apache Storm and NiFi were developed in Java and Python to capture patient monitoring and laboratory data for downstream analytics. Results: The use of emerging data management approaches along with open-source technologies such as Hadoop can be used to create integrated data lakes to store large, real-time data sets. This infrastructure also provides a robust analytics platform where healthcare and biomedical research data can be analyzed in near real-time for precision medicine and computational healthcare use cases. Discussion: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional data sets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Conclusion: Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to healthcare data for computational healthcare and precision medicine research., Comment: 8 pages, 4 figures, 1 table

The governance of data used for biomedical research and clinical trials is an important requirement for generating accurate results. To improve the visibility of data quality and analysis, we developed TrialChain, a blockchain-based platform that can be used to validate data integrity from large, biomedical research studies. We implemented a private blockchain using the MultiChain platform and integrated it with a data science platform deployed within a large research center. An administrative web application was built with Python to manage the platform, which was built with a microservice architecture using Docker. The TrialChain platform was integrated during data acquisition into our existing data science platform. Using NiFi, data were hashed and logged within the local blockchain infrastructure. To provide public validation, the local blockchain state was periodically synchronized to the public Ethereum network. The use of a combined private/public blockchain platform allows for both public validation of results while maintaining additional security and lower cost for blockchain transactions. Original data and modifications due to downstream analysis can be logged within TrialChain and data assets or results can be rapidly validated when needed using API calls to the platform. The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data. The platform provides cryptographic assurance of data authenticity and can also be used to document data analysis.