Search

COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile.A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study.Eric and Wendy Schmidt.

Polymerase chain reaction (PCR)-based loss of heterozygosity (LOH) studies of archival formalin-fixed, paraffin-embedded (FFPE) tumor tissues have become an important tool in the search for tumor suppressor genes and oncogenes and are also used increasingly in clinical practice. However, FFPE tissue samples may contain little amplifiable DNA, resulting in frequent reaction failures and unreliable LOH data. Using pairs of serial dilutions of reference DNA, we determined the minimum amplifiable DNA concentration necessary for reliable microsatellite-PCR LOH analysis. We then measured the amplifiable DNA content of a selection of frozen and FFPE-derived tumor specimens by real-time quantitative PCR. A minimum input of 600 pg of 100% amplifiable DNA per PCR was required for reliable LOH analysis. While the total DNA concentrations of all samples exceeded this figure, most FFPE-sample-derived DNA was non-amplifiable, with ratios of actually amplifiable DNA to total DNA as low as 1 to 3625. Many FFPE samples therefore contained substantially less than 600 pg/microl of actually amplifiable DNA, making them potentially unsuitable for LOH studies. Real-time quantitative PCR before LOH studies of FFPE tissues allows: identification of samples, which will fail microsatellite-PCR; exclusion of samples, which will yield unreliable results; and optimal adjustment of template input for the remainder. Amplification reactions undertaken without this precaution can result in unreliable LOH data.