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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.

The chromosomal regions containing the two putative tumour suppressors, fragile histidine triad gene (FHIT) and tumour suppressor gene 101 (TSG101), are deleted frequently in thyroid tumours. We therefore analysed FHIT and TSG101 transcripts in a group of advanced thyroid tumours to establish their role in thyroid tumorigenesis.Retrospective analysis of FHIT and TSG101 mRNA transcripts and genomic DNA from cryo-preserved thyroid tumours. TP53, previously shown at the genomic level not to be mutated in this cohort of tumours, served as a control.We analysed nine follicular thyroid carcinomas (FTC), six papillary thyroid carcinomas and six follicular adenomas (FA) and histologically normal thyroid tissue from four of the FA patients.Single stage and nested reverse transcription polymerase chain reaction (RT-PCR) products of FHIT, TSG101, and TP53 were analysed by agarose or polyacrylamide gel electrophoresis and sequenced. Genomic DNA was also analysed by polymerase chain reaction and sequencing (FHIT) or by Southern blotting (TSG101). Clinical data were correlated with the results of the mutation analysis.Truncated FHIT transcripts were observed frequently alongside full length transcripts with nested RT-PCR, most often in FTC, while single stage RT-PCR revealed only normal length transcripts in all tumours. Similar results were obtained for TP53, while abnormal TSG101 transcripts were detectable by single stage RT-PCR. Sequence analysis of the truncated FHIT and TSG101 transcripts revealed mainly exon skipping and alternate RNA processing events. Only a single point mutation (of TSG101) was found. Southern blotting for the TSG101 gene, and PCR amplification and sequencing of the FHIT gene showed no evidence of genomic abnormalities in either case, and there was no evidence of splice site mutations in the FHIT gene, suggesting that the truncated transcripts result from altered RNA processing. There was no relationship between tumour stage, grade or survival and the presence of FHIT or TSG101 abnormalities.Truncated FHIT and TSG101 transcripts in thyroid tumours reflect alternate mRNA splicing events, rather than genomic deletions. Such abnormal RNA processing seems to be common and widespread in thyroid neoplasms, as similar results were obtained by analysis of transcripts of TP53, which we had previously shown not to be mutated in these specimens. Although a pathogenetic role for these aberrant transcripts remains possible, no correlation was found with stage, histological grade or outcome in this small group of advanced thyroid malignancies. Relaxation of mRNA splice control appears to be a feature of follicular cell-derived thyroid neoplasms.