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ObjectivesTo investigate whether the risk of developing an incident autoimmune disease is increased in patients with previous COVID-19 disease compared to people without COVID-19.MethodA cohort was selected from German routine health care data covering 38.9 million individuals. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through December 31, 2020. Patients were matched 1:3 to control patients without COVID-19. Both groups were followed up until June 30, 2021. We used the four quarters preceding the index date until the end of follow-up to analyze the onset of autoimmune diseases during the post-acute period. Incidence rates (IR) per 1000 person-years were calculated for each outcome and patient group. Poisson models were deployed to estimate the incidence rate ratios (IRRs) of developing an autoimmune disease conditional on a preceding diagnosis of COVID-19.ResultsIn total, 641,704 patients with COVID-19 were included. Comparing the incidence rates in the COVID-19 (IR=15.05, 95% CI: 14.69-15.42) and matched control groups (IR=10.55, 95% CI: 10.25-10.86), we found a 42.63% higher likelihood of acquiring autoimmunity for patients who had suffered from COVID-19. This estimate was similar for common autoimmune diseases, such as Hashimoto thyroiditis, rheumatoid arthritis, or Sjögren syndrome. The highest IRR was observed for autoimmune disease of the vasculitis group. Patients with a more severe course of COVID-19 were at a greater risk for incident autoimmune diseases.ConclusionsSARS-CoV-2 infection is associated with an increased risk of developing new-onset autoimmune diseases after the acute phase of infection.

SummaryThe annotation of microRNAs, an important class of post-transcriptional regulators, depends on the availability of transcriptomics data and expert knowledge. This led to a large gap between novel genomes made available and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in MirMachine, our new tool for the annotation of conserved microRNA complements from genomes only. We successfully applied MirMachine to a wide range of animal species, including those with very large genomes, additional genome duplications and extinct species, where smallRNA sequencing will be hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field facilitating automated genome annotation pipelines and deeper studies on the evolution of genome regulation, even in extinct organisms.HighlightsAn annotation pipeline using trained covariance models of microRNA familiesEnables massive parallel annotation of microRNA complements of genomesMirMachine creates meaningful annotations for very large and extinct genomesmicroRNA score to assess genome assembly completenessGraphical abstract