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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

Background Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters. Objective To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making. Design, setting, and participants We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays. Outcome measurements and statistical analysis We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA). Results and limitations ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p = 2e-09) and with BCR in the TCGA validation cohort (Cox regression: p = 3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p = 0.001; DoD, training cohort) and for GG ≥ 3, pathological stage ≥T3, and resection state (p = 0.037; BCR, validation cohort). Conclusions ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP. Patient summary ProstaTrend provides molecular patient risk stratification after radical prostatectomy.

Objectives The pathology of structural valvular heart disease (sVHD) ranges from basic diseases of rheumatologic origin to chronic degenerative remodeling processes after acute bacterial infections. Molecular genetic methods allow detection of the complete microbial spectrum in heart valve tissues independent of microbiological cultivation. In particular, whole-metagenome analysis is a sensitive and highly specific analytical method that allows a deeper insight into the pathogenicity of the diseases. In the present study we assessed the pathogen spectrum in heart valve tissue from 25 sVHD patients using molecular and microbiological methods. Methods Twenty-five sVHD patients were selected randomly from an observational cohort study (March 2016 to January 2017). The explanted native heart valves were examined using microbiological methods and immunohistological structural analysis. In addition, the bacterial metagenome of the heart valve tissue was determined using next-generation sequencing. Results The use of sonication as a pretreatment of valve tissue from 4 sVHD patients permitted successful detection of Clostridium difficile, Enterococcus faecalis, Staphylococcus saccharolyticus, and Staphylococcus haemolyticus using microbial cultivation. Histological staining revealed intramural localization. Metagenome analysis identified a higher rate of bacterial infiltration in 52% of cases. The pathogen spectrum included both gram-positive and gram-negative bacteria. Conclusions Microbiological and molecular biological studies are necessary to detect the spectrum of bacteria in a calcified heart valve. Metagenome analysis is a valid method to gain new insight into the polymicrobial pathophysiology of sVHD. Our results suggest that an undetected proportion of sVHD might be triggered by chronic inflammation or influenced by secondary bacterial infiltration.

measured using RT-qPCR. Results from cohort A were used to develop a model with (combinations of) the four genes based on the comparative CT method, and the model was validated in the independent cohort B. RESULTS: The HOXC6-DLX1 score model resulted in the highest area-under-the-curve (AUC) of 0.75 (95% confidence interval: 0.70-0.80) for the diagnosis of high-grade PCa upon prostate biopsy, with a specificity of 36% and 91% sensitivity. The HOXC6-DLX1 score outperformed PCA3 in both cohorts (cohort A: AUC 0.75 vs. 0.65; cohort B: 0.73 vs. 0.62). Furthermore, the HOXC6-DLX1 score was significantly correlated with the Gleason score upon biopsy. Subgroup analysis confirmed the added value of this urine test for the prediction of high-grade PCa in patients with low serum PSA values (

The poultry red mite (PRM) Dermanyssus gallinae causes high economic losses and is among the most important parasites in poultry farming worldwide. Different chemical, physical, and biological strategies try to control the expansion of PRM. However, effective solutions to this problem still have to be found. Here, we present a method for the development of an immunological control strategy, based on the identification of mite protein antigens which elicit antibodies with anti-mite activity in the immunized chicken. Hens were immunized with different PRM protein extracts formulated with two different adjuvants, and IgY-antibodies were isolated from the eggs. A PRM in vitro feeding assay which used chicken blood spiked with these IgY-preparations was used to detect antibodies which caused PRM mortality. In vitro feeding of mites with IgY isolated from hens immunized with PRM extract formulated with one of the adjuvants showed a statistically significant increase in the mortality as compared to control mites. After the separation of total PRM extracts in two-dimensional gels, several protein spots were recognized by such IgY preparations. Ten protein spots were subjected to mass spectrometry (MS/MS) for the identification of the corresponding proteins. Complete protein sequences were deduced from genomic and transcriptomic assemblies derived from high throughput sequencing of total PRM DNA and RNA. The results may contribute to the development of an immunological control strategy of D. gallinae.

Traditionally fMRI data analysis aims at identifying brain areas in which the amplitude of the BOLD signal responds to experimental stimulations. However, since the brain acts as a network, we would expect differential effects on network topology. Therefore, the target of statistical inference should not only be individual voxels or brain areas but rather network connections. Here we introduce a new approach to correlation-based statistics in fMRI. At the heart of our approach is the concept of correlation bundles as a functional analogy to anatomical fibre bundles. Statistical tests are applied to these bundles using large-scale inference methods such as FDR. We call this approach correlation bundle statistics (CBS). In contrast to previous correlation-based approaches to fMRI statistics, CBS does not require a presegmentation or smoothing of the data so that anatomical specificity is preserved. The result of a CBS analysis is not a set of voxels or brain regions but rather a set of correlation bundles that are found to be significantly affected by some experimental manipulation.

Background: High-throughput sequencing technologies are improving in quality, capacity and costs, providing versatile applications in DNA and RNA research. For small genomes or fraction of larger genomes, DNA samples can be mixed and loaded together on the same sequencing track. This so-called multiplexing approach relies on a specific DNA tag or barcode that is attached to the sequencing or amplification primer and hence appears at the beginning of the sequence in every read. After sequencing, each sample read is identified on the basis of the respective barcode sequence.Alterations of DNA barcodes during synthesis, primer ligation, DNA amplification, or sequencing may lead to incorrect sample identification unless the error is revealed and corrected. This can be accomplished by implementing error correcting algorithms and codes. This barcoding strategy increases the total number of correctly identified samples, thus improving overall sequencing efficiency. Two popular sets of error-correcting codes are Hamming codes and Levenshtein codes.Result: Levenshtein codes operate only on words of known length. Since a DNA sequence with an embedded barcode is essentially one continuous long word, application of the classical Levenshtein algorithm is problematic. In this paper we demonstrate the decreased error correction capability of Levenshtein codes in a DNA context and suggest an adaptation of Levenshtein codes that is proven of efficiently correcting nucleotide errors in DNA sequences. In our adaption we take the DNA context into account and redefine the word length whenever an insertion or deletion is revealed. In simulations we show the superior error correction capability of the new method compared to traditional Levenshtein and Hamming based codes in the presence of multiple errors.Conclusion: We present an adaptation of Levenshtein codes to DNA contexts capable of correction of a pre-defined number of insertion, deletion, and substitution mutations. Our improved method is additionally capable of recovering the new length of the corrupted codeword and of correcting on average more random mutations than traditional Levenshtein or Hamming codes.As part of this work we prepared software for the flexible generation of DNA codes based on our new approach. To adapt codes to specific experimental conditions, the user can customize sequence filtering, the number of correctable mutations and barcode length for highest performance.