Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine.

The annotation of microRNAs depends on the availability of transcriptomics data and expert knowledge. This has led to a gap between the availability of novel genomes 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 our tool MirMachine, which annotates conserved microRNAs within genomes. We successfully applied MirMachine to a range of animal species, including those with large genomes and genome duplications and extinct species, where small RNA sequencing is 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 by facilitating automated genome annotation pipelines and deeper studies into the evolution of genome regulation, even in extinct organisms.

Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study.

OBJECTIVES: To investigate whether the risk of developing an incident autoimmune disease is increased in patients with prior COVID-19 disease compared to those without COVID-19, a large cohort study was conducted. METHOD: A cohort was selected from German routine health care data. 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. RESULTS: In 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 diseases of the vasculitis group. Patients with a more severe course of COVID-19 were at a greater risk for incident autoimmune disease. CONCLUSIONS: SARS-CoV-2 infection is associated with an increased risk of developing new-onset autoimmune diseases after the acute phase of infection. Key Points • In the 3 to 15 months after acute infection, patients who had suffered from COVID-19 had a 43% (95% CI: 37-48%) higher likelihood of developing a first-onset autoimmune disease, meaning an absolute increase in incidence of 4.50 per 1000 person-years over the control group. • COVID-19 showed the strongest association with vascular autoimmune diseases.

Selection and Validation of siRNAs Preventing Uptake and Replication of SARS-CoV-2.

In 2019, the novel highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak rapidly led to a global pandemic with more than 346 million confirmed cases worldwide, resulting in 5.5 million associated deaths (January 2022). Entry of all SARS-CoV-2 variants is mediated by the cellular angisin-converting enzyme 2 (ACE2). The virus abundantly replicates in the epithelia of the upper respiratory tract. Beyond vaccines for immunization, there is an imminent need for novel treatment options in COVID-19 patients. So far, only a few drugs have found their way into the clinics, often with modest success. Specific gene silencing based on small interfering RNA (siRNA) has emerged as a promising strategy for therapeutic intervention, preventing/limiting SARS-CoV-2 entry into host cells or interfering with viral replication. Here, we pursued both strategies. We designed and screened nine siRNAs (siA1-9) targeting the viral entry receptor ACE2. SiA1, (siRNA against exon1 of ACE2 mRNA) was most efficient, with up to 90% knockdown of the ACE2 mRNA and protein for at least six days. In vitro, siA1 application was found to protect Vero E6 and Huh-7 cells from infection with SARS-CoV-2 with an up to ∼92% reduction of the viral burden indicating that the treatment targets both the endosomal and the viral entry at the cytoplasmic membrane. Since the RNA-encoded genome makes SARS-CoV-2 vulnerable to RNA interference (RNAi), we designed and analysed eight siRNAs (siV1-8) directly targeting the Orf1a/b region of the SARS-CoV-2 RNA genome, encoding for non-structural proteins (nsp). As a significant hallmark of this study, we identified siV1 (siRNA against leader protein of SARS-CoV-2), which targets the nsp1-encoding sequence (a.k.a. 'host shutoff factor') as particularly efficient. SiV1 inhibited SARS-CoV-2 replication in Vero E6 or Huh-7 cells by more than 99% or 97%, respectively. It neither led to toxic effects nor induced type I or III interferon production. Of note, sequence analyses revealed the target sequence of siV1 to be highly conserved in SARS-CoV-2 variants. Thus, our results identify the direct targeting of the viral RNA genome (ORF1a/b) by siRNAs as highly efficient and introduce siV1 as a particularly promising drug candidate for therapeutic intervention.

ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer.

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.

Master and servant: LINC00152 - a STAT3-induced long noncoding RNA regulates STAT3 in a positive feedback in human multiple myeloma.

BACKGROUND: The survival of INA-6 human multiple myeloma cells is strictly dependent upon the Interleukin-6-activated transcription factor STAT3. Although transcriptional analyses have revealed many genes regulated by STAT3, to date no protein-coding STAT3 target gene is known to mediate survival in INA-6 cells. Therefore, the aim here was to identify and analyze non-protein-coding STAT3 target genes. In addition to the oncogenic microRNA-21, we previously described five long noncoding RNAs (lncRNAs) induced by STAT3, named STAiRs. Here, we focus on STAT3-induced RNA 18 (STAiR18), an mRNA-like, long ncRNA that is duplicated in the human lineage. One STAiR18 locus is annotated as the already well described LINC00152/CYTOR, however, the other harbors the MIR4435-2HG gene and is, up to now, barely described. METHODS: CAPTURE-RNA-sequencing was used to analyze STAiR18 transcript architecture. To identify the STAiR18 and STAT3 phenotype, siRNA-based knockdowns were performed and microarrays were applied to identify their target genes. RNA-binding partners of STAiR18 were determined by Chromatin-Isolation-by-RNA-Purification (ChIRP) and subsequent sequencing. STAT3 expression in dependence of STAiR18 was investigated by immunoblots, chromatin- and RNA-immunoprecipitations. RESULTS: As identified by CAPTURE-RNA sequencing, a complex splice pattern originates from both STAiR18 loci, generating different transcripts. Knockdown of the most abundant STAiR18 isoforms dramatically decreased INA-6 cell vitality, suggesting a functional role in myeloma cells. Additionally, STAiR18 and STAT3 knockdowns yielded overlapping changes of transcription patterns in INA-6 cells, suggesting a close functional interplay between the two factors. Moreover, Chromatin isolation by RNA purification (ChIRP), followed by genome-wide RNA sequencing showed that STAiR18 associates specifically with the STAT3 primary transcript. Furthermore, the knockdown of STAiR18 reduced STAT3 levels on both the RNA and protein levels, suggesting a positive feedback between both molecules. Furthermore, STAiR18 knockdown changes the histone methylation status of the STAT3 locus, which explains the positive feedback and indicates that STAiR18 is an epigenetic modulator. CONCLUSION: Hence, STAiR18 is an important regulator of myeloma cell survival and is strongly associated with the oncogenic function of STAT3. The close functional interplay between STAT3 and STAiR18 suggests a novel principle of regulatory interactions between long ncRNAs and signaling pathways.

Bacterial infiltration in structural heart valve disease.

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.

STAT3-induced long noncoding RNAs in multiple myeloma cells display different properties in cancer.

Interleukin-6 (IL-6)-activated Signal Transducer and Activator of Transcription 3 (STAT3) facilitates survival in the multiple myeloma cell line INA-6 and therefore represents an oncogenic key player. However, the biological mechanisms are still not fully understood. In previous studies we identified microRNA-21 as a STAT3 target gene with strong anti-apoptotic potential, suggesting that noncoding RNAs have an impact on the pathogenesis of human multiple myeloma. Here, we describe five long noncoding RNAs (lncRNAs) induced by IL-6-activated STAT3, which we named STAiRs. While STAiRs 1, 2 and 6 remain unprocessed in the nucleus and show myeloma-specific expression, STAiRs 15 and 18 are spliced and broadly expressed. Especially STAiR2 and STAiR18 are promising candidates. STAiR2 originates from the first intron of a tumor suppressor gene. Our data support a mutually exclusive expression of either STAiR2 or the functional tumor suppressor in INA-6 cells and thus a contribution of STAiR2 to tumorigenesis. Furthermore, STAiR18 was shown to be overexpressed in every tested tumor entity, indicating its global role in tumor pathogenesis. Taken together, our study reveals a number of STAT3-induced lncRNAs suggesting that the interplay between the coding and noncoding worlds represents a fundamental principle of STAT3-driven cancer development in multiple myeloma and beyond.

New insights into valve-related intramural and intracellular bacterial diversity in infective endocarditis.

AIMS: In infective endocarditis (IE), a severe inflammatory disease of the endocardium with an unchanged incidence and mortality rate over the past decades, only 1% of the cases have been described as polymicrobial infections based on microbiological approaches. The aim of this study was to identify potential biodiversity of bacterial species from infected native and prosthetic valves. Furthermore, we compared the ultrastructural micro-environments to detect the localization and distribution patterns of pathogens in IE. MATERIAL AND METHODS: Using next-generation sequencing (NGS) of 16S rDNA, which allows analysis of the entire bacterial community within a single sample, we investigated the biodiversity of infectious bacterial species from resected native and prosthetic valves in a clinical cohort of 8 IE patients. Furthermore, we investigated the ultrastructural infected valve micro-environment by focused ion beam scanning electron microscopy (FIB-SEM). RESULTS: Biodiversity was detected in 7 of 8 resected heart valves. This comprised 13 bacterial genera and 16 species. In addition to 11 pathogens already described as being IE related, 5 bacterial species were identified as having a novel association. In contrast, valve and blood culture-based diagnosis revealed only 4 species from 3 bacterial genera and did not show any relevant antibiotic resistance. The antibiotics chosen on this basis for treatment, however, did not cover the bacterial spectra identified by our amplicon sequencing analysis in 4 of 8 cases. In addition to intramural distribution patterns of infective bacteria, intracellular localization with evidence of bacterial immune escape mechanisms was identified. CONCLUSION: The high frequency of polymicrobial infections, pathogen diversity, and intracellular persistence of common IE-causing bacteria may provide clues to help explain the persistent and devastating mortality rate observed for IE. Improved bacterial diagnosis by 16S rDNA NGS that increases the ability to tailor antibiotic therapy may result in improved outcomes.

DNABarcodes: an R package for the systematic construction of DNA sample tags.

Motivation: DNA barcodes are commonly used for counting and discriminating purposes in molecular and cell biology. Not every set of DNA sequences is equally suitable for this goal. There is a growing demand for more sophisticated barcode designs, with only few tools available. We prepared an R package that combines known algorithms and innovative methods for the efficient, flexible and near-optimal generation of robust barcode sets. Results: Our R-software package 'DNABarcodes' generates sets of DNA barcodes from a few basic input parameters (e.g. length, distance metric, minimum distance, chemical properties). It satisfies the specifics of most particular experimental demands in de novo design of barcodes. Additionally, the package allows analysing existing sets of DNA barcodes as well as the generation of subsets of those existing sets to improve their error correction and detection properties. 'DNABarcodes' was designed for speed, versatility, provable correctness and large set sizes. Availability and Implementation: The DNABarcodes R package is available from Bioconductor at http://bioconductor.org/packages/DNABarcodes under the GPL-2 license. Contact: tilo.buschmann@izi.fraunhofer.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

A method to identify protein antigens of Dermanyssus gallinae for the protection of birds from poultry mites.

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.

Enhancing the detection of barcoded reads in high throughput DNA sequencing data by controlling the false discovery rate.

BACKGROUND: DNA barcodes are short unique sequences used to label DNA or RNA-derived samples in multiplexed deep sequencing experiments. During the demultiplexing step, barcodes must be detected and their position identified. In some cases (e.g., with PacBio SMRT), the position of the barcode and DNA context is not well defined. Many reads start inside the genomic insert so that adjacent primers might be missed. The matter is further complicated by coincidental similarities between barcode sequences and reference DNA. Therefore, a robust strategy is required in order to detect barcoded reads and avoid a large number of false positives or negatives.For mass inference problems such as this one, false discovery rate (FDR) methods are powerful and balanced solutions. Since existing FDR methods cannot be applied to this particular problem, we present an adapted FDR method that is suitable for the detection of barcoded reads as well as suggest possible improvements. RESULTS: In our analysis, barcode sequences showed high rates of coincidental similarities with the Mus musculus reference DNA. This problem became more acute when the length of the barcode sequence decreased and the number of barcodes in the set increased. The method presented in this paper controls the tail area-based false discovery rate to distinguish between barcoded and unbarcoded reads. This method helps to establish the highest acceptable minimal distance between reads and barcode sequences. In a proof of concept experiment we correctly detected barcodes in 83% of the reads with a precision of 89%. Sensitivity improved to 99% at 99% precision when the adjacent primer sequence was incorporated in the analysis. The analysis was further improved using a paired end strategy. Following an analysis of the data for sequence variants induced in the Atp1a1 gene of C57BL/6 murine melanocytes by ultraviolet light and conferring resistance to ouabain, we found no evidence of cross-contamination of DNA material between samples. CONCLUSION: Our method offers a proper quantitative treatment of the problem of detecting barcoded reads in a noisy sequencing environment. It is based on the false discovery rate statistics that allows a proper trade-off between sensitivity and precision to be chosen.

Deficient approaches to human neuroimaging.

Functional magnetic resonance imaging (fMRI) is the workhorse of imaging-based human cognitive neuroscience. The use of fMRI is ever-increasing; within the last 4 years more fMRI studies have been published than in the previous 17 years. This large body of research has mainly focused on the functional localization of condition- or stimulus-dependent changes in the blood-oxygenation-level dependent signal. In recent years, however, many aspects of the commonly practiced analysis frameworks and methodologies have been critically reassessed. Here we summarize these critiques, providing an overview of the major conceptual and practical deficiencies in widely used brain-mapping approaches, and exemplify some of these issues by the use of imaging data and simulations. In particular, we discuss the inherent pitfalls and shortcomings of methodologies for statistical parametric mapping. Our critique emphasizes recent reports of excessively high numbers of both false positive and false negative findings in fMRI brain mapping. We outline our view regarding the broader scientific implications of these methodological considerations and briefly discuss possible solutions.

Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping.

Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a non-parametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Levenshtein error-correcting barcodes for multiplexed DNA sequencing.

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.