Search

For many research aspects on small non-coding RNAs, especially microRNAs, computational tools and databases are developed. This includes quantification of miRNAs, piRNAs, tRNAs and tRNA fragments, circRNAs and others. Furthermore, the prediction of new miRNAs, isomiRs, arm switch events, target and target pathway prediction and miRNA pathway enrichment are common tasks. Additionally, databases and resources containing expression profiles, e.g., from different tissues, organs or cell types, are generated. This information in turn leads to improved miRNA repositories. While most of the respective tools are implemented in a species-independent manner, we focused on tools for human small non-coding RNAs. This includes four aspects: (1) miRNA analysis tools (2) databases on miRNAs and variations thereof (3) databases on expression profiles (4) miRNA helper tools facilitating frequent tasks such as naming conversion or reporter assay design. Although dependencies between the tools exist and several tools are jointly used in studies, the interoperability is limited. We present HumiR, a joint web presence for our tools. HumiR facilitates an entry in the world of miRNA research, supports the selection of the right tool for a research task and represents the very first step towards a fully integrated knowledge-base for human small non-coding RNA research. We demonstrate the utility of HumiR by performing a very comprehensive analysis of Alzheimer&rsquo, s miRNAs.

Though SARS-CoV-2 primarily targets the respiratory system, it is increasingly appreciated that patients may suffer neurological symptoms of varied severity1–3. However, an unbiased understanding of the molecular processes across brain cell types that could contribute to these symptoms in COVID-19 patients is still missing. Here, we profile 47,678 droplet-based single-nucleus transcriptomes from the frontal cortex and choroid plexus across 10 non-viral, 4 COVID-19, and 1 influenza patient. We complement transcriptomic data with immunohistochemical staining for the presence of SARS-CoV-2. We find that all major cortex parenchymal and choroid plexus cell types are affected transcriptionally with COVID-19. This arises, in part, from SARS-CoV-2 infection of the cortical brain vasculature, meninges, and choroid plexus, stimulating increased inflammatory signaling into the brain. In parallel, peripheral immune cells infiltrate the brain, microglia activate programs mediating the phagocytosis of live neurons, and astrocytes dysregulate genes involved in neurotransmitter homeostasis. Among neurons, layer 2/3 excitatory neurons—evolutionarily expanded in humans4—show a specific downregulation of genes encoding major SNARE and synaptic vesicle components, predicting compromised synaptic transmission. These perturbations are not observed in terminal influenza. Many COVID-19 gene expression changes are shared with those in chronic brain disorders and reside in genetic variants associated with cognitive function, schizophrenia, and depression. Our findings and public dataset provide a molecular framework and new opportunities to understand COVID-19 related neurological disease.

BACKGROUND The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has instigated immediate and massive worldwide research efforts. Rapid publication of research data may be desirable but also carries the risk of quality loss. OBJECTIVE This analysis aimed to correlate the severity of the COVID-19 outbreak with its related scientific output per country. METHODS All articles related to the COVID-19 pandemic were retrieved from Web of Science and analyzed using the web application SciPE (science performance evaluation), allowing for large data scientometric analyses of the global geographical distribution of scientific output. RESULTS A total of 7185 publications, including 2592 articles, 2091 editorial materials, 2528 early access papers, 1479 letters, 633 reviews, and other contributions were extracted. The top 3 countries involved in COVID-19 research were the United States, China, and Italy. The confirmed COVID-19 cases or deaths per region correlated with scientific research output. The United States was most active in terms of collaborative efforts, sharing a significant amount of manuscript authorships with the United Kingdom, China, and Italy. The United States was China’s most frequent collaborative partner, followed by the United Kingdom. CONCLUSIONS The COVID-19 research landscape is rapidly developing and is driven by countries with a generally strong prepandemic research output but is also significantly affected by countries with a high prevalence of COVID-19 cases. Our findings indicate that the United States is leading international collaborative efforts. CLINICALTRIAL

RNA sequencing data sets rapidly increase in quantity. For microRNAs (miRNAs), frequently dozens to hundreds of billion reads are generated per study. The quantification of annotated miRNAs and the prediction of new miRNAs are leading computational tasks. Now, the increased depth of coverage allows to gain deeper insights into the variability of miRNAs. The analysis of isoforms of miRNAs (isomiRs) is a trending topic, and a range of computational tools for the analysis of isomiRs has been developed. We provide an overview on 27 available computational solutions for the analysis of isomiRs. These include both stand-alone programs (17 tools) and web-based solutions (10 tools) and span a publication time range from 2010 to 2020. Seven of the tools were published in 2019 and 2020, confirming the rising importance of the topic. While most of the analyzed tools work for a broad range of organisms or are completely independent of a reference organism, several tools have been tailored for the analysis of human miRNA data or for plants. While 14 of the tools are general analysis tools of miRNAs, and isomiR analysis is one of their features, the remaining 13 tools have specifically been developed for isomiR analysis. A direct comparison on 20 deep sequencing data sets for selected tools provides insights into the heterogeneity of results. With our work, we provide users a comprehensive overview on the landscape of isomiR analysis tools and in that support the selection of the most appropriate tool for their respective research task.

Chronic obstructive pulmonary disease (COPD) is associated with an increased risk of death, reducing life expectancy on average between 5 and 7 years. The survival time after diagnosis, however, varies considerably as a result of the heterogeneity of COPD. Therefore, markers that predict individual survival of COPD patients are of great value. We analyzed baseline molecular profiles and collected 54 months of follow-up data of the cohort study &ldquo, COPD and SYstemic consequences-COmorbidities NETwork&rdquo, (COSYCONET). Genome-wide microRNA signatures from whole blood collected at time of the inclusion in the study were generated for 533 COPD patients including patients that deceased during the 54-month follow-up period (n = 53) and patients that survived this period (n = 480). We identified two blood-born microRNAs (miR-150-5p and miR-320b) that were highly predictive for survival of COPD patients. The expression change was then confirmed by RT-qPCR in 245 individuals. Ninety percent of patients with highest expression of miR-150-5p survived the 54-month period in contrast to only 50% of patients with lowest expression intensity. Moreover, the abundance of the oncogenic miR-150-5p in blood of COPD patients was predictive for the development of cancer. Thus, molecular profiles measured at the time of a COPD diagnosis have a high predictive power for the survival of patients.

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinson’s diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinson’s Progression Marker Initiative (PPMI) and Luxembourg Parkinson’s Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.

The validation of microRNAs (miRNAs) identified by next generation sequencing involves amplification-free and hybridization-based detection of transcripts as criteria for confirming valid miRNAs. Since respective validation is frequently not performed, miRNA repositories likely still contain a substantial fraction of false positive candidates while true miRNAs are not stored in the repositories yet. Especially if downstream analyses are performed with these candidates (e.g. target or pathway prediction), the results may be misleading. In the present study, we evaluated 558 mature miRNAs from miRBase and 1,709 miRNA candidates from next generation sequencing experiments by amplification-free hybridization and investigated their distributions in patients with various disease conditions. Notably, the most significant miRNAs in diseases are often not contained in the miRBase. However, these candidates are evolutionary highly conserved. From the expression patterns, target gene and pathway analyses and evolutionary conservation analyses, we were able to shed light on the complexity of miRNAs in humans. Our data also highlight that a more thorough validation of miRNAs identified by next generation sequencing is required. The results are available in miRCarta ( https://mircarta.cs.uni-saarland.de ).

This paper estimates the potential to reduce market-based charging and discharging of a battery energy storage system (BESS) that provides frequency containment reserve (FCR) through (a) the utilization of degrees of freedom (DOFs) during reserve provision, and (b) the utilization of the flexibility of local power units (LPUs) at an investigated industrial site. In context of a of a real BESS providing 7 MW of FCR, the potential is quantified based on a simulation using real grid frequency data and real power profiles of LPUs at an industrial site for the year 2016. At the explored site, LPUs identified to provide charge management of the BESS include wind turbines (WT), a combined heat and power plant (CHPP), and power consumption by the fans of a heating, ventilation and air conditioning (HVAC) system. Results indicate that the energy charged and discharged through market-based transactions can be reduced by 56% and 66% respectively; however, the majority of these reductions are caused by the intelligent use of DOFs. Savings by using the flexibility of the mentioned LPUs can be attributed largely to the flexible operation of the HVAC system due to its highest availability and potential to substitute market-based charge events of the BESS.

MiRNAs play a central role in physiological and pathological processes. Both for the biological understanding and for their clinical application, it is essential to understand the interaction of miRNAs and their targets. Target identification largely hinges on in-silico prediction, which requires a complete consideration of miRNA binding sites within the UTRs of target genes. Here, we show that 5-mer sites might also play an essential role for human miRNA-target binding. We implemented and employed an algorithm to all pairs of 2,588 human miRNAs annotated in miRBase and the 3′ UTRs of 16725 genes (>43 million combinations). Our in-silico analysis showed a highly significant enrichment (p = 1.4 × 10(−69)) of 5-mer binding sites in 3′ UTRs across all experimentally validated miRNA-target gene pairs. We next confirmed the central role of 5-mer binding sites by reporter assays and demonstrated that two non-canonical 5-mer sites of miR-34a in the 3′ UTR of T-cell receptor alpha (TCRA) have a significantly stronger influence on its posttranscriptional regulation than the canonical binding sites. These observations indicate an essential role of 5-mer binding sites for the miRNA targeting in human cells.

// Tim Kehl 1, * , Christina Backes 2, * , Fabian Kern 2 , Tobias Fehlmann 2 , Nicole Ludwig 3 , Eckart Meese 3 , Hans-Peter Lenhof 1 and Andreas Keller 2, * 1 Center for Bioinformatics, Saarland University, Saarland Informatics Campus, Saarbrucken, Germany 2 Chair for Clinical Bioinformatics, Saarland University, Saarland Informatics Campus, Saarbrucken, Germany 3 Department of Human Genetics, Saarland University, Homburg, Germany * These authors have contributed equally to this work Correspondence to: Andreas Keller, email: andreas.keller@ccb.uni-saarland.de Keywords: non-coding RNA; systems biology; target gene; microRNA Received: July 27, 2017 Accepted: September 21, 2017 Published: November 09, 2017 ABSTRACT miRNAs are typically repressing gene expression by binding to the 3’ UTR, leading to degradation of the mRNA. This process is dominated by the eight-base seed region of the miRNA. Further, miRNAs are known not only to target genes but also to target significant parts of pathways. A logical line of thoughts is: miRNAs with similar (seed) sequence target similar sets of genes and thus similar sets of pathways. By calculating similarity scores for all 3.25 million pairs of 2,550 human miRNAs, we found that this pattern frequently holds, while we also observed exceptions. Respective results were obtained for both, predicted target genes as well as experimentally validated targets. We note that miRNAs target gene set similarity follows a bimodal distribution, pointing at a set of 282 miRNAs that seems to target genes with very high specificity. Further, we discuss miRNAs with different (seed) sequences that nonetheless regulate similar gene sets or pathways. Most intriguingly, we found miRNA pairs that regulate different gene sets but similar pathways such as miR-6886-5p and miR-3529-5p. These are jointly targeting different parts of the MAPK signaling cascade. The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons. The full set of information that allows to infer detailed results on each miRNA has been included in miRPathDB, the miRNA target pathway database ( https://mpd.bioinf.uni-sb.de ).

The state-of-the-art blood pressure measurement is beside common cuff-based methods the cuff-less estimation of pulse-transit-time, which is the time a blood pressure wave requires to travel from left ventricle of the heart to another peripheral point in the cardiovascular system. Within this work we present a novel estimation method for cuff-less blood pressure measurement by analysing a single photoplethysmographic signal in the frequency domain. The harmonic phase-shift of the fundamental frequency and the first harmonic within the photoplethysmographic signal has proven a strong correlation of r = 0.8514 and r = 0.9315 with systolic and diastolic blood pressure respectively.

Small non-coding RNAs such as microRNAs are master regulators of gene expression. One of the most promising applications of miRNAs is the use as liquid biopsy. Especially early diagnosis is an effective means to increase patients’ overall survival. E.g. in oncology a tumor is detected at best prior to its clinical manifestation. We generated genome-wide miRNA profiles from serum of patients and controls from the population-based Janus Serum Bank (JSB) and analyzed them by bioinformatics and artificial intelligence approaches. JSB contains sera from 318,628 originally healthy persons, more than 96,000 of whom later developed cancer. We selected 210 serum samples of patients with lung, colon or breast cancer at three time points prior to diagnosis, after cancer diagnosis and controls. The controls were matched with regard to age of the blood donor and to the time points of blood drawing, which were 27, 32, or 38 years prior to diagnosis. Using ANOVA we report 70 significantly deregulated markers (adjusted p-value−10). Further, 91miRNAs were differently expressed in pre-diagnostic samples as compared to controls (nominal p

Arm selection, the preferential expression of a 3′ or 5′ mature microRNA (miRNA), is a highly dynamic and tissue-specific process. Time-dependent expression shifts or switches between the arms are also relevant for human diseases. We present miRSwitch, a web server to facilitate the analysis and interpretation of arm selection events. Our species-independent tool evaluates pre-processed small non-coding RNA sequencing (sncRNA-seq) data, i.e. expression matrices or output files from miRNA quantification tools (miRDeep2, miRMaster, sRNAbench). miRSwitch highlights potential changes in the distribution of mature miRNAs from the same precursor. Group comparisons from one or several user-provided annotations (e.g. disease states) are possible. Results can be dynamically adjusted by choosing from a continuous range of highly specific to very sensitive parameters. Users can compare potential arm shifts in the provided data to a human reference map of pre-computed arm shift frequencies. We created this map from 46 tissues and 30,521 samples. As case studies we present novel arm shift information in a Alzheimer’s disease biomarker data set and from a comparison of tissues in Homo sapiens and Mus musculus. In summary, miRSwitch offers a broad range of customised arm switch analyses along with comprehensive visualisations, and is freely available at: https://www.ccb.uni-saarland.de/mirswitch/.

Background Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter–enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability. Results We have extended our $$\textsc{TEPIC}$$TEPIC framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer–promoter loops involving YY1 in different cell lines. Conclusion We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

Multiple studies endorsed the positive effect of regular exercise on mental and physical health. However, the molecular mechanisms underlying training-induced fitness in combination with personal life-style remain largely unexplored. Circulating biomarkers such as microRNAs (miRNAs) offer themselves for studying systemic and cellular changes since they can be collected from the bloodstream in a low-invasive manner. In Homo sapiens miRNAs are known to regulate a substantial number of protein-coding genes in a post-transcriptional manner and hence are of great interest to understand differential gene expression profiles, offering a cost-effective mechanism to study molecular training adaption, and connecting the dots from genomics to observed phenotypes. Here, we investigated molecular expression patterns of 2549 miRNAs in whole-blood samples from 23 healthy and untrained adult participants of a cross-over study, consisting of eight weeks of endurance training, with several sessions per week, followed by 8 weeks of washout and another 8 weeks of running, using microarrays. Participants were randomly assigned to one of the two study groups, one of which administered carbohydrates before each session in the first training period, and switching the treatment group for the second training period. During running sessions clinical parameters as heartbeat frequency were recorded. This information was extended with four measurements of maximum oxygen uptake (VO 2 max) for each participant. We observed that multiple circulating miRNAs show expression changes after endurance training, leveraging the capability to separate the blood samples by training status. To this end, we demonstrate that most of the variance in miRNA expression can be explained by both common and known biological and technical factors. Our findings highlight six distinct clusters of miRNAs, each exhibiting an oscillating expression profile across the four study timepoints, that can effectively be utilized to predict phenotypic VO 2 max levels. In addition, we identified miR-532-5p as a candidate marker to determine personal alterations in physical training performance on a case-by-case analysis taking the influence of a carbohydrate-rich nutrition into account. In literature, miR-532-5p is known as a common down-regulated miRNA in diabetes and obesity, possibly providing a molecular link between cellular homeostasis, personal fitness levels, and health in aging. We conclude that circulating miRNA expression can be altered due to regular endurance training, independent of the carbohydrate (CHO) availability in the training timeframe. Further validation studies are required to confirm the role of exercise-affected miRNAs and the extraordinary function of miR-532-5p in modulating the metabolic response to a high availability of glucose.

Motivation Since the initial discovery of microRNAs as post-transcriptional, regulatory key players in the 1990s, a total number of $2656$ mature microRNAs have been publicly described for Homo sapiens. As discovery of new miRNAs is still on-going, target identification remains to be an essential and challenging step preceding functional annotation analysis. One key challenge for researchers seems to be the selection of the most appropriate tool out of the larger multiverse of published solutions for a given research study set-up. Results In this review we collectively describe the field of in silico target prediction in the course of time and point out long withstanding principles as well as recent developments. By compiling a catalog of characteristics about the 98 prediction methods and identifying common and exclusive traits, we signpost a simplified mechanism to address the problem of application selection. Going further we devised interpretation strategies for common types of output as generated by frequently used computational methods. To this end, our work specifically aims to make prospective users aware of common mistakes and practical questions that arise during the application of target prediction tools. Availability An interactive implementation of our recommendations including materials shown in the manuscript is freely available at https://www.ccb.uni-saarland.de/mtguide.

Summary Prediction of transcription factor (TF) binding from epigenetics data and integrative analysis thereof are challenging. Here, we present TEPIC 2 a framework allowing for fast, accurate and versatile prediction, and analysis of TF binding from epigenetics data: it supports 30 species with binding motifs, computes TF gene and scores up to two orders of magnitude faster than before due to improved implementation, and offers easy-to-use machine learning pipelines for integrated analysis of TF binding predictions with gene expression data allowing the identification of important TFs. Availability and implementation TEPIC is implemented in C++, R, and Python. It is freely available at https://github.com/SchulzLab/TEPIC and can be used on Linux based systems. Supplementary information Supplementary data are available at Bioinformatics online.

Breast cancer is a heterogeneous disease with distinct molecular subtypes including the aggressive subtype triple-negative breast cancer (TNBC). We compared blood-borne miRNA signatures of early-stage basal-like (cytokeratin-CK5-positive) TNBC patients to age-matched controls. The miRNAs of TNBC patients were assessed prior to and following platinum-based neoadjuvant chemotherapy (NCT). After an exploratory genome-wide study on 21 cases and 21 controls using microarrays, the identified signatures were verified independently in two laboratories on the same and a new cohort by RT-qPCR. We differentiated the blood of TNBC patients before NCT from controls with 84% sensitivity. The most significant miRNA for this diagnostic classification was miR-126-5p (two tailed t-test p-value of 1.4 × 10−5). Validation confirmed the microarray results for all tested miRNAs. Comparing cancer patients prior to and post NCT highlighted 321 significant miRNAs (among them miR-34a, p-value of 1.2 × 10−23). Our results also suggest that changes in miRNA expression during NCT may have predictive potential to predict pathological complete response (pCR). In conclusion we report that miRNA expression measured from blood facilitates early and minimally-invasive diagnosis of basal-like TNBC. We also demonstrate that NCT has a significant influence on miRNA expression. Finally, we show that blood-borne miRNA profiles monitored over time have potential to predict pCR.

A four-month-old male fox shot dead by a hunter was subjected to unenhanced postmortem computed tomography (PMCT), PMCT-angiography (PMCTA) and magnetic resonance imaging (PMMR) to document the effects of semi-jacketed hunting ammunition (.17 Hornet) on the body of a small animal. The effect of the ammunition on bone structures (crushing of the cervical spine and spinal cord), blood vessels (laceration of all supra-aortal vessels) and surrounding soft tissue (large wound cavity) was extensive, and imaging confirmed the fatal power of the ammunition. Virtual autopsy (Virtopsy) is a valuable examination tool for veterinary indications as well as in humans.