ProtVar: mapping and contextualizing human missense variation.

Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Transfer learning for drug-target interaction prediction.

MOTIVATION: Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach. RESULTS: Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.

Inflammatory bowel disease induces pathological α-synuclein aggregation in the human gut and brain.

AIMS: According to Braak's hypothesis, it is plausible that Parkinson's disease (PD) originates in the enteric nervous system (ENS) and spreads to the brain through the vagus nerve. In this work, we studied whether inflammatory bowel diseases (IBDs) in humans can progress with the emergence of pathogenic α-synuclein (α-syn) in the gastrointestinal tract and midbrain dopaminergic neurons. METHODS: We have analysed the gut and the ventral midbrain from subjects previously diagnosed with IBD and form a DSS-based rat model of gut inflammation in terms of α-syn pathology. RESULTS: Our data support the existence of pathogenic α-syn in both the gut and the brain, thus reinforcing the potential role of the ENS as a contributing factor in PD aetiology. Additionally, we have analysed the effect of a DSS-based rat model of gut inflammation to demonstrate (i) the appearance of P-α-syn inclusions in both Auerbach's and Meissner's plexuses (gut), (ii) an increase in α-syn expression in the ventral mesencephalon (brain) and (iii) the degeneration of nigral dopaminergic neurons, which all are considered classical hallmarks in PD. CONCLUSION: These results strongly support the plausibility of Braak's hypothesis and emphasise the significance of peripheral inflammation and the gut-brain axis in initiating α-syn aggregation and transport to the substantia nigra, resulting in neurodegeneration.

The Quest for Orthologs orthology benchmark service in 2022.

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Hierarchical deep learning for predicting GO annotations by integrating protein knowledge.

MOTIVATION: Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data generated by high-throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO-terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional prediction are mainly focused on sequence data and have not achieved breakthrough performances yet. RESULTS: We propose DeeProtGO, a novel deep-learning model for predicting GO annotations by integrating protein knowledge. DeeProtGO was trained for solving 18 different prediction problems, defined by the three GO sub-ontologies, the type of proteins, and the taxonomic kingdom. Our experiments reported higher prediction quality when more protein knowledge is integrated. We also benchmarked DeeProtGO against state-of-the-art methods on public datasets, and showed it can effectively improve the prediction of GO annotations. AVAILABILITY AND IMPLEMENTATION: DeeProtGO and a case of use are available at https://github.com/gamerino/DeeProtGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Changes in vineyard soil parameters after repeated application of organic-inorganic amendments based on spent mushroom substrate.

The changes of physicochemical and biochemical parameters of a silty loam (S1) and sandy loam (S2) vineyard soils added with spent mushroom substrate (SMS) or SMS composted with ophite (OF) as rock dust (SMS + OF) were studied. Two doses of SMS or SMS + OF (25 and 100 Mg ha-1) were applied for two consecutive years (2020-2021) and changes of soil physicochemical parameters, and dehydrogenase activity (DHA), respiration (RES), microbial biomass (BIO), and the phospholipid fatty acids (PLFAs) profile were assayed on a temporal basis. The results showed an increase in soil organic carbon (OC) content, total and mineralised N, P, and K, especially when the highest SMS dose was applied to soils. Repeated application caused OC content over time up to 2.3 times higher than initial content in the silty loam soil. This increase was not observed in sandy soil, possibly due to a higher bioavailability of OC, as indicated by the evolution of extractable humic acid/fulvic acid pools. In both soils, all biochemical parameters increased after amendment, being favoured both by the OC and by the presence of OF. Significant positive correlations were found between DHA, RES and BIO, and OC content especially in the first part and then levelled off after the second dose application. Total bacterial or fungal PLFAs patterns reflected the variation of BIO by SMS application. The higher growth of fungi vs. bacterial community in amended soils was recorded after the first SMS application, although the opposite effect occurred after the second application, with similar results in both soils. The findings indicate that the application of SMS or SMS + OF in vineyard soils could be an appropriate agronomic management practice for maintaining soil sustainability, although doses and application times of these amendments should first be evaluated depending on soil texture.

Combining participatory action research and emerging ways of collective action to promote institutional change toward social commitment: Groundings, strategies, and implications of an experience.

AIMS: This study reports the foundations, strategies, and results of an institutional change experience based on the combination of participatory-action-research and new currents of collective mobilization and political participation. It aimed to achieve the institution's greater social commitment and a more participatory and transparent management. METHODS: The process took place in a Spanish public university and was promoted and coordinated by a Work Group that emerged from grassroots university community. Collective diagnosis was performed through face-to-face strategies (global, sectorial, and faculty meetings) and virtual tools (web-blog, on-line surveys, shared documents). Collective action combined nonformal with formal institutional participation and applied hybrid activism, self-organization in horizontal structures and integrative conflict management. RESULTS: A sequential process of diagnosis, collective action, and negotiation was implemented. As a result, the university Governing Team, representatives from different sectors and members of the Work Group worked jointly to define several institutional actions that were thereafter launched. Those actions aimed to improve institutional participation and transparency, and greater institutional social commitment. CONCLUSION: The combination of participatory-action-research and new ways of collective action can be an excellent tool to draw institutions towards greater social engagement, thus contributing to sustainable social change. A model to guide institutional change is drafted.

PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models.

BACKGROUND: Epistasis is the interaction between different genes when expressing a certain phenotype. If epistasis involves more than two loci it is called high-order epistasis. High-order epistasis is an area under active research because it could be the cause of many complex traits. The most common way to specify an epistasis interaction is through a penetrance table. RESULTS: This paper presents PyToxo, a Python tool for generating penetrance tables from any-order epistasis models. Unlike other tools available in the bibliography, PyToxo is able to work with high-order models and realistic penetrance and heritability values, achieving high-precision results in a short time. In addition, PyToxo is distributed as open-source software and includes several interfaces to ease its use. CONCLUSIONS: PyToxo provides the scientific community with a useful tool to evaluate algorithms and methods that can detect high-order epistasis to continue advancing in the discovery of the causes behind complex diseases.

Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation.

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

Epidemiological Study of Tricuspid Regurgitation After Cardiac Transplantation. Does it Influence Survival?

Background: Tricuspid valve disease is the most frequent valvulopathy after heart transplantation (HTx). Evidence for the negative effect of post-transplant tricuspid regurgitation (TR) on survival is contradictory. The aim of this study was to analyze the causes of post-transplant TR and its effect on overall mortality. Methods: This is a retrospective observational study of all transplants performed in two Spanish centers (1009 patients) between 2000 and 2019. Of the total number of patients, 809 had no TR or mild TR and 200 had moderate or severe TR. The etiology of TR was analyzed in all cases. Results: The prevalence of moderate and severe TR was 19.8%. The risk of mortality was greater when TR was caused by early primary graft failure (PGF) or rejection (p < 0.05). TR incidence was related to etiology: incidence of PGF-induced TR was higher in the first period, while TR due to rejection and undefined causes occurred more frequently in three periods: in the first year, in the 10-14-year period following HTx, and in the long term (16-18 years). In the multivariable analysis, TR was significantly associated with mortality/retransplantation (HR:1.04, 95% CI:1.01-1.07, p:0.02). Conclusion: The development of TR after HTx is relatively frequent. The annual incidence depends on TR severity and etiology. The risk of mortality is greater in severe TR due to PGF or rejection.

Harmonized D-dimer levels upon admission for prognosis of COVID-19 severity: Results from a Spanish multicenter registry (BIOCOVID-Spain study).

Coagulopathy is a key feature of COVID-19 and D-dimer has been reported as a predictor of severity. However, because D-dimer test results vary considerably among assays, resolving harmonization issues is fundamental to translate findings into clinical practice. In this retrospective multicenter study (BIOCOVID study), we aimed to analyze the value of harmonized D-dimer levels upon admission for the prediction of in-hospital mortality in COVID-19 patients. All-cause in-hospital mortality was defined as endpoint. For harmonization of D-dimer levels, we designed a model based on the transformation of method-specific regression lines to a reference regression line. The ability of D-dimer for prediction of death was explored by receiver operating characteristic curves analysis and the association with the endpoint by Cox regression analysis. Study population included 2663 patients. In-hospital mortality rate was 14.3%. Harmonized D-dimer upon admission yielded an area under the curve of 0.66, with an optimal cut-off value of 0.945 mg/L FEU. Patients with harmonized D-dimer ≥ 0.945 mg/L FEU had a higher mortality rate (22.4% vs. 9.2%; p < 0.001). D-dimer was an independent predictor of in-hospital mortality, with an adjusted hazard ratio of 1.709. This is the first study in which a harmonization approach was performed to assure comparability of D-dimer levels measured by different assays. Elevated D-dimer levels upon admission were associated with a greater risk of in-hospital mortality among COVID-19 patients, but had limited performance as prognostic test.

The Quest for Orthologs benchmark service and consensus calls in 2020.

The identification of orthologs-genes in different species which descended from the same gene in their last common ancestor-is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Quest for Orthologs consortium maintains a reference set of proteomes and provides a web server for continuous orthology benchmarking (http://orthology.benchmarkservice.org). Furthermore, consensus ortholog calls derived from public benchmark submissions are provided on the Alliance of Genome Resources website, the joint portal of NIH-funded model organism databases.

Toxo: a library for calculating penetrance tables of high-order epistasis models.

BACKGROUND: Epistasis is defined as the interaction between different genes when expressing a specific phenotype. The most common way to characterize an epistatic relationship is using a penetrance table, which contains the probability of expressing the phenotype under study given a particular allele combination. Available simulators can only create penetrance tables for well-known epistasis models involving a small number of genes and under a large number of limitations. RESULTS: Toxo is a MATLAB library designed to calculate penetrance tables of epistasis models of any interaction order which resemble real data more closely. The user specifies the desired heritability (or prevalence) and the program maximizes the table's prevalence (or heritability) according to the input epistatic model boundaries. CONCLUSIONS: Toxo extends the capabilities of existing simulators that define epistasis using penetrance tables. These tables can be directly used as input for software simulators such as GAMETES so that they are able to generate data samples with larger interactions and more realistic prevalences/heritabilities.

Expanding the horizons of microRNA bioinformatics.

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.

Mobility monitoring of two herbicides in amended soils: A field study for modeling applications.

This paper reports the mobility and total balance of chlorotoluron (CTL), flufenacet (FNC) and bromide ion (Br-) throughout a sandy soil profile after the application of spent mushroom substrate (SMS) and green compost (GC). Obtaining mobility dataset is crucial to simulate the herbicides' fate under amended soil scenarios by application pesticide leaching models with regulatory application (FOCUS models). The application of organic residues is nowadays increased to improve the crop yields and there is a gap in the simulations of this kind of amended scenarios. A two-year field experiment involving unamended soil (S) and SMS- or GC-amended soil plots was conducted. CTL, FNC, and Br- were annually applied and their residual concentrations were determined in soil profiles (0-100 cm) regularly sampled. In all the treatments the order of mobility is followed as FNC < CTL < Br-. SMS and GC increased herbicide retention in the top 10 cm by the higher organic carbon (OC) content than the unamended soil, and their ability to increase the soil's water-holding capacity and to decrease water percolation. Simultaneously dissolved organic carbon (DOC) content facilitated herbicide transport being it favoured by the initial soil moisture content and the rainfall shortly after the chemicals' initial application. Over the first year, residual amounts (<2.6%) of Br-, CTL and FNC were leached down to 90-100 cm depth in the three treatments. However, over the second year low CTL and FNC amounts (<1.0%) reached the bottom layer only in S + SMS although high Br- concentrations did so in the three treatments (<20%). According to the total balance of Br-, CTL, and FNC in the soil profiles other processes (degradation, mineralisation, bound residues formation, and/or crop uptake) different from leaching below 1 m depth might play a key role in their dissipation especially in the amended soil profiles. SMS and GC are likely to be used as organic amendments to preserve the soil and water quality but in the case of SMS, its higher DOC content could imply a higher potential risk for groundwater contamination than GC.

UniProt genomic mapping for deciphering functional effects of missense variants.

Understanding the association of genetic variation with its functional consequences in proteins is essential for the interpretation of genomic data and identifying causal variants in diseases. Integration of protein function knowledge with genome annotation can assist in rapidly comprehending genetic variation within complex biological processes. Here, we describe mapping UniProtKB human sequences and positional annotations, such as active sites, binding sites, and variants to the human genome (GRCh38) and the release of a public genome track hub for genome browsers. To demonstrate the power of combining protein annotations with genome annotations for functional interpretation of variants, we present specific biological examples in disease-related genes and proteins. Computational comparisons of UniProtKB annotations and protein variants with ClinVar clinically annotated single nucleotide polymorphism (SNP) data show that 32% of UniProtKB variants colocate with 8% of ClinVar SNPs. The majority of colocated UniProtKB disease-associated variants (86%) map to 'pathogenic' ClinVar SNPs. UniProt and ClinVar are collaborating to provide a unified clinical variant annotation for genomic, protein, and clinical researchers. The genome track hubs, and related UniProtKB files, are downloadable from the UniProt FTP site and discoverable as public track hubs at the UCSC and Ensembl genome browsers.

Standardized benchmarking in the quest for orthologs.

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

In-hospital postoperative infection after heart transplantation: Risk factors and development of a novel predictive score.

INTRODUCTION: Infection is one of the most significant complications following heart transplantation (HT). The aim of this study was to identify specific risk factors for early postoperative infections in HT recipients, and to develop a multivariable predictive model to identify HT recipients at high risk. METHODS: A single-center, observational, and retrospective study was conducted. The dependent variable was in-hospital postoperative infection. We examined demographic and epidemiological data from donors and recipients, surgical features, and adverse postoperative events as independent variables. Backwards, stepwise multivariable logistic regression with a P-value < 0.05 was used to identify clinical factors independently associated with the risk of in-hospital postoperative infections following HT. RESULTS: Six hundred seventy-seven patients were included in this study. During the in-hospital postoperative period, 348 episodes of infection were diagnosed in 239 (35.9%) patients. Seven variables were identified as independent clinical predictors of early postoperative infection after HT: history of diabetes mellitus, previous sternotomy, preoperative mechanical ventilation, primary graft failure, major surgical bleeding, use of mycophenolate mofetil, and use of itraconazole. Based on the results of multivariable models, we constructed a 7-variable (8-point) score to predict the risk of in-hospital postoperative infection in HT recipients, which showed a reasonable ability to predict the risk of in-hospital postoperative infection in this population. Prospective external validation of this new score is warranted to confirm its clinical applicability. CONCLUSIONS: In-hospital postoperative infection is a common complication after HT, affecting 35% of patients who underwent this procedure at our institution. Diabetes mellitus, previous sternotomy, preoperative mechanical ventilation, primary graft failure, major surgical bleeding, use of mycophenolate mofetil, and itraconazole were all independent clinical predictors of early postoperative infection after HT.