EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt.

The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene-disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics.

Open Targets Genetics (https://genetics.opentargets.org) is an open-access integrative resource that aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. This enables systematic identification and prioritisation of likely causal variants and genes across all published trait-associated loci. In this paper, we describe the public resources we aggregate, the technology and analyses we use, and the functionality that the portal offers. Open Targets Genetics can be searched by variant, gene or study/phenotype. It offers tools that enable users to prioritise causal variants and genes at disease-associated loci and access systematic cross-disease and disease-molecular trait colocalization analysis across 92 cell types and tissues including the eQTL Catalogue. Data visualizations such as Manhattan-like plots, regional plots, credible sets overlap between studies and PheWAS plots enable users to explore GWAS signals in depth. The integrated data is made available through the web portal, for bulk download and via a GraphQL API, and the software is open source. Applications of this integrated data include identification of novel targets for drug discovery and drug repurposing.

Multi-omics Characterization of Interaction-mediated Control of Human Protein Abundance levels.

Proteogenomic studies of cancer samples have shown that copy-number variation can be attenuated at the protein level for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction-mediated control of protein abundance remains poorly characterized. To study this, we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction-dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of posttranscriptional control of protein abundance in cancer and healthy cells.

Benchmarking substrate-based kinase activity inference using phosphoproteomic data.

MOTIVATION: Phosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies. RESULTS: We used curated phosphoproteomic experiments and a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence ( in vivo , in vitro or in silico ) supporting these influence the predictions. CONCLUSIONS: Most models performed well with the Z-test and the GSEA performing best as determined by the area under the ROC curve (Mean AUC = 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions. AVAILABILITY AND IMPLEMENTATION: The KSEA implementation is available in https://github.com/ evocellnet/ksea. Additional data is available in http://phosfate.com. CONTACT: pbeltrao@ebi.ac.uk or ochoa@ebi.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast.

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

An atlas of human kinase regulation.

The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision-making has been limited by the small number of simultaneously monitored phospho-regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 conditions derived from a large compilation of phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co-regulation along the conditions predicts kinase-complex and kinase-substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essential mediators of stem cell differentiation, modulators of Salmonella infection, and new targets of AKT1. This provides a global view of human phosphorylation-based signaling and the necessary context to better understand kinase-driven decision-making.

Detection of significant protein coevolution.

MOTIVATION: The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals. RESULTS: In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information. AVAILABILITY AND IMPLEMENTATION: The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/. CONTACT: pazos@cnb.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kinase-two-hybrid: towards the conditional interactome.

The dynamics of the protein­protein interaction network and how it responds to biological perturbations remain difficult to assay by most traditional techniques. A novel kinase-dependent yeast two­hybrid framework by Stelzl and colleagues (Grossmann et al, 2015) provides a new prism to study how tyrosine phosphorylation regulates the changes in the interactome under varying conditions.

Sex hormones and the total testosterone:estradiol ratio as predictors of severe acute respiratory syndrome coronavirus 2 infection in hospitalized men.

BACKGROUND: The predictive ability of the early determination of sex steroids and the total testosterone:estradiol ratio for the risk of severe coronavirus disease 2019 or the potential existence of a biological gradient in this relationship has not been evaluated. OBJECTIVES: To assess the relationship of sex steroid levels and the total testosterone:estradiol ratio with the risk of severe acute respiratory syndrome coronavirus 2 infection in men, defined as the need for intensive care unit admission or death, and the predictive ability of each biomarker. MATERIALS AND METHODS: This was a prospective observational study. We included all consecutive adult men with severe acute respiratory syndrome coronavirus 2 infections in a single center admitted to a general hospital ward or to the intensive care unit. Sex steroids were evaluated at the centralized laboratory of our hospital. RESULTS: We recruited 98 patients, 54 (55.1%) of whom developed severe coronavirus disease in 2019. Compared to patients with nonsevere coronavirus disease 2019, patients with severe coronavirus disease 2019 had significantly lower serum levels of total testosterone (111 ± 89 vs. 191 ± 143 ng/dL; p < 0.001), dehydroepiandrosterone (1.69 ± 1.26 vs. 2.96 ± 2.64 ng/mL; p < 0.001), and dehydroepiandrosterone sulfate (91.72 ± 76.20 vs. 134.28 ± 98.261 µg/dL; p = 0.009), significantly higher levels of estradiol (64.61 ± 59.35 vs. 33.78 ± 13.78 pg/mL; p = 0.001), and significantly lower total testosterone:estradiol ratio (0.28 ± 0.31 vs. 0.70 ± 0.75; p < 0.001). The lower the serum level of androgen and the lower the total testosterone:estradiol ratio values, the higher the likelihood of developing severe coronavirus disease 2019, with the linear trend in the adjusted analyses being statistically significant for all parameters except for androstenedione (p = 0.064). In the receiver operating characteristic analysis, better predictive performance was shown by the total testosterone:estradiol ratio, with an area under the curve of 0.77 (95% confidence interval 0.68-0.87; p < 0.001). DISCUSSION AND CONCLUSION: Our results suggest that men with severe acute respiratory syndrome coronavirus 2 infection, decreased androgen levels and increased estradiol levels have a higher likelihood of developing an unfavorable outcome. The total testosterone:estradiol ratio showed the best predictive ability.

Incidental Diagnosis of Metastatic Breast Cancer in a Man With 99m Tc-PSMA SPECT/CT.

ABSTRACT: Breast cancer in men is a rare and unsuspected malignancy. A 48-year-old man begins with disabling low back pain. The CT scan reported a compression fracture in L2 and diffuse skeletal lesions suggestive of metastatic disease. The serum prostate-specific antigen was 6.2 ng/mL. He was referred for SPECT/CT with 99m Tc-EDDA/HYNIC-inhibitor prostate-specific membrane antigen due to clinical suspicion of prostate cancer. SPECT/CT with 99m Tc-EDDA/HYNIC-inhibitor prostate-specific membrane antigen showed a primary lesion in the left breast and multiple bone lesions. Biopsy confirmed infiltrating ductal carcinoma with positive hormone receptors and indeterminate HER2 (human epidermal growth factor receptor 2).

Network expansion of genetic associations defines a pleiotropy map of human cell biology.

Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity.

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

A Comparison of 18F-PSMA-1007 and 64Cu-PSMA in 2 Patients With Metastatic Prostate Cancer.

ABSTRACT: 18F-prostate-specific membrane antigen (PSMA) 1007 is one of the most promising radiotracers for PET imaging in prostate cancer, minimal urinary clearance, and higher spatial resolution, which are the most outstanding features. PSMA can also be labeled with 64Cu, offering a longer half-life and different resolution imaging. We present images of metastatic prostate cancer in two patients, where 64Cu-PSMA PET/CT was performed one day after 18F-PSMA-1007 PET/CT. In the two patients, both radiotracers provided high image quality and a similar range of detection for metastatic lesions.

Diagnosis of Traumatic Bronchobiliary Fistula With Late 99m Tc-Mebrofenin SPECT/CT.

ABSTRACT: Bronchobiliary fistula (BBF) represents a rare disorder; it consists of abnormal interconnection between the biliary tract and bronchial trees. A 22-year-old woman with persistent chest pain, jaundice, and biliptysis was referred for hepatobiliary scintigraphy under clinical suspicion of a BBF. Patient medical history was consistent with biliary tree reconstruction secondary to an iatrogenic injury during cholecystectomy 4 years ago. Previous complementary studies (CT and MR cholangiopancreatography) were equivocal for diagnosis. Planar dynamic images of hepatobiliary scintigraphy in the first hour were inconclusive. A 24-hour SPECT/CT was performed and confirmed the BBF in a minimally invasive way.

High-throughput functional characterization of protein phosphorylation sites in yeast.

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, fewer than 5% of thousands of recently discovered phosphosites have been functionally annotated. In this study, we devised a chemical genetic approach to study the functional relevance of phosphosites in Saccharomyces cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion, suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale.

Semiautomatic assessment of whole-body tumor burden with 18F-PSMA-1007 in biochemical recurrent prostate cancer.

OBJECTIVE: The aim of the study was to evaluate the 18F-PSMA-1007 PET/computed tomography (CT) semiautomatic volumetric parameters to assess the whole-body tumor burden and its correlation with prostate-specific antigen (PSA) and Gleason score in patients with biochemically recurrent prostate cancer (PCa). MATERIALS AND METHODS: A total of 110 patients referred for 18F-PSMA-1007 PET/CT due to biochemical recurrence were retrospectively analyzed. Whole-body total lesion prostate-specific membrane antigen (wbTl-PSMA) and whole-body PSMA-derived tumor volume (wbPSMA-TV) metrics on 18F-PSMA-1007 were obtained semiautomatically in dedicated software. A Spearman test was performed to explore the correlation of volumetric imaging parameters with PSA levels and Gleason score. To analyze the association between volumetric measures and PSA subgroups, we used a Kruskal-Wallis test and a Dunn's test to identify each group causing an observed difference. RESULTS: A total of 492 metastatic lesions were analyzed, and a significant correlation was found between wbTL-PSMA (R = 0.63, P < 0.0001) and wbPSMA-TV (R = 0.49, P < 0.0001) with serum PSA. A statistically significant difference with wbTL-PSMA was found in patients with a PSA less than or equal 0.5 ng/ml and PSA in the range of 0.51-1.0 ng/ml. CONCLUSION: 18F-PSMA-1007 PSMA volumetric parameters can provide a quantitative imaging biomarker for whole-body tumor burden.

Selection of organisms for the co-evolution-based study of protein interactions.

BACKGROUND: The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature. RESULTS: We show that the performance of three mirrortree-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions. CONCLUSIONS: In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest.

Studying the co-evolution of protein families with the Mirrortree web server.

SUMMARY: The Mirrortree server allows to graphically and interactively study the co-evolution of two protein families, and investigate their possible interactions and functional relationships in a taxonomic context. The server includes the possibility of starting from single sequences and hence it can be used by non-expert users. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://csbg.cnb.csic.es/mtserver. It was tested in the main web browsers. Adobe Flash Player is required at the client side to perform the interactive assessment of co-evolution. CONTACT: pazos@cnb.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.