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The SOLPS-ITER code has been utilised to study the movement of the detachment front location from target towards the X-point for MAST-U Super-X plasmas. Two sets of detached steady state solutions are obtained by either varying the deuterium ( D _2 ) fuelling rate or the nitrogen ( N ) seeding rate to scan the corresponding ‘control’ parameters of outboard midplane density, $n_\mathrm{u}$ , and the divertor impurity concentration, $f_\mathrm{I}$ . At seeding and fuelling rates ∼10× and ∼5× that required to start detachment at the divertor target, the detachment front only reaches ∼50% of the poloidal distance to the X-point, $l_\mathrm{pol}$ , corresponding to a region of strong parallel gradients in the total magnetic field B . The region of strong total field gradients correlates with where the detachment front location becomes less sensitive to control parameter variation. This result is qualitatively consistent with the predictions of a simple, analytic detachment location sensitivity (DLS) model (Lipschultz et al 2016 Nucl. Fusion 56 056007) which is based in a scaled parallel-to- B space, z . While the DLS model predictions are in agreement with SOLPS-ITER results in terms of where the front location becomes less sensitive to controls (i.e. in the region of strong parallel gradients in B ), the DLS model predicts a higher sensitivity in the region of weak parallel gradients in B downstream as compared to the simulation results. Potential sources of differences between the SOLPS-ITER and DLS model predictions were explored: The DLS model does not include energy sinks beyond radiation from a single impurity nor cross-field energy transport. Momentum and particle balance are also not included in the DLS model. The tight opening into the divertor for flux surfaces could lead to variations in plasma-neutral pressure balance as the detachment front reaches that region, exactly how this affects the front movement needs further investigation.

Genetic variants might exert their functional effects via influencing molecular interaction. Here, the authors present a resource featuring almost 28,000 annotations describing the effect of small sequence changes on physical protein interactions, curated by IMEx Consortium curators.

Abstract Background Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. Results The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. Conclusions PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download.

Abstract Background A number of different molecular interactions data download formats now exist, designed to allow access to these valuable data by diverse user groups. These formats include the PSI-XML and MITAB standard interchange formats developed by Molecular Interaction workgroup of the HUPO-PSI in addition to other, use-specific downloads produced by other resources. The onus is currently on the user to ensure that a piece of software is capable of read/writing all necessary versions of each format. This problem may increase, as data providers strive to meet ever more sophisticated user demands and data types. Results A collaboration between EMBL-EBI and the University of Cambridge has produced JAMI, a single library to unify standard molecular interaction data formats such as PSI-MI XML and PSI-MITAB. The JAMI free, open-source library enables the development of molecular interaction computational tools and pipelines without the need to produce different versions of software to read different versions of the data formats. Conclusion Software and tools developed on top of the JAMI framework are able to integrate and support both PSI-MI XML and PSI-MITAB. The use of JAMI avoids the requirement to chain conversions between formats in order to reach a desired output format and prevents code and unit test duplication as the code becomes more modular. JAMI’s model interfaces are abstracted from the underlying format, hiding the complexity and requirements of each data format from developers using JAMI as a library.

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

Chemotherapy-induced cognitive impairment affects ~30% of breast cancer survivors, but the effects on how chemotherapy impacts brain lipids, and how omega-3 polyunsaturated fatty acid supplementation may confer protection, is unknown. Ovariectomized mice were randomized to two rounds of injections of doxorubicin + cyclophosphamide or vehicle after consuming a diet supplemented with 2% or 0% EPA+DHA, and sacrificed 4, 7, and 14 days after the last injection (study 1, n = 120) or sacrificed 10 days after the last injection (study 2, n = 40). Study 1 whole brain samples were extracted and analyzed by UHPLC-MS/MS to quantify specialized pro-resolving mediators (SPMs). Lipidomics analyses were performed on hippocampal extracts from study 2 to determine changes in the brain lipidome. Study 1 results: only resolvin D1 was present in all samples, but no differences in concentration were observed (P > 0.05). Study 2 results: chemotherapy was positively correlated with omega-9 fatty acids, and EPA+DHA supplementation helped to maintain levels of plasmalogens. No statistically significant chemotherapy*diet effect was observed. Results demonstrate a limited role of SPMs in the brain post-chemotherapy, but a significant alteration of hippocampal lipids previously associated with other models of cognitive impairment (i.e., Alzheimer’s and Parkinson’s disease).

Background: Perceived cognitive impairments(PCI) are the most common complications that Non-Central Nervous System (Non-CNS) cancers survivors experience. Studies have suggested that those who expreience fear of cancer recurrence (FCR) tend to report cognitive problems; however, this association has not been examined.Methods: Participants (n = 6,714) were enrolled in the Women’s Health Initiative Life and Longevity After Cancer study. FCR was assessed using the Cancer Worry Scale and PCI was assessed using the PCI subscale of FACT-Cog. The association between FCR and PCI was analyzed using univariable and multivariable logistic regression models. A cut off score of [Formula: see text] 14 is indicative of high FCR and below 14 indicating low FCR. Scores lower than 60 indicated PCI.Result: The multivariable model showed that higher FCR corresponded to an increase in odds of PCI (OR = 1.15, p

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated reference database for molecular complexes. It is a unifying web resource linking aggregated data on composition, topology and the function of macromolecular complexes from 28 species. In addition to significantly extending the number of manually curated complexes, we have massively extended the coverage of the human complexome through the incorporation of high confidence assemblies predicted by machine-learning algorithms trained on large-scale experimental data. The current content of the portal comprising 2150 human complexes has been augmented by 14 964 machine-learning (ML) predicted complexes from hu.MAP3.0. We have refactored the website to enable easy search and filtering of these different classes of protein complexes and have implemented the Complex Navigator, a visualisation tool to facilitate comparison of related complexes in the context of orthology or paralogy. We have embedded the Rhea reaction visualisation tool into the website to enable users to view the catalytic activity of enzyme complexes., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including π-HuB in China.

Objectives: Few studies have examined the impact of late-life depression trajectories on specific domains of cognitive function. This study aims to delineate how different depressive symptom trajectories specifically affect cognitive function in older adults., Design: Prospective longitudinal cohort study., Setting: Australia and the United States of America., Participants: In total, 11,035 community-dwelling older adults with a mean age of 75 years., Measurements: Depressive trajectories were modelled from depressive symptoms according to annual Centre for Epidemiological Studies Depression Scale 10 (CES-D-10) surveys. Four trajectories of depressive symptoms were identified: low ("nondepressed"), consistently mild ("subthreshold depression"), consistently moderate ("persistent depression"), and initially low but increasing ("emerging depression"). Global cognition (Modified Mini-Mental State Examination [3MS]), verbal fluency (Controlled Oral Word Association Test [COWAT]), processing speed (Symbol Digit Modalities Test [SDMT]), episodic memory (Hopkins Verbal Learning Test - Revised [HVLT-R]), and a composite z-score were assessed over a subsequent median 2 years., Results: Subthreshold depression predicted impaired performance on the SDMT (Cohen's d -0.04) and composite score (-0.03); emerging depression predicted impaired performance on the SDMT (-0.13), HVLT-R (-0.09), 3 MS (-0.08) and composite score (-0.09); and persistent depression predicted impaired performance on the SDMT (-0.08), 3 MS (-0.11), and composite score (-0.09)., Conclusions: Depressive symptoms are associated with later impaired processing speed. These effects are small. Diverse depression trajectories have different impacts on cognitive function., (This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.)

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ~75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is a collaborative effort with a central mission of aggregating detailed molecular interaction experimental evidence in a machine-readable format, supported by controlled vocabularies and standard ontologies. The IntAct molecular interaction database (www.ebi.ac.uk/intact), as an IMEx partner, serves as a valuable portal for accessing IMEx data through user-friendly search options and an array of interactive filters. The resource currently hosts an extensive repository of 1,293,508 binary interactions meticulously captured from 75,098 experiments documented in 23,366 publications (as of the February 2024 release), with this corpora being added to by regular data releases. IMEx curation policy has consistently prioritized a fine-grained data and curation model, with a focus on capturing the relevant experimental details essential for interpreting molecular interaction data effectively. Our curation process is designed to support the generation of interactomes tailored to contexts such as disease-specific or tissue-/cell-type-specific interactomes. These interactions are ranked according to a scoring system based on the Proteomics Standard Initiative Molecular Interaction (PSI MI) standards. This scoring system allows users to assess the degree of confidence in binary interactions, enhancing the value of the data. The resource provides insights into the nature of relationships among interacting partners as defined by the experimental setup and the associated biological context. Interactive filters enable users to navigate these rich, multilayered data, promoting a deeper understanding of biological complexity. Additionally, the IntAct website fosters the creation of networks for collaborative analyses by the scientific community. The recent transformation of the IntAct website, supported by a graph-type database, empowers users to execute custom queries tailored to their specific research interests. This article illustrates the diverse levels of annotations available for interactions and the multiple search options at users' disposal to access data of interest. © 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using Quick Search, network visualization, and filters Support Protocol: Accessing fine annotations from intact: Unlocking the molecular details Alternate Protocol: Using batch search: Querying multiple interactors Basic Protocol 2: Using advanced search: Precision and customization., (© 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC.)

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed., Competing Interests: Declaration of interests J.R.P. has received research honoraria from Novartis Biosciences and is a paid consultant for ProFound Therapeutics. J.G.A. is a paid consultant for Enara Bio and Moderna. J.L.A. is an advisor to Microneedle Solutions. T.F.M. is a consultant for and holds equity in Velia Therapeutics. J.S.W. is an advisor and holds equity in Velia Therapeutics. G.M. is co-founder and CSO of OHMX.bio. S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs, Seer and PrognomIQ. N.T.I. hold equity in Velia Therapeutics and holds equity and serves as a scientific advisor to Tevard Biosciences. P.F. is a member of the scientific advisory board of Infinitopes. A.-R. C. is a member of the advisory board of ProFound Therapeutics.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

In recent years, a huge amount of data on ncRNA interactions has been described in scientific papers and databases. Although considerable effort has been made to annotate the available knowledge in public repositories, there are still significant discrepancies in how different resources capture and interpret data on ncRNA functional and physical associations. In the present paper, we present a collection of microRNA-mRNA interactions annotated from the scientific literature following recognized standard criteria and focused on microRNAs, which regulate genes associated with rare diseases as a case study. The list of protein-coding genes with a known role in specific rare diseases was retrieved from the Genome England PanelApp, and associated microRNA-mRNA interactions were annotated in the IntAct database and compared with other datasets. RNAcentral identifiers were used for unambiguous, stable identification of ncRNAs. The information about the interaction was enhanced by a detailed description of the cell types and experimental conditions, providing a computer-interpretable summary of the published data, integrated with the huge amount of protein interactions already gathered in the database. Furthermore, for each interaction, the binding sites of the microRNA are precisely mapped on a well-defined mRNA transcript of the target gene. This information is crucial to conceive and design optimal microRNA mimics or inhibitors to interfere in vivo with a deregulated process. As these approaches become more feasible, high-quality, reliable networks of microRNA interactions are needed to help, for instance, in the selection of the best target to be inhibited and to predict potential secondary off-target effects. Database URL https://www.ebi.ac.uk/intact., (© The Author(s) 2023. Published by Oxford University Press.)

The ASPREE randomized controlled trial (2010-2017) of 19,114 community-dwelling older adults without cardiovascular disease and significant disability compared daily 100mg aspirin to placebo. A total of 16,317 (93%) of 17,546 surviving and non-withdrawn participants agreed to continue regular study follow-up visits in the post-trial phase, named ASPREE-XT (2017-2024). We present a statistical analysis plan to underpin three main papers to report aspirin effects through to the fourth post-trial ASPREE-XT study visit with focus areas of: (1) death, dementia, and disability, (2) CVD events and bleeding, and (3) cancer. The focus of the plan is to estimate long-term (entire timespan of RCT plus post-trial) and legacy (post-trial period only) effects of aspirin in the setting of primary prevention for older individuals. Preliminary insights to these effects are presented that are based on data that has been reported to the study's observational study monitoring board however formal data lock is not expected until October 2023.

An unambiguous description of an experiment, and the subsequent biological observation, is vital for accurate data interpretation. Minimum information guidelines define the fundamental complement of data that can support an unambiguous conclusion based on experimental observations. We present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the parameters required for the wider scientific community to understand the findings of an experiment studying the structural properties of intrinsically disordered regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardize the dissemination of the key metadata on an IDR experiment by IDR data sources., (© 2023. Springer Nature America, Inc.)

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Borderline personality disorder (BPD) is characterized by dichotomous thinking, biases in processing emotion-related information, impulsive responding, and identity disturbance - each of which may affect how individuals respond to assessment items. This study used item-response theory tree models to examine the association between number of self-reported BPD symptoms and response style bias on the NEO Five Factor Inventory-3, administered annually from ages 18 to 20 to 2196 community participants enrolled in the Pittsburgh Girls Study. Small to moderate negative correlations emerged between number of BPD symptoms and midpoint responding. Small correlations emerged between BPD symptoms and extremity responding. These patterns did not vary by age. Participants who endorsed higher initial BPD symptoms reported lower extraversion over time, whereas those initially endorsing midpoint extroversion reported greater decreases in BPD severity. Low initial agreeableness was associated with higher BPD symptom slopes. The current study found only small associations between response style biases and BPD symptoms among a sample with subthreshold symptoms for diagnosis., Competing Interests: Conflict of Interest We can confirm that our manuscript meets the guidelines for ethical conduct and report of research. The information in this submission is original. It has not been published previously or posted on a website, nor is it being considered for publication elsewhere. We have no financial or other conflicts of interest to report. All authors have agreed to authorship in the indicated order and complied with APA ethical standards. Conflict of Interest The authors declare that they have no conflict of interest.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. It provides 10 searchable datasets and four main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries, such as literature citations, taxonomy, and subcellular locations, among others. This article focuses on how to use UniProt datasets. The first basic protocol describes navigation and searching mechanisms for the UniProt datasets, and two additional protocols build on the first protocol to describe advanced search and query building. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching UniProt datasets Basic Protocol 2: Advanced search and query building Basis Protocol 3: Adding parameters using advanced search., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2023). The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides four main tools. These are the "BLAST" tool for sequence similarity searching, the "Align" tool for multiple sequence alignment, the "Peptide Search" tool for retrieving proteins containing a short peptide sequence, and the "Retrieve/ID Mapping" tool for using a list of identifiers to retrieve UniProt Knowledgebase (UniProtKB) proteins and to convert database identifiers from UniProt to external databases or vice versa. This article provides four basic protocols and seven alternate protocols for using UniProt tools. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Basic local alignment search tool (BLAST) in UniProt Alternate Protocol 1: BLAST through UniProt text search results pages Alternate Protocol 2: BLAST through UniProt basket Basic Protocol 2: Multiple sequence alignment in UniProt Alternate Protocol 3: Align tool through UniProt results pages and entry pages Alternate Protocol 4: Align tool through UniProt basket Basic Protocol 3: Peptide search in UniProt Basic Protocol 4: Batch retrieval and ID mapping in UniProt Alternate Protocol 5: Retrieve/ID Mapping tool through UniProt text search results pages and BLAST and Align results pages Alternate Protocol 6: Retrieve/ID Mapping tool through UniProt basket Alternate Protocol 7: Retrieve/ID Mapping tool through UniProt search box., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

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., (© 2023. The Author(s).)

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 status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme-related information is spread across multiple resources making its retrieval time-consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme-related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme-related data the Enzyme Portal provides, how the information is organized and, by show-casing two potential use cases, how to access and retrieve it., (© 2021 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Objectives: Polypharmacy and frailty are two common geriatric conditions. In community-dwelling healthy older adults, we examined whether polypharmacy is associated with frailty and affects disability-free survival (DFS), assessed as a composite of death, dementia, or persistent physical disability., Methods: We included 19,114 participants (median age 74.0 years, IQR: 6.1 years) from ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. Frailty was assessed by a modified Fried phenotype and a deficit accumulation Frailty Index (FI). Polypharmacy was defined as concomitant use of five or more prescription medications. Multinomial logistic regression was used to examine the cross-sectional association between polypharmacy and frailty at base line, and Cox regression to determine the effect of polypharmacy and frailty on DFS over five years., Results: Individuals with polypharmacy (vs. <5 medications) were 55% more likely to be pre-frail (Relative Risk Ratio or RRR: 1.55; 95%Confidence Interval or CI:1.44, 1.68) and three times more likely to be frail (RRR: 3.34; 95%CI:2.64, 4.22) according to Fried phenotype. Frailty alone was associated with double risk of the composite outcome (Hazard ratio or HR: 2.16; 95%CI: 1.56, 2.99), but frail individuals using polypharmacy had a four-fold risk (HR: 4.24; 95%CI: 3.28, 5.47). Effect sizes were larger when frailty was assessed using the FI., Conclusion: Polypharmacy was significantly associated with pre-frailty and frailty at baseline. Polypharmacy-exposed frailty increased the risk of reducing disability-free survival among older adults. Addressing polypharmacy in older people could ameliorate the impact of frailty on individuals' functional status, cognition and survival., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the 'Support' link., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/., (© The Author(s) 2021. Published by Oxford University Press.)

The domain of transcription regulation has been notoriously difficult to annotate in the Gene Ontology, partly because of the intricacies of gene regulation which involve molecular interactions with DNA as well as amongst protein complexes. The molecular function 'transcription coregulator activity' is a part of the biological process 'regulation of transcription, DNA-templated' that occurs in the cellular component 'chromatin'. It can mechanistically link sequence-specific DNA-binding transcription factor (dbTF) regulatory DNA target sites to coactivator and corepressor target sites through the molecular function 'cis-regulatory region sequence-specific DNA binding'. Many questions arise about transcription coregulators (coTF). Here, we asked how many unannotated, putative coregulators can be identified in protein complexes? Therefore, we mined the CORUM and hu.MAP protein complex databases with known and strongly presumed human transcription coregulators. In addition, we trawled the BioGRID and IntAct molecular interaction databases for interactors of the known 1457 human dbTFs annotated by the GREEKC and GO consortia. This yielded 1093 putative transcription factor coregulator complex subunits, of which 954 interact directly with a dbTF. This substantially expands the set of coTFs that could be annotated to 'transcription coregulator activity' and sets the stage for renewed annotation and wet-lab research efforts. To this end, we devised a prioritisation score based on existing GO annotations of already curated transcription coregulators as well as interactome representation. Since all the proteins that we mined are parts of protein complexes, we propose to concomitantly engage in annotation of the putative transcription coregulator-containing complexes in the Complex Portal database., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Fine root endophytes (FRE) were traditionally considered a morphotype of arbuscular mycorrhizal fungi (AMF), but recent genetic studies demonstrate that FRE belong within the subphylum Mucoromycotina, rather than in the subphylum Glomeromycotina with the AMF. These findings prompt enquiry into the fundamental ecology of FRE and AMF. We sampled FRE and AMF in roots of Trifolium subterraneum from 58 sites across temperate southern Australia. We investigated the environmental drivers of composition, richness, and root colonization of FRE and AMF by using structural equation modelling and canonical correspondence analyses. Root colonization by FRE increased with increasing temperature and rainfall but decreased with increasing phosphorus (P). Root colonization by AMF increased with increasing soil organic carbon but decreased with increasing P. Richness of FRE decreased with increasing temperature and soil pH. Richness of AMF increased with increasing temperature and rainfall but decreased with increasing soil aluminium (Al) and pH. Aluminium, soil pH, and rainfall were, in decreasing order, the strongest drivers of community composition of FRE; they were also important drivers of community composition of AMF, along with temperature, in decreasing order: rainfall, Al, temperature, and soil pH. Thus, FRE and AMF showed the same responses to some (e.g. soil P, soil pH) and different responses to other (e.g. temperature) key environmental factors. Overall, our data are evidence for niche differentiation among these co-occurring mycorrhizal associates.