A novel insight into neurological disorders through HDAC6 protein-protein interactions.

Due to its involvement in physiological and pathological processes, histone deacetylase 6 (HDAC6) is considered a promising pharmaceutical target for several neurological manifestations. However, the exact regulatory role of HDAC6 in the central nervous system (CNS) is still not fully understood. Hence, using a semi-automated literature screening technique, we systematically collected HDAC6-protein interactions that are experimentally validated and reported in the CNS. The resulting HDAC6 network encompassed 115 HDAC6-protein interactions divided over five subnetworks: (de)acetylation, phosphorylation, protein complexes, regulatory, and aggresome-autophagy subnetworks. In addition, 132 indirect interactions identified through HDAC6 inhibition were collected and categorized. Finally, to display the application of our HDAC6 network, we mapped transcriptomics data of Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis on the network and highlighted that in the case of Alzheimer's disease, alterations predominantly affect the HDAC6 phosphorylation subnetwork, whereas differential expression within the deacetylation subnetwork is observed across all three neurological disorders. In conclusion, the HDAC6 network created in the present study is a novel and valuable resource for the understanding of the HDAC6 regulatory mechanisms, thereby providing a framework for the integration and interpretation of omics data from neurological disorders and pharmacodynamic assessments.

FAIR assessment of nanosafety data reusability with community standards.

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

Copy number variant risk loci for schizophrenia converge on the BDNF pathway.

OBJECTIVES: Schizophrenia genetics is intricate, with common and rare variants' contributions not fully understood. Certain copy number variations (CNVs) elevate risk, pivotal for understanding mental disorder models. Despite CNVs' genome-wide distribution and variable gene and protein effects, we must explore beyond affected genes to interaction partners and molecular pathways. METHODS: In this study, we developed machine-readable interactive pathways to enable analysis of functional effects of genes within CNV loci and identify ten common pathways across CNVs with high schizophrenia risk using the WikiPathways database, schizophrenia risk gene collections from GWAS studies, and a gene-disease association database. RESULTS: For CNVs that are pathogenic for schizophrenia, we found overlapping pathways, including BDNF signalling, cytoskeleton, and inflammation. Common schizophrenia risk genes identified by different studies are found in all CNV pathways, but not enriched. CONCLUSIONS: Our findings suggest that specific pathways - BDNF signalling - are critical contributors to schizophrenia risk conferred by rare CNVs. Our approach highlights the importance of not only investigating deleted or duplicated genes within pathogenic CNV loci, but also study their direct interaction partners, which may explain pleiotropic effects of CNVs on schizophrenia risk and offer a broader field for interventions.

Interactive neuroinflammation pathways and transcriptomics-based identification of drugs and chemical compounds for schizophrenia.

OBJECTIVES: Schizophrenia is a psychiatric disorder affecting 1% of the population. Accumulating evidence indicates that neuroinflammation is involved in the pathology of these disorders by altering neurodevelopmental processes and specifically affecting glutamatergic signalling and astrocytic functioning. The aim of this study was to curate interactive biological pathways involved in schizophrenia for the identification of novel pharmacological targets implementing pathway, gene ontology, and network analysis. METHODS: Neuroinflammatory pathways were created using PathVisio and published in WikiPathways. A transcriptomics dataset, originally created by Narla et al. was selected for data visualisation and analysis. Transcriptomics data was visualised within pathways and networks, extended with transcription factors, pathways, and drugs. Network hubs were determined based on degrees of connectivity. RESULTS: Glutamatergic, immune, and astrocytic signalling as well as extracellular matrix reorganisation were altered in schizophrenia while we did not find an effect on the complement system. Pharmacological agents that target the glutamate receptor subunits, inflammatory mediators, and metabolic enzymes were identified. CONCLUSIONS: New neuroinflammatory pathways incorporating the extracellular matrix, glutamatergic neurons, and astrocytes in the aetiology of schizophrenia were established. Transcriptomics based network analysis provided novel targets, including extra-synaptic glutamate receptors, glutamate transporters and extracellular matrix molecules that can be evaluated for therapeutic strategies.

WikiPathways 2024: next generation pathway database.

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome.

BACKGROUND: 22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood. RESULTS: Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different. CONCLUSIONS: Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability.

INTRODUCTION: The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. METHODOLOGY: To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. RESULTS: Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. CONCLUSIONS: This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

The FAIR Cookbook - the essential resource for and by FAIR doers.

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for "FAIR doers" in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations.

BACKGROUND: Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. METHODS: Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. RESULTS: The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis. CONCLUSION: The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Tissue-specific pathway activities: A retrospective analysis in COVID-19 patients.

The ACE2 receptors essential for SARS-CoV-2 infections are expressed not only in the lung but also in many other tissues in the human body. To better understand the disease mechanisms and progression, it is essential to understand how the virus affects and alters molecular pathways in the different affected tissues. In this study, we mapped the proteomics data obtained from Nie X. et al. (2021) to the pathway models of the COVID-19 Disease Map project and WikiPathways. The differences in pathway activities between COVID-19 and non-COVID-19 patients were calculated using the Wilcoxon test. As a result, 46% (5,235) of the detected proteins were found to be present in at least one pathway. Only a few pathways were altered in multiple tissues. As an example, the Kinin-Kallikrein pathway, an important inflammation regulatory pathway, was found to be less active in the lung, spleen, testis, and thyroid. We can confirm previously reported changes in COVID-19 patients such as the change in cholesterol, linolenic acid, and arachidonic acid metabolism, complement, and coagulation pathways in most tissues. Of all the tissues, we found the thyroid to be the organ with the most changed pathways. In this tissue, lipid pathways, energy pathways, and many COVID-19 specific pathways such as RAS and bradykinin pathways, thrombosis, and anticoagulation have altered activities in COVID-19 patients. Concluding, our results highlight the systemic nature of COVID-19 and the effect on other tissues besides the lung.

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions.

To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content.

Introduction: The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated. Materials and Methods: We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property-object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology. Results: The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org. Discussion: SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. Conclusion: Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow.

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites' variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules' landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org .

The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework.

Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Pathway (AOP) framework helps to organize existing mechanistic information and contributes to what is currently being described as New Approach Methodologies (NAMs). AOP knowledge and data are currently submitted directly by users and stored in the AOP-Wiki (https://aopwiki.org/). Automatic and systematic parsing of AOP-Wiki data is challenging, so we have created the EPA Adverse Outcome Pathway Database. The AOP-DB, developed by the US EPA to assist in the biological and mechanistic characterization of AOP data, provides a broad, systems-level overview of the biological context of AOPs. Here we describe the recent semantic mapping efforts for the AOP-DB, and how this process facilitates the integration of AOP-DB data with other toxicologically relevant datasets through a use case example.

A systematically derived overview of the non-ubiquitous pathways and genes that define the molecular and genetic signature of the healthy trabecular meshwork.

PURPOSE: The trabecular meshwork (TM) is situated in the most frontal part of the eye and is thought to play an important role in the regulation of the eye pressure. However, this tissue is rather difficult to harvest for research. The purpose of this study is therefore to integrate the existing gene expression data of the healthy TM to increase sample size and identify its signature genes and pathways. This provides a robust reference for the study of molecular disease processes and supports the selection of candidate target genes for new treatments. METHODS: A systematic search identified microarray data of healthy TM tissue. After quality control, datasets of low quality and deviating samples were excluded. Remaining individuals were jointly normalized and integrated into one database. The average gene expression of each tested gene over all individuals was calculated. The 25% genes with the highest average expression were identified as the most active genes in the healthy TM and used as input for pathway and network analysis. Additionally, ubiquitous pathways and genes were identified and excluded from the results. Lastly, we identified genes which are likely to be TM-specific. RESULTS: The gene expression data of 44 individuals, obtained from 18 datasets, were jointly normalized. Ubiquitous genes (n = 688) and ubiquitous pathways (n = 73) were identified and excluded. Following, 1882 genes and 211 pathways were identified as the signature genes and pathways of the healthy TM. Pathway analysis revealed multiple molecular processes of which some were already known to be active in the TM, for example extracellular matrix and elastic fiber formation. Forty-six candidate TM-specific genes were identified. These consist mainly of pseudogenes or novel transcripts of which the function is unknown. CONCLUSIONS: In this comprehensive meta-analysis we identified non-ubiquitous genes and pathways that form the signature of the functioning of the healthy TM. Additionally, 46 candidate TM-specific genes were identified. This method can also be used for other tissues that are difficult to obtain for study.

Impact of In Utero Folate Exposure on DNA Methylation and Its Potential Relevance for Later-Life Health-Evidence from Mouse Models Translated to Human Cohorts.

SCOPE: Persistent DNA methylation changes may mediate effects of early-life exposures on later-life health. Human lifespan is challenging for prospective studies, therefore data from longitudinal studies are limited. Projecting data from mouse models of early-life exposure to human studies offers a tool to address this challenge. METHODS AND RESULTS: C57BL/6J mice were fed low/normal folate diets before and during pregnancy and lactation. Genome-wide promoter methylation was measured in male offspring livers at 17.5 days gestation and 28 weeks. Eight promoters were concurrently hypermethylated by folate depletion in fetuses and adults (>1.10 fold-change; p < 0.05). Processes/pathways potentially influenced by global changes, and function of these eight genes, suggest neurocognitive effects. Human observational and randomized controlled trial data were interrogated for translation. Methylation at birth was inversely associated with maternal plasma folate in six genes (-1.15% to -0.16% per nmol L-1 ; p < 0.05), while maternal folic acid supplementation was associated with differential methylation of four genes in adulthood. Three CpGs were persistently hypermethylated with lower maternal folate (p = 0.04). CONCLUSION: Some persistent folate-induced methylation changes in mice are mirrored in humans. This demonstrates utility of mouse data in identifying human loci for interrogation as biomarkers of later-life health.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.