Obesity II: Establishing causal links between chemical exposures and obesity

Obesity is a multifactorial disease with both genetic and environmental components. The prevailing view is that obesity results from an imbalance between energy intake and expenditure caused by overeating and insufficient exercise. We describe another environmental element that can alter the balance between energy intake and energy expenditure: obesogens. Obesogens are a subset of environmental chemicals that act as endocrine disruptors affecting metabolic endpoints. The obesogen hypothesis posits that exposure to endocrine disruptors and other chemicals can alter the development and function of the adipose tissue, liver, pancreas, gastrointestinal tract, and brain, thus changing the set point for control of metabolism. Obesogens can determine how much food is needed to maintain homeostasis and thereby increase the susceptibility to obesity. The most sensitive time for obesogen action is in utero and early childhood, in part via epigenetic programming that can be transmitted to future generations. This review explores the evidence supporting the obesogen hypothesis and highlights knowledge gaps that have prevented widespread acceptance as a contributor to the obesity pandemic. Critically, the obesogen hypothesis changes the narrative from curing obesity to preventing obesity.

Obesity III: Obesogen assays: Limitations, strengths, and new directions

There is increasing evidence of a role for environmental contaminants in disrupting metabolic health in both humans and animals. Despite a growing need for well-understood models for evaluating adipogenic and potential obesogenic contaminants, there has been a reliance on decades-old in vitro models that have not been appropriately managed by cell line providers. There has been a quick rise in available in vitro models in the last ten years, including commercial availability of human mesenchymal stem cell and preadipocyte models; these models require more comprehensive validation but demonstrate real promise in improved translation to human metabolic health. There is also progress in developing three-dimensional and co-culture techniques that allow for the interrogation of a more physiologically relevant state. While diverse rodent models exist for evaluating putative obesogenic and/or adipogenic chemicals in a physiologically relevant context, increasing capabilities have been identified for alternative model organisms such as Drosophila, C. elegans, zebrafish, and medaka in metabolic health testing. These models have several appreciable advantages, including most notably their size, rapid development, large brood sizes, and ease of high-resolution lipid accumulation imaging throughout the organisms. They are anticipated to expand the capabilities of metabolic health research, particularly when coupled with emerging obesogen evaluation techniques as described herein.

The Exposome and Toxicology: A Win-Win Collaboration

The development of the exposome concept has been one of the hallmarks of environmental and health research for the last decade. The exposome encompasses the life course environmental exposures including lifestyle factors from the prenatal period onwards. It has inspired many research programs and is expected to influence environmental and health research, practices, and policies. Yet, the links bridging toxicology and the exposome concept have not been well developed. In this review, we describe how the exposome framework can interface with and influence the field of toxicology, as well as how the field of toxicology can help advance the exposome field by providing the needed mechanistic understanding of the exposome impacts on health. Indeed, exposome-informed toxicology is expected to emphasize several orientations including (1) developing approaches integrating multiple stressors, in particular chemical mixtures, as well as the interaction of chemicals with other stressors, (2) using mechanistic frameworks such as the adverse outcome pathways to link the different stressors with toxicity outcomes, (3) characterizing the mechanistic basis of long-term effects by distinguishing different patterns of exposures and further exploring the environment-DNA interface through genetic and epigenetic studies, and (4) improving the links between environmental and human health, in particular through a stronger connection between alterations in our ecosystems and human toxicology. The exposome concept provides the linkage between the complex environment and contemporary mechanistic toxicology. What toxicology can bring to exposome characterization is a needed framework for mechanistic understanding and regulatory outcomes in risk assessment.

Merging the exposome into an integrated framework for “omics” sciences

The exposome concept encourages holistic consideration of the non-genetic factors (environmental exposures including lifestyle) that influence an individual’s health over their life course. However, disconnect between the concept and practical application has promoted divergent interpretations of the exposome across disciplines and reinforced separation of the environmental (emphasizing exposures) and biological (emphasizing responses) research communities. In particular, while knowledge of biological responses can help to distinguish actual (i.e. experienced) from potential exposures, the inclusion of endogenous processes has generated confusion about the position of the exposome in a multi-omics systems biology context. We propose a reattribution of “exposome” to exclusively represent the totality of contact with external factors that a biological entity experiences, and introduce the term “functional exposomics” to denote the systematic study of exposure-phenotype interaction. This reoriented definition of the exposome allows a more readily integrable dataset for multi-omics and systems biology research.

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Reattribution of exposome concept to exclusively represent environmental exposures

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Generalized the exposome concept for all levels of biological organization

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Functional exposome presented as the totality of exposure-phenotype interaction

Emerging Bioinformatics Methods and Resources in Drug Toxicology

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach

BACKGROUND

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

OBJECTIVES

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

METHODS

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

RESULTS

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

CONCLUSIONS

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Development of a chemogenomics library for phenotypic screening

With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

AOP-helpFinder webserver: a tool for comprehensive analysis of the literature to support adverse outcome pathways development

MOTIVATION

Adverse outcome pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome.

RESULTS

Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently, a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks supporting the chemical risk assessment.

AVAILABILITY AND IMPLEMENTATION

AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of non-validated endocrine disrupting chemical characterization methods by screening of the literature using artificial intelligence and by database exploration

BACKGROUND

Exposure to endocrine disrupting chemicals (EDCs) represents a critical public health threat. Several adverse health outcomes (e.g., cancers, metabolic and neurocognitive/neurodevelopmental disorders, infertility, immune diseases and allergies) are associated with exposure to EDCs. However, the regulatory tests that are currently employed in the EU to identify EDCs do not assess all of the endocrine pathways.

OBJECTIVE

Our objective was to explore the literature, guidelines and databases to identify relevant and reliable test methods which could be used for prioritization and regulatory pre-validation of EDCs in missing and urgent key areas.

METHODS

Abstracts of articles referenced in PubMed were automatically screened using an updated version of the AOP-helpFinder text mining approach. Other available sources were manually explored. Exclusion criteria (computational methods, specific tests for estrogen receptors, tests under validation or already validated, methods accepted by regulatory bodies) were applied according to the priorities of the French Public-privatE Platform for the Pre-validation of Endocrine disRuptors (PEPPER) characterisation methods.

RESULTS

226 unique non-validated methods were identified. These experimental methods (in vitro and in vivo) were developed for 30 species using diverse techniques (e.g., reporter gene assays and radioimmunoassays). We retrieved bioassays mainly for the reproductive system, growth/developmental systems, lipogenesis/adipogenicity, thyroid, steroidogenesis, liver metabolism-mediated toxicity, and more specifically for the androgen-, thyroid hormone-, glucocorticoid- and aryl hydrocarbon receptors.

CONCLUSION

We identified methods to characterize EDCs which could be relevant for regulatory pre-validation and, ultimately for the efficient prevention of EDC-related severe health outcomes. This integrative approach highlights a successful and complementary strategy which combines computational and manual curation approaches.

[Endocrine Disruptors: what are we talking about and what new mecanisms of toxicity do they bring into play?]

WHAT ARE WE TALKING ABOUT AND WHAT NEW MECHANISMS OF TOXICITY DO THEY BRING INTO PLAY? Endocrine disruptors (EDs) are chemicals that can interfere with the functioning of the endocrine system and thereby cause an adverse event. They are suspected of being toxic to the environment and to humans and to increase the risk of developing pathologies such as cancer, metabolic, neurological or immune diseases. These substances are defined by their mechanisms of action which are now described as "Adverse Outcome Pathways" or AOPs. AOPs correspond to a logical chain of events leading to an adverse effect. EDs have properties which have modified our concepts in toxicology, in particular due to the low-dose effects of certain EDs, the possible effects of ED mixtures and finally their delayed effects over time, sometimes with years or decades that separate exposure and impact. Epigenetic mechanisms probably explain these delayed effects.

[What do we know about effects of the endocrine disruptors on metabolism and obesity?]

WHAT DO WE KNOW ABOUT EFFECTS OF THE ENDOCRINE DISRUPTORS ON METABOLISM AND OBESITY? Some endocrine disruptors (EDs) are suspected to be involved in the increase of the prevalence of obesity and metabolic diseases. Data from epidemiological, in vivo, in vitro and in silico studies suggest that EDs may exert their effects on numerous tissues involved in energy metabolism and in the regulation of appetite: adipose tissue, liver, muscle, pancreas, gut and hypothalamus. Their effects are due to: disruptions of the carbohydrate and lipid homeostasis in these organs, via the activation of specific nuclear receptors or transcriptional factors, disturbances in communication between these organs, and epigenetic mechanisms, involved for example in intergenerational effects. The characterization of the effects of EDs on endocrine systems is still under investigations in several European and international projects and initiatives, with the aim to establish new validated regulatory tests for ED identification.

Associations between Exposure to Organochlorine Chemicals and Endometriosis: A Systematic Review of Experimental Studies and Integration of Epidemiological Evidence

BACKGROUND

Growing epidemiological evidence suggests that organochlorine chemicals (OCCs), including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), may play a role in the pathogenesis of endometriosis.

OBJECTIVES

We aimed to systematically review the experimental evidence (in vivo and in vitro) on the associations between exposure to OCCs and endometriosis-related end points.

METHODS

A systematic review protocol was developed following the National Toxicology Program /Office of Health Assessment and Translation (NTP/OHAT) framework and managed within a web-based interface. In vivo studies designed to evaluate the impact of OCCs on the onset or progression of endometriosis and proliferation of induced endometriotic lesions were eligible. Eligible in vitro studies included single-cell and co-culture models to evaluate the proliferation, migration, and/or invasion of endometrial cells. We applied the search strings to PubMed, Web of Science, and Scopus®. A final search was performed on 24 June 2020. Assessment of risk of bias and the level of evidence and integration of preevaluated epidemiological evidence was conducted using NTP/OHAT framework Results: Out of 812 total studies, 39 met the predetermined eligibility criteria (15 in vivo, 23 in vitro, and 1 both). Most studies (n=27) tested TCDD and other dioxin-like chemicals. In vivo evidence supported TCDD's promotion of endometriosis onset and lesion growth. In vitro evidence supported TCDD's promotion of cell migration and invasion, but there was insufficient evidence for cell proliferation. In vitro evidence further supported the roles of the aryl hydrocarbon receptor and matrix metalloproteinases in mediating steroidogenic disruption and inflammatory responses. Estrogen interactions were found across studies and end points.

CONCLUSION

Based on the integration of a high level of animal evidence with a moderate level of epidemiological evidence, we concluded that TCDD was a known hazard for endometriosis in humans and the conclusion is supported by mechanistic in vitro evidence. Nonetheless, there is need for further research to fill in our gaps in understanding of the relationship between OCCs and their mixtures and endometriosis, beyond the prototypical TCDD. https://doi.org/10.1289/EHP8421.

AOP4EUpest: mapping of pesticides in adverse outcome pathways using a text mining tool

MOTIVATION

Exposure to pesticides may lead to adverse health effects in human populations, in particular vulnerable groups. The main long-term health concerns are neurodevelopmental disorders, carcinogenicity as well as endocrine disruption possibly leading to reproductive and metabolic disorders. Adverse outcome pathways (AOP) consist in linear representations of mechanistic perturbations at different levels of the biological organization. Although AOPs are chemical-agnostic, they can provide a better understanding of the Mode of Action of pesticides and can support a rational identification of effect markers.

RESULTS

With the increasing amount of scientific literature and the development of biological databases, investigation of putative links between pesticides, from various chemical groups and AOPs using the biological events present in the AOP-Wiki database is now feasible. To identify co-occurrence between a specific pesticide and a biological event in scientific abstracts from the PubMed database, we used an updated version of the artificial intelligence-based AOP-helpFinder tool. This allowed us to decipher multiple links between the studied substances and molecular initiating events, key events and adverse outcomes. These results were collected, structured and presented in a web application named AOP4EUpest that can support regulatory assessment of the prioritized pesticides and trigger new epidemiological and experimental studies.

AVAILABILITY AND IMPLEMENTATION

http://www.biomedicale.parisdescartes.fr/aop4EUpest/home.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The COVID-19 pandemic and global environmental change: Emerging research needs

The outbreak of COVID-19 raised numerous questions on the interactions between the occurrence of new infections, the environment, climate and health. The European Union requested the H2020 HERA project which aims at setting priorities in research on environment, climate and health, to identify relevant research needs regarding Covid-19. The emergence and spread of SARS-CoV-2 appears to be related to urbanization, habitat destruction, live animal trade, intensive livestock farming and global travel. The contribution of climate and air pollution requires additional studies. Importantly, the severity of COVID-19 depends on the interactions between the viral infection, ageing and chronic diseases such as metabolic, respiratory and cardiovascular diseases and obesity which are themselves influenced by environmental stressors. The mechanisms of these interactions deserve additional scrutiny. Both the pandemic and the social response to the disease have elicited an array of behavioural and societal changes that may remain long after the pandemic and that may have long term health effects including on mental health. Recovery plans are currently being discussed or implemented and the environmental and health impacts of those plans are not clearly foreseen. Clearly, COVID-19 will have a long-lasting impact on the environmental health field and will open new research perspectives and policy needs.

Drug-target-ADR Network and Possible Implications of Structural Variants in Adverse Events

Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the biological complexity of human systems, understanding the underlying mechanisms involved in development of ADRs remains a challenging task. Here, we applied network sciences to analyze a tripartite network between 1000 drugs, 1407 targets, and 6164 ADRs. It allowed us to suggest drug targets susceptible to be associated to ADRs and organs, based on the system organ class (SOC). Furthermore, a score was developed to determine the contribution of a set of proteins to ADRs. Finally, we identified proteins that might increase the susceptibility of genes to ADRs, on the basis of knowledge about genomic structural variation in genes encoding proteins targeted by drugs. Such analysis should pave the way to individualize drug therapy and precision medicine.

Endocrine disrupting chemicals and COVID-19 relationships: a computational systems biology approach

Background

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

Objectives

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

Methods

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

Results

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

Conclusions

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Development of an adverse drug event network to predict drug toxicity

Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

Integrative Strategy of Testing Systems for Identification of Endocrine Disruptors Inducing Metabolic Disorders-An Introduction to the OBERON Project

Exposure to chemical substances that can produce endocrine disrupting effects represents one of the most critical public health threats nowadays. In line with the regulatory framework implemented within the European Union (EU) to reduce the levels of endocrine disruptors (EDs) for consumers, new and effective methods for ED testing are needed. The OBERON project will build an integrated testing strategy (ITS) to detect ED-related metabolic disorders by developing, improving and validating a battery of test systems. It will be based on the concept of an integrated approach for testing and assessment (IATA). OBERON will combine (1) experimental methods (in vitro, e.g., using 2D and 3D human-derived cells and tissues, and in vivo, i.e., using zebrafish at different stages), (2) high throughput omics technologies, (3) epidemiology and human biomonitoring studies and (4) advanced computational models (in silico and systems biology) on functional endpoints related to metabolism. Such interdisciplinary framework will help in deciphering EDs based on a mechanistic understanding of toxicity by providing and making available more effective alternative test methods relevant for human health that are in line with regulatory needs. Data generated in OBERON will also allow the development of novel adverse outcome pathways (AOPs). The assays will be pre-validated in order to select the test systems that will show acceptable performance in terms of relevance for the second step of the validation process, i.e., the inter-laboratory validation as ring tests. Therefore, the aim of the OBERON project is to support the organization for economic co-operation and development (OECD) conceptual framework for testing and assessment of single and/or mixture of EDs by developing specific assays not covered by the current tests, and to propose an IATA for ED-related metabolic disorder detection, which will be submitted to the Joint Research Center (JRC) and OECD community.

Computational systems biology as an animal-free approach to characterize toxicological effects of persistent organic pollutants

Exposure to persistent organic pollutants (POPs), as defined by the Stockholm Convention, may alter biological systems and cause toxic effects. Computational studies appear to be a relevant approach to increase our understanding of the molecular mechanisms triggered by POPs. We investigated the use of a systems toxicology approach to explore the effects of POPs on human health. A protein-protein association network (PPAN) was developed based on known POP-protein interactions. This model was used to predict protein complexes for several candidate POPs, including dicofol, methoxychlor, and perfluorooctanoic acid (PFOA), that are listed or proposed to be listed as POPs by the Stockholm Convention. Integration of multiple data sources (pathways, disease annotations, adverse outcome pathways) involving the identified protein complexes was performed independently in order to reveal putative risk factors for human health. This approach revealed that several systems may be disturbed by these candidate POPs, mainly the reproductive, metabolic and nervous systems. This study highlights that a computational systems toxicology approach may help to decipher putative biological mecha­nisms of poorly studied chemicals and link them to possible adverse effects with the aim to support regulatory assessment and trigger new epidemiological and experimental studies. In order to develop more accurate computational models as alternative methods to animal testing, the next challenge will be to integrate more data according to the findable, acces­sible, interoperable and reusable (FAIR) data principles.