The future of food and nutrition in ELIXIR

Workshop “ELIXIR Food and Nutrition Community Workshop”

The workshop was organised on the 23^rd and 24^th of September 2019 in The Hague (The Netherlands). The invitation to the workshop was widely advertised through the newsletter of the NuGO, the ENPADASI, Richfields and FNH-RI mailing lists, and ELIXIR dissemination channels, including ELIXIR Technical Coordinators, Heads of Node mailing lists and the ELIXIR newsletter.

The workshop included 26 participants from across 14 countries (NL, DE, BE, SI, UK, SE, DK, ES, CH, IT, FR, FI, EE, IE) representing the ELIXIR Nodes, including the ELIXIR Hub, and additional countries. The objective of the meeting was to identify the principal challenges in food and nutrition and prioritise actions, in particular those within the scope and mission of ELIXIR. The workshop showcased flash presentations on the (inter) national F&N activities, ideas, and requirements that an ELIXIR F&N Community could address. An overview of ELIXIR Use Cases and Platforms was also presented by a representative of the ELIXIR Hub. The ELIXIR Training Platform was presented by one of the Training Platform coordinators. The presentations were followed by discussions on the needs and challenges present in the F&N community. The following challenges were identified:

These F&N challenges can only be efficiently resolved if a number of bioinformatic challenges are overcome:

Data availability

These challenges are described in more detail in the following sections.

Food and nutrition challenges

Individual health concept

Being healthy as opposed to having a disease, is generally not the way healthiness is defined today. It has been accepted by the World Health Organization that being healthy also includes physical, mental and social well-being. This indicates the need for new ways to quantify health status and include relevant data on well-being and in healthy people. How can we go from general health advice to subgroup or even to personal advice, linking with a human data use case? Can we use dynamic data for this? Can we set up methods to measure health status in relation to nutrition?

In order to measure health we need to operationalize the concept of health and to make it measurable. In the F&N community this is often done by challenge tests to probe the individual resilience to a specific health challenge. The idea behind challenge tests is that a dynamic response is telling more about the status of the underlying processes than only the baseline values. Challenge tests are therefore a way forward in quantifying individual health.

Ideally, we would like to know all relevant aspects of health or at least the most important ones for general metabolic balance but this may seem like a difficult target to reach. Using omics, it is potentially possible to measure multiple responses to a complex health challenge and to span the individual responses across many metabolic pathways covering the human as well as the microbial composition and metabolism. This approach would circumvent the need for pre-defining all aspects of health while allowing actual variability in health to be determined. Exposing individuals to a multitude of different nutritional challenges is therefore a prerequisite for measuring nutritional health. A diet is in fact such a multifaceted health challenge and extracting information on health status is therefore likely embedded in many multi-omics nutritional datasets. For example, the variation of gut microbiome composition, often in response to external cues such as diet,² and its far-reaching effects on the host in highly prevalent disorders is widely documented in the literature.^3–6 In addition, the microbiome is emerging as an important determinant of metabolic responses to food and a driver for inter-individual variability in metabolic health biomarkers.^7,8 For a more meaningful interpretation of the mechanisms underlying this two-way interplay and for translating microbiome research into effective benefits for the host, a correlation with other -omics profiles, such as metabolome⁹ or host transcriptomes,^10,11 through physiological and pathological conditions, is required. Relatively few such datasets have been shared and open access to varied nutritional data is therefore an important goal. For this purpose, both data on diseased and healthy citizens are needed. This case study is focused on connecting the human nutritional datasets available in the F&N community to the datasets available in ELIXIR (including the BBMRI and ECRIN data) that are more focused on diseased subjects.

Nutrigenomic biomarkers are needed to clarify association between the personal genetic background, phenotype, food, and nutrition. Additionally, interactions between drugs/pharmacotherapy/toxicology and food/nutrition should be investigated.^12,13

To accomplish this, datasets from complex nutritional challenge tests and dietary interventions covering multiple foods, nutrients and common food additives and toxicants are needed and need to be integrated in smart ways with the current knowledge. Work in this area has been initiated years ago in collaborative projects under NuGO, however a generalized model for data interpretation has not yet been developed. Currently additional steps are being made in the projects FNS-cloud and JPI HDHL INTIMIC knowledge platform (JPI KP).

A considerable number of highly controlled meal studies and dietary intervention studies exist (e.g. see Phenotype database and trials registries) and data from these studies are in principle decided by the consortia to be FAIR; however, they are not yet fully FAIRified and efforts to provide such datasets including related rich omics data are therefore a major priority. However, rich metadata for data intensive omics, such as the microbiome, are an issue, in particular as comprehensive (rich) diet-related metadata are challenging to obtain routinely in nutritional intervention studies. In the JPI KP rich meta-data on the nutritional studies are integrated with metabolomics and microbiome data. This type of data (metabolomics and microbiome) are also available within ELIXIR and solutions are available to share them. Integrating the Phenotype database with these solutions, e.g. Metabolights would improve the current solutions and would make the nutritional data available to ELIXIR.

In addition, omics analysis software (and hardware) for optimal data extraction, standardization, integration, and analytics tools to address the individualized response across multiple pathways are needed for the F&N community. Several of these tools exist, but some are fragmented or in an immature form and need to be joined to comprehensive workflows, where ELIXIR may help.

Individual dietary needs

Beyond defining individual health and resilience there is a need to develop methods to deliver dietary advice at an individual level to improve health. Data integration from a multitude of sources and different data types is crucial in understanding the individual human phenotype and what is optimal for his/her health. This challenge requires integration of data on behaviour, dietary intake, health-related endpoints, biometric dynamics, images, and omics data. Combining this information from a rich data source could provide a prediction of the longer-term health effect of a specific food or dietary intake even at the level of an individual.

Understanding and measuring food intake and its dynamics is central for developing effective lifestyle interventions. It is also relevant for the food industry, which has agreed to work with EU member states to make food supply healthier through food reformulation. Several tools and eHealth solutions relevant to a broader community and food industry have been developed. For instance, RICHFIELDS, JPI HDHL Foodball, ENPADASI, EIT Food, and FNS-Cloud have delivered the standardized requirements and ontologies¹⁴ that were needed to unambiguously describe the determinants of consumers' food choices, the composition of food and dietary intake (including biomarkers of intake) as well as the subsequent metabolic effects of food components in the human body. A diversity of tools is needed to integrate (personal) data that can be linked to dietary, health, and behavioural (consumer) reference data and ontologies. To give personal advice, a combination of user-friendly apps with sensors to capture non-invasive or minimally invasive biometric signals in real time are needed, that are able to onboard individuals and keep them connected and motivated. Individual data will be generated that can be used to give personalized advice but can also be used from research. For secondary use of data, consent is needed, unless data are completely anonymized. To adhere to privacy legislation federated learning solutions may be needed. To support the reuse of these individual data, the infrastructure to support this is being developed where personal health and consumer study data will be stored safely (FNH-RI). ELIXIR has the knowledge and tooling that can help to develop the solution mentioned.

Complex intake biomarker

All the above challenges require data on what people have eaten and their responses. Many self-reported questionnaires/diaries exist to enable collection of these data and some solutions are developed to make this data interoperable. However, it is important to recognise the limitations associated with such self-reported data: participants tend to under-report their food intake and the approaches are burdensome on participants thus resulting in issues with incomplete data.^15,16 Therefore, there is a great need for molecular biomarkers for food intake (BFIs) to provide stronger tools for intake assessment and understand the relationship to biomarkers of health and disease. A considerable number of such biomarkers have been identified^17,18 and in the JPI FoodBAll project a systematic approach was used to define,¹⁹ search²⁰ and validate²¹ BFIs and to introduce elements of an ontology for the area.²² Moreover, several food challenge studies were conducted to find new candidate BFIs for the most common foods in Europe, covering specific meats,^23,24 fruits,^25,26 vegetables,²⁷ legumes,²⁸ and several others. However, biomarkers for many foods have still not been proposed and many of the candidate biomarkers published are still not fully validated. An example of an important aspect of validation, which is often missing in the work on BFIs is the quantity of the food ingested; the current methodology is largely qualitative and combining data across several studies could be a facile way to improve this aspect of validation. To develop and validate this type of biomarker rich standardized questionnaires on intake, as the (not-so-) gold standard, and metabolomics data (as potential source of the biomarkers) are often used,²⁹ but as more and more markers are identified they may also be further refined by biomarker combinations.^26,30 Work on biomarker approaches to assess whole diets have also been advanced in recent projects and could develop into another set of tools in the intake biomarker toolbox.^31–33 However, tools to search information related to biomarker validation and rich databases of nutritional studies with associated metabolomics data are needed to reach a coverage of the diet with appropriate biomarkers to allow improved assessment of dietary intakes and compliance. These data need also to be made interoperable, both in terms of the metadata provided with studies and the information needed to identify the compounds identified as potential BFIs. This requires rich databases of mass spectral and other compound information that can be searched and used for verification. Some of this work has been started by other international players and some of it in the JPI HDHL Foodball project, but the solutions and resources require further solutions for interoperability. The interaction with ELIXIR may make further development possible especially by connecting to the MetaboLights initiative or similar repositories.

Standardization/interoperability

A key issue is to understand the relationships between consumer behaviour, food intake, and nutritional status to help consumers to choose a healthy diet. This information would also be helpful to commercial companies in the development of more healthy foods and dietary solutions. These types of data are collected by dietary monitoring systems in interventions and observational studies conducted across Europe using a standardized methodology. Important in this data collection is storage of relevant metadata on the study design as well as the methods used. Moreover, standards for dietary assessment, measurements for dietary intake and nutritional status are needed for comparability and quality of data, e.g. to start the discussion about FAIR (Findable, Accessible Interoperable and Reusable) food data. Guidelines to improve reporting of dietary assessment and nutrition research will help to re-use existing evidence for public health recommendations.³⁴ Relevant ontologies for nutrition knowledge can guide users towards better use and appraisal of research findings.³⁵

In the last decades, a great amount of work has been done in predictive healthcare using Artificial Intelligence methods as a result of the existence of publicly available biomedical vocabularies and standards together with tools for standardization of health-related data. Despite the large number of resources in the health domain, the food and nutrition domain is still low-resourced. There exist only a few food ontologies that are developed for a specific application scenario, with a small number of studies that are focused on exploring relations between different ontologies and standards. For this reason, the workshop “Big Food and Nutrition Data Management and Analysis - BFNDMA” was initiated at the 2019 IEEE International Conference on Big Data (Los Angeles, USA) and is an ongoing initiative, with a focus on methodologies for management and analysis of food and nutrition data.

The project RICHFIELDS started working on standardization of food items that are described and classified using different standards, by presenting a method known as StandFood³⁶ that is a synergy of Natural Language Processing and Machine Learning in order to standardize and classify foods with regard to the FoodEx2 provided by the European Food Safety Agency (EFSA). The method focuses only on identifying lexical similarity between the English names of the food items. Further, this work continues as a part of the current project FNS-Cloud, where different food semantic standards (i.e. Hansard taxonomy, Food-On, OntoFood, and SNOMED-CT) have been explored for food data annotations. The results showed that not all ontologies relevant for F&N are in place and that the current ontologies do not have a good coverage.³⁷ Based on the results, the FoodOntoMap,³⁸ a semantic resource was created, which provides links between different food ontologies that can be further reused to develop applications for understanding the relation between food systems, human health, and the environment. Additionally, the FoodBase³⁹ corpus is one of the first annotated recipe corpuses with food entities standardized using the Hansard taxonomy. To make this information available for subject-matter experts, the FoodViz⁴⁰ tool has been presented as a visualization tool for presenting automatically annotated food-related textual data, where subject-matter experts can check the automatically annotated results and also make corrections (i.e. manual annotations). In this way, textual data related to food, nutrition, and health, as a typical example of unstructured big data being collected from different data sources, can be handled.

Together with ELIXIR, the F&N Community will standardize, connect, and model the data pipeline. Furthermore, standardisation in food behaviour data (why do I eat what I eat), especially food choice motives, will provide tooling to create unique data pipelines based on linkage between food intake and its determinants including contextual variability and will prevent high measurement errors and time-consuming data collection. All tools and services will also become available for the wider ELIXIR community.

One of the aims of the interaction between ELIXIR and the F&N Community may be to unify the RICHFIELDS, ENPADASI and FNS-Cloud requirements and ontologies and link them with other standardised ones (provided through ELIXIR). Standardisation in consumer and human nutrition science is mandatory to exchange data and to monitor and analyse food behaviour. Data gathered by the ELIXIR Implementation Study “A microbial metabolism resource for Systems Biology”, are of special interest in this respect, as they can be exploited to integrate metagenomics data with the human nutritional phenotype. The effect of food in modulating the human gut microbiome is broadly recognized, but standardized tools for exploring big data at the interface between nutrition and the microbiome for self-sustainability of health are still fragmented and lack acceptable levels of standardization and integration.

The F&N Community is now using a number of profiling tools to explore the relationship of nutritional change with biological outcomes. This clearly aligns the area with a number of topics covered in ELIXIR. This includes data processing and analysis tools, databases for proteomics, genomics, mass spectral information and microbiomics. Especially for development of biomarkers related to health, dietary exposures and biological effects there is a good prospect for cross-fertilization of the food and nutrition area with several other topical areas within ELIXIR.

Data availability

Some of the datasets in the F&N Community are already well structured, but not yet fully FAIR. For example, ENPADASI has developed three templates for data collection covering all types of study design, study content and study objectives. The project has collected 111 intervention studies (of which 27 studies were made open access) and 23 observational studies. Those studies include clinical chemistry data, transcriptomics, genomics, metabolomics and microbiome data. As databases the Phenotype database and the Opal/Mica systems were used. Datashield was used to integrate data from the different systems and the FAIR principles were considered as much as possible. Minimal requirements for nutritional data sharing and information on studies needed for quality appraisal were developed, these requirements will make sure that all information relevant to judge the study quality are shared, where possible. In addition, based on the templates and uploaded studies nutritional terms were identified that were mapped to existing ontologies and new ontologies are being developed for nutritional terms (ONS) and nutrition epidemiology (Ontology for Nutritional Epidemiology). As indicated, we need to connect to other data sources and knowledge to fulfil the goals in the community. This requires full implementation of FAIR and Omics related standards in ELIXIR.

RICHFIELDS worked towards solutions to collect data on dietary habits. This includes privacy sensitive data. Therefore, RICHFIELDS has examined how the GDPR Regulation addresses these privacy matters. RICHFIELDS provides a framework for the design of the ethical and legal aspects and includes the following recommendations: (1) use of pseudonymisation with appropriate safeguards for unauthorised reversal of pseudonymisation; (2) use of appropriate technical and organisational measures to ensure GDPR compliance; (3) systems for dealing with queries and requests from data subjects; (4) appointment of a Data Protection Officer (DPO); (5) mechanisms for handling freedom of information (FOI) requests; (6) use of suitable data protection clauses for trans-border data transfer; (7) obtaining insurance to cover liability in the event of data breaches; and (8) the establishment of an independent ethics committee with the remit to monitor the activities, protocols on matters relating to security, transfer of data to third countries, assessing genuineness of requests from data users and procedures for dealing with ethically suspect requests, and procedures for handling requests from data subjects. This framework may be integrated with the solutions on this matter in ELIXIR.

Data reuse

The above-described F&N challenges all require complex interaction of diverse data sets. This integration will require knowledge networks, specific tools and algorithms development and analysis pipelines and may require hardware (cloud solutions). Although some Food and Nutrition knowledge networks and analysis pipelines are in place (e.g. Micronutrients Wikipathways, NutriGenomeDB), many are still lacking or require better generalization for reuse. Currently the F&N community has no cloud solution available, which may be needed especially if microbiome data (or other datasets that require large computation volume) are being integrated. Several of the datasets needed in the community will be private data, for which explicit consent is needed if the data is reused. Solutions for requesting secondary data reuse are therefore important and need to be outlined to improve ease of application.

Data Platform

The ELIXIR request for 2021 Data Platform priorities focuses on sustaining Europe’s life science data infrastructures in the long term by working on guidelines and indicators to improve the impact of data resources and their long-term sustainability. Additionally, this platform aims to improve links between curated and non-curated data resources and literature.

The F&N Community realised early on that they need a study capturing tool that can capture the full study design, large study outcome data sets from for instance transcriptomics studies and rich phenotype descriptions and that also offers an analysis platform for integrated data analysis for related studies. The Phenotype database was developed for this purpose after evaluating other study capture environments available at that time. These alternatives, especially ISA-creator, were not able to capture study designs typical for the domain (cross-over studies) and did not have the necessary support for food intake registration and the nutritional phenotype in general. The continued development of the phenotype database was supported in the ENPADASI project which also allowed development of a food and nutrition ontology that can be used to capture most aspects that are specific to the field.¹⁴ Further development of that ontology will happen in the new Food Nutrition-Security Cloud project (FNS-Cloud).

Study capture databases like the phenotype database basically are project level databases that come in between the capturing of individual studies happening in the departments, clinics, metabolic wards, and laboratory environments and the scale of technology specific to ELIXIR recommended data repositories and Biosamples/Biostudies. Other examples are Molgenis (used in the rare disease field), ISA-tools and FAIRdom. We think there are important challenges in the integration of study level data capturing. Discussions with especially Molgenis, have indicated that co-development of a template system and software libraries that support ontology term selection based on combinations of free text entry and pull-down menus would be really useful and would in practice lead to collection of more interoperable data. Increased data interoperability and the ability to find and access data between instances of study capture databases would also allow federated analysis across such instances.

Tools Platform

The Tools Platform drives access and utilisation of bioinformatics research software by working closely with services and connectors. Additionally, this Platform aims to facilitate the discovery (bio.tools, EDAM), benchmarking (OpenEBench) and interoperability of bioinformatics software, by focusing on software development best practices (4OSS), and on strategy for workflows and software containers (BioContainers).

The tools provided and used by the F&N Community will be registered in the bio.tools registry, and related concepts will be added to EDAM if needed. Training in software best practices and development of Software Management Plans will help the community with providing high-quality computational tools.

Interoperability Platform

The Interoperability Platform provides support to the discovery, integration and analysis of biological data based on FAIR principles. A set of recommended tools and services have been selected by the Interoperability platform and they are named Recommended Interoperability Resources, including persistent identifiers, metadata, and data markups (Bioschemas), standard for workflow description, registries for ontologies, controlled vocabularies, exchange and storage formats. This Platform facilitates work on the description of interoperability services and organises specialised BYOD (Bring Your Own Data) workshops with the aim to improve the FAIRness of data resources.

F&N data must be interoperable in order to align with other data sources to be able to answer the complex scientific challenges. Extension of the current metadata standards toward food and consumer science is needed to make this possible. Part of this has been included in ENPADASI and Richfields, but full integration with other interoperability platforms is lacking and black areas are still present.

Compute Platform

The Compute Platform is devoted to the compute, transfer, storage, authentication, and authorization related to biological data relying on services provided by ELIXIR Nodes and other e-infrastructures.

Key elements of the Compute Platform are:

It doesn't directly provide resources but can help in brokering access.

The interaction with the compute platform will allow the F&N Community to process an increasing number of relevant experimental data (i.e. omics) using standardized bioinformatics pipelines and statistical analyses. Therefore, the resulting data structure will be suitable for comparison, integration and modelling. The computing power necessary for the analyses that the F&N Community needs for the development and application of nutrition health care models will be huge. The Community should have a helpdesk that knows the access points for the Compute Platforms and how to use them.

Training Platform

Training is a key component of the sustainability of a community. We will make all training material available for the ELIXIR Community to collect data on food composition, dietary assessment, and food related behaviour of highest scientific quality to support food intake studies in surveillance, nutritional interventions and clinical studies. For new food and health web-based and mobile applications new training material will be developed (based on a structured gap analysis on the training needs). Training on the use of measuring behaviour, dietary intake, food matching and imaging algorithms and tools, and their limitations will be made accessible. Moreover, the training packages developed by ENPADASI on nutritional data upload and by RICHFIELDS and ENPADSI on guidelines in relation to ethics, privacy and IP with a focus on data sharing will be made part of the ELIXIR training portfolio.

Courses from the F&N Community will be registered in TeSS (the ELIXIR Training portal) and first steps will be taken to define learning paths considering the different starting points of the users. On the other hand, ELIXIR courses, such as those on tools and services related to data management and stewardship will be necessary to the F&N Community. Special attention, also due to COVID-19 challenges, should be focused on e-learning materials, tools and services and virtual or hybrid training/CB events. ELIXIR training platform services are listed on the Platform’s website.

Connection with innovation and SME forum

Omic approaches are becoming a reality also at the company level and not only in the academic field. A rapid and cost-effective and on-site technology represents the gateway to the application of (nutri-)genomics directly in food production and nutritional screening supporting technology transfer. This has led to a positive increase to data production and access by private citizens (e.g. microbiome screening) but also sometimes an incorrect use of analytical techniques, with a potential long-term impact on data reliability. The guidelines for correct data and pipeline integration may derive from the connection between the F&N Community and the SME forum in order to collect the real needs of companies, in terms of training, capacity building and research lines.”

Alignment with ELIXIR communities and focus groups

The ELIXIR Platforms are currently complemented by 11 scientific Communities, here we indicate the Communities and focus groups that are most relevant to the F&N community:

The F&N Community is in several ways in line with the other communities defined so far in ELIXIR and requires similar data solutions, but it also adds new data sources and new solutions to the current ELIXIR Communities. For instance, the F&N Community adds a consumer perspective to the current Communities in ELIXIR. Working with consumer data has privacy and ethical issues that are in line with the other communities (especially the rare diseases because of the individual data and the possibility of de-anonymization). Interestingly people also collect health and food-intake related data with smart phones and other personal devices, including sensors to capture non or minimally invasive biometric signals in real time. Individual digital health is becoming a revolution. Advances in sensor design, smart device connectivity and data acquisition help to keep track of parameters such as food intake, calories burnt or activity levels to complement self-reporting dietary electronic notebook.⁴¹ In addition, smart devices can be used to send meal photographs, notifications and reminders to complete tasks providing additional data to evaluate diet adherence or consumer experience.⁷ This is expected to become more relevant and especially more used in the F&N field rapidly. For ELIXIR this means involvement in a new and modern data field that wasn’t covered so much up to now. The individual data collection needed for this is in line with the work of the Rare Diseases Community.

The current Federated Human Data Community is directed towards patients, which could create mutual enrichments by aligning with the data on healthy free-living individuals that are collected in the F&N Community. To make this possible, alignment of standards is needed.

The relation between dietary intake, microbiome and health is becoming more and more obvious. Standardization of metagenomics research is needed to bring the research to the next level. The Marine Metagenomics (Microbiome) Community is actively working in the area and the F&N Community can benefit from their developments. Links to workflows, sequence reference resources and tools for taxonomic/genetic profiling of microbiomes developed by the Microbiome Community are relevant to the F&N Community. For example, ELIXIR-Italy provided amplicons and shotgun metagenomic data analysis tools, DNA barcoding reference databases and recently a virtual research environment targeted in particular at eukaryotic microbial communities. In the context of this connection, specific sections in the described IT resources could be dedicated to nutrition-related microorganisms (such as gut microbes, food production chain microbes, probiotics…). In this respect links to the Biodiversity Focus Group and the Microbiome Community are relevant as well. For example, the increasing connection of nutritional -omics datasets will contribute to the identification of unbiased microbial biomarkers. This is difficult, given the intrinsic nature of the microbiome and its genetic pool. In contrast to the human inherited, largely static and “non-coding” genome, the metagenome is gained and has a very dynamical composition in response to a multitude of factors. Often viewed as hosting only pathogens, causing disease, or as passive bystanders, it is now observed that the microbiome has a wide ‘grey zone’ that cannot be simply classified into this dichotomy. Many ‘pathogens’ inhabit disease free-hosts (e.g., Helicobacter pylori) and also commensals may promote pathology onset under certain conditions (e.g. immuno depressed or highly stressed subjects). Because of this high variability between individuals, it is very difficult to establish what the normal condition is, what dysbiosis is and whether the latter is associated with an unhealthy phenotype⁴² or diet. For this, the F&N Community has proposed some general metrics, such as alpha species diversity, the ratio of Firmicutes to Bacteroidetes phyla, and the relative abundance of beneficial genera versus facultative anaerobes or pro-inflammatory microbes.⁴³

The role of the microbiome also spans beyond the microbiome composition and requires functional analysis. A central question is whether we can predict and model the metabolic activities in the gut starting from available, mostly sequence, data. In a practical sense that leads to evaluation of DNA and RNA sequences to predict the presence of enzymatic activity on the protein level using recognition of translated domains and mapping to microbiome genome scale metabolic models and combination with metabolomics data. This has clear links with the Microbial Biotechnology and Metabolomics Communities and for the complex integration of these omics data and the personalization of nutritional interventions with the machine learning community and also to the currently active Systems Biology Focus Group.

Understanding the effect of food on health requires measures of intake and of health; metabolomics platforms can deliver these markers as has been shown in the JPI HDHL project, Foodball. Therefore, a link to the Metabolomics Community is very important to the F&N Community. Linking to other communities is also important; for example, linking to the plant science community is crucial because of the interrelationship between plant nutrients and their effects on human metabolism and health. Large-scale transcriptomics studies demonstrate that edible plants, also including their derivatives such as olive oil, can induce significant changes in the human gene expression profile including many ncRNAs (i.e. miRNAs and lncRNAs) that are key regulators of gene expression. It is noteworthy that plant nutrients have been demonstrated to have a positive impact on many signalling and metabolic pathways related to diabetes, obesity, neurodegenerative disorders and in general to the immune response to stress factors and inflammation. At cell process level plant nutrients have been demonstrated to have positive effects (i.e. gene expression) on different DNA repair mechanisms, apoptosis, oxidative phosphorylation and mitochondrial metabolism, just to cite only the most relevant. To elucidate the molecular mechanisms and the plant nutrients able to induce such beneficial effects on human health requires an effective integration of the Plant Science, Metabolomics and F&N Communities at level of competences, data resources and analysis tools. The F&N Community members have partly implemented the FAIR principles in ENPADASI and have developed ontologies, quality assessment tools, and standardized questionnaires that have been made publicly available as much as possible.

The F&N Community brings data on consumer science and ways to collect them to ELIXIR. Moreover, we bring ways to collect rich meta-data e.g. on cross-over designs, healthy phenotypes, challenge studies, protocol sets, dietary data and dietary questionnaires.

Alignment with other ESFRIs

The F&N community is aligned with several other ESFRI; see Table 1 for these interactions.

Related projects

A number of projects have previously developed bioinformatics tools for the Food & Nutrition field. These may also become for the ELIXIR Food & Nutrition Community. Some of these projects are producing data and/or tools which can be used by the F&N Community (see Table 2).

Data and tools developed by the F&N community

Table 3 shows an initial overview of data and tools that the F&N community has available at the time being. During the upcoming Implementation Study this will be extended.