Foodomics as part of the host-microbiota-exposome interplay

Journal of Proteomics 147 (2016) 3–20

Contents lists available at ScienceDirect

Journal of Proteomics journal homepage: www.elsevier.com/locate/jprot

Review

Foodomics as part of the host-microbiota-exposome interplay Lorenza Putignani a,⁎, Bruno Dallapiccola b a b

Units of Parasitology and Human Microbiome, Bambino Gesù Children's Hospital and Research Institute, Piazza Sant'Onofrio 4, 00165 Rome, Italy Scientific Directorate, Bambino Gesù Children's Hospital and Research Institute, Piazza Sant'Onofrio 4, 00165 Rome, Italy

a r t i c l e

i n f o

Article history: Received 16 January 2016 Received in revised form 10 April 2016 Accepted 20 April 2016 Available online 26 April 2016 Keywords: Data integration Systems biology Systems medicine Panomics Phenomics Foodomics

a b s t r a c t The functional complexity of human gut microbiota and its relationship with host physiology and environmental modulating factors, offers the opportunity to investigate (i) the host and microbiota role in organismenvironment relationship; (ii) the individual functional diversity and response to environmental stimuli (exposome); (iii) the host genome and microbiota metagenomes' modifications by diet-mediated epigenomic controls (nutriepigenomics); and (iv) the genotype-phenotype “trajectories” under physiological and disease constraints. Systems biology-based approaches aim at integrating biological data at cellular, tissue and organ organization levels, using computational modeling to interpret diseases' physiopathological mechanisms (i.e., onset and progression). Proteomics improves the existing gene models by profiling molecular phenotypes at protein abundance level, by analyzing post-translational modifications and protein–protein interactions and providing specific pathway information, hence contributing to functional molecular networks. Transcriptomics and metabolomics may determine host ad microbiota changes induced by food ingredients at molecular level, complementing functional genomics and proteomics data. Since foodomics is an -omic wide methodology may feed back all integrative data to foster the omics-based systems medicine field. Hence, coupled to ecological genomics of gut microbial communities, foodomics may highlight health benefits from nutrients, dissecting dietinduced gut microbiota eubiosis mechanisms and significantly contributing to understand and prevent complex disease phenotypes. Biological significance: Besides transcriptomics and proteomics there is a growing interest in applying metabolic profiling to food science for the development of functional foods. Indeed, one of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide, intrinsically respecting the high inter-individual variability, driven by complex host/nutrients/microbiota/environment interactions. Therefore, metabolic profiling can assist at various levels for the development of functional foods, starting from screening for food composition to identification of new biomarkers to trace food intake. This current approach can support diet intervention strategies, epidemiological studies, and controlling of metabolic disorders worldwide spreading, hence ensuring healthy aging. With high-throughput molecular technologies driving foodomics, studying bidirectional interactions of hostmicrobial co-metabolism, innate immune development, dysfunctional nutrient absorption and processing, complex signaling pathways involved in nutritional metabolism, is now likely. In all cases, as microbiome pipeline efforts continue, it is possible that enhanced standardized protocols can be developed, which may lead to new testable biological and clinical hypotheses. This Review provides a comprehensive update on the current state-of-the-art of the integrated -omics route in food, microbiota and host co-metabolism studies, which may revolutionize the design of new dietary intervention strategies. © 2016 Elsevier B.V. All rights reserved.

Contents 1.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. High-throughput omics data for current biology and medicine . . . 1.2. Systems medicine: from genotype to phenotype beyond the genome Systems medicine: from genotype to phenotype beyond the metagenome . 2.1. The human microbiome: the major internal exposome player . . . 2.2. -Omics-based consortia and clinical applications . . . . . . . . .

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⁎ Corresponding author at: Unit of Parasitology and Unit of Human Microbiome, Bambino Gesù Children's Hospital, IRCCS, Piazza Sant'Onofrio 4, Rome 00165, Italy. E-mail address: [email protected] (L. Putignani).

http://dx.doi.org/10.1016/j.jprot.2016.04.033 1874-3919/© 2016 Elsevier B.V. All rights reserved.

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3.

The foodomics in the new systems medicine . . . . . . . . 3.1. The interplay between microbiota, nutrients and diet 3.2. Global foodomics strategies . . . . . . . . . . . . 4. Conclusions. . . . . . . . . . . . . . . . . . . . . . . Declaration of conflicts of interest . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction 1.1. High-throughput omics data for current biology and medicine Large scale sequencing projects have decrypted the genomes for a wide number of species including humans, thus entering biological sciences into the post-genomic era. In fact, once the genome has been deciphered, the succeeding challenge consists in determining the molecular bases of the “phenotypes”. This exciting biological research is now feasible because new technological platforms are enabling widerange high-throughput analyses. These improvements are revolutionizing the investigation methods from the conventional “reductionist” approach, endowed with a limited number of molecular targets analyzed at a given time, to the more comprehensive “olistic” approach, whereby the analysis of the entire molecular sample content may allow a global drawing of the biological “system”. Recently, the systems biology has become a distinct “discipline”. Particularly, in the biomedical sciences this trend is growing as research moves from the reductionist to the “systems understanding” paradigm that attempts to understand biology and pathophysiology in an integrate way, making use of both rapidly available novel data (−omics) and other relevant quantitative biological/medical information. Therefore, the current emerging perspective is shifting bio-medical investigations from the so called “hypothesis driven” research to the “hypothesis generating” or “data-driven research”, based on “big data” generation by –omics disciplines. The advent of additional methods suitable for simultaneous evaluation of large numbers of biomolecules has generated a series of investigations (i.e.,-omics), even described by openfree web portals (Table 1). The regulome analysis may assist in interpreting the entire DNAprotein molecular machinery (e.g., regulatory elements such as genes, mRNAs, proteins, and metabolites); the mutome make-up provides the description of the whole sets of nuclear gene mutations, associated with human diseases; the epigenome profile offers the classification of chemical changes of DNA and histone proteins, altered by environmental conditions and acting as genome regulatory players; the exome analysis assembles mature RNAs after intron removal by RNA splicing; the transcriptome outline catalogues all RNAs sets, including mRNA, rRNA, tRNA, and other non-coding RNA produced by cell populations. Hence, the transcriptome, downstream the whole genomic machinery, includes the whole population of RNA molecules translated into proteins, definitely guarantying the entire cell flow paradigm from “genotype to individual phenotype” (Fig. 1). The development of proteomic and metabolomic technologies has allowed to investigate genome products (i.e., proteome and metabolome) in deepness, by analyzing the whole proteins' core pattern, the metabolite profiling and metabolic maps, also looking at biomolecule glycosylation (glycomics) and at the global variety of lipid content (lipidomics). 1.2. Systems medicine: from genotype to phenotype beyond the genome The -omics integration, i.e. “systems biology”, is a multidisciplinary approach encompassing biology, chemistry, physics, informatics. Application of “systems biology” to medical research and practice does generate “systems medicine”, aiming at integrate a variety of biological/ medical data using the power of computational modeling, to enable understanding of the pathophysiology mechanisms, prognosis, diagnosis

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and treatment of disease. The derived integrative algorithms may highlight functions and interactions of the whole biological systems in a dynamic range. However, omics- data and integration methods need to be standardized through shared rules for biobanks' generation, −omicsbased profiling, normalization of heterogeneous datasets and quality filtering [1]. There is a growing need to develop comprehensive, centralized reference resources for clinical communities, including reference biobanks collecting biofluids such as blood, urine, saliva and faecal sample (Table 1) [1]. Based on standardized procedures it will be possible to identify biomarkers, using testing and validation sets [1] (Fig. 2). However, to move from the laboratory to clinical medicine applications it is mandatory to integrate data across different levels of biological organizations, addressing key aspects of organismal and vertical biology, including: (1) organism organizational levels; (2) organism role in organism-environment linkages; (3) organisms' functional diversity; (4) organisms' generation from genomes; (5) organisms' selection through stability and changes during evolutionary changes; and (6) organisms' role within ecosystems [2,3]. Therefore, the “integration” across levels of biological organization is the conceptual solution to an appropriate interpretation of the flow from genes to ecosystems across time and space, under different ecological constraints (e.g., earth's climate, pollution, habitat change, invasive species [4]). To accomplish this aim, a few disciplines have been identified as leading, including “macrophysiology”, to vertically integrate ecology with physiological ecology [5], “functional genomics” to integrate gene regulation with physiology [6], “ecological genomics” to apply molecular techniques to the study of ecology [7–9]. These disciplines have actually founded the global systems biology, accumulating early individual and population data and generating the first comprehensive physiological and clinical phenotyping charts [10]. More recently, metabolomics, proteomics have started to complement genomics, opening the “post-genomic era”. Despite the spectacular advances in this field, a gap still exists between experimental data and medical knowledge, particularly when “new knowledge” is evaluated in terms of clinical utility and benefit to the patient. As a result, despite major technological advances, some obstacles are still separating systems biology from medical applications. Systems medicine has the aim of bridging this gap, and identify the bottlenecks preventing the translation of systems biology to the clinic. However, in order for systems medicine to become a medical practice, scientific and clinical communities need to have a coordinated vision of all databases, well constructed and annotated as the Human Genome Project (HGP) (Table 1). The creation of a strong networking effort among systems biology projects is therefore essential to share information/resources with the broader communities (Fig. 2), in order to integrate biological/medical data at all relevant levels of cellular organization using the power of computational modeling. A number of clinical requirements shoud be the drivers for the applications of systems medicine, including: i) systems biology-based clinical trials; ii) re-definition of clinical phenotypes, based on molecular and dynamic parameters; iii) discovery of multiple nature biomarkers for disease progression (clinical risk, prognosis, diagnosis); iv) combinatorial therapy (e.g., combination and drop of doses of effective drugs, in particular in co-morbidities); v) improvement of drug development (e.g., drug efficacy, safety and delivery; therapy timing and dosage; vi) maintenance of individual health. This new medicine promishes to be particularly effective in the understanding of chronic complex diseases

Table 1 Main openfree databases employed for –omics data analyses. Principal –omics databases for data analysis and integration

Genome

Encyclopedia of DNA Elements (ENCODE) Project http://www.genome.gov/encode/ BioGrid, http://www.thebiogrid.org

Gene Ontology Consortium http://geneontology.org/

MRM, http://www.mrmatlas.org

Peptide Atlas, http://www.peptideatlas.org

Metabolome/ Hmdb, http://www.hmdb.ca/ metabonome Microbiome Clusters and communities, http://cfinder.org/ Immunome VirHostNet, http://virhostnet.prabi.fr/

Serum metabolome, http://www.serummetabolome.ca/ Escherichia coli, http://ccdb.wishartlab.com/CCDB/ Immunome, http://bioinf.uta.fi/Immunome/

E. coli metabolism http://ecmdb.ca/

Yeast metabolome, http://www.ymdb.ca/ Bacterial genomes, Candida genome database, http://wishart.biology.ualberta.ca/BacMap/ http://www.candidagenome.org/ Immunome, Macrophages, http://structure.bmc.lu.se/idbase/ http://www.macrophages.com/ Immunome/index.php immunome-database

Microbial genome database, http://mbgd.genome.ad.jp/ Cancer Immunity, http://cancerimmunity.org/ resources/other-databases/

Phenome

OMICS.org, http://omics.org/index.php/ Main_Page EPIGENIE, http://epigenie.com/ epigenetic-tools-and-databases/

UK Biobank, http://www.ukbiobank.ac.uk Tomato Epigenome, http://ted.bti.cornell.edu/epigenome/

Sharing personal genomes, http://www.personalgenomes.org ROADMAP, http://www.roadmapepigenomics.org/

MarkerDB, http://www.markerdb.ca/ IHEC, http://ihec-epigenomes.org/ links/

Exposure Biology and the Exposome, https://www.niehs.nih.gov/research/ supported/exposure/bio/ Infectome, http://www.infectome.org/software.html

EXPOsOMICS, http://www.exposomicsproject.eu/

The human early life human exposome http://www.projecthelix.eu/ Infectious diseases counter, http://lmart999.github.io/2015/02/ 10/infectome/

ChEMBL, https://www.ebi.ac.uk/chembl/compound

FooDB, http://en.wikipedia.org/ wiki/Foodb

CDC, Exposome and Exposomics http://www.cdc.gov/niosh/ topics/exposome/ Ferret database, http://www.ferretscience.org/ 2012/02/ferret-transcriptomeproject.html Dairy metabolome, http://www.cowmetdb.ca/ cgi-bin/browse.cgi

Proteome

Epigenome

Exposome

Neuropsychiatric Phenomics, http://www.phenomics.ucla.edu Ncbi epigenomics, http://www.ncbi.nlm.nih.gov/ epigenomics Human Exposome Project, http://humanexposomeproject.com/

Infectome

GIDEON database, http://web.gideononline.com/web/ diagnosis/index.php

Foodome

NutriChem, http://www.cbs.dtu.dk/services/ NutriChem-1.0/

Virus pathogen resource, http://www.viprbrc.org/brc/ home.spg?decorator=vipr

mRNA Assembler http://www.ferretscience.org/ 2012/02/iliad-assembler.html IntAct protein interaction database, http://www.ebi.ac.uk/intact

De novo assembly SOAPdenovo http://soap.genomics.org.cn/ soapdenovo.html Peptide Sieve, http://tools.proteomecenter.org/ wiki/index.php?title=Software: PeptideSieve SMPDB, http://smpdb.ca/

Human Genome project http://www.genome.gov/10001772

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Omics matrix

Wageninge tools, http:// www.wageningenur.nl/en/ Expertise-Services/ Research-Institutes/rikilt/ Foodomics-at-RIKILT.htm (continued on next page)

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Table 1 Main openfree databases employed for –omics data analyses. Table 1 (continued) Principal –omics databases for data analysis and integration

BIGG Models, http://bigg.ucsd.edu/ Human Microbiome Project, https://commonfund.nih.gov/ hmp/databases Immunome Knowledge Base (IKB) http:// g6g-softwaredirectory.com/bio/ cross-omics/dbs-kbs/ 20412UTampereIKB.php Mouse phenome, http://phenome.jax.org/ EpiFactors, http://epifactors.autosome.ru/ Exposome, https://en.wikipedia.org/ wiki/Exposome Infectome and Autoimmunity, http://cirrie.buffalo.edu/ database/189545/ Phenol explorer, http://phenol-explorer.eu/

EBI, http://www.ebi.ac.uk/genomes/

PhospoSitePlus http://www.phosphosite.org/ homeAction.do;jsessionid= 210062D616661FB3AC595F4475D3D0EA MetaCyc, http://metacyc.org/ DACC, http://hmpdacc.org/ resources/data_browser.php Meta Keys, http://www.universitieshandbook.com/ directory/search-by/meta-keys?value= Cancer%20Immunome%20Database

KEGG genome database, http://www.genome.jp/kegg/ genome.html PRIDE http://www.ebi.ac.uk/ pride/archive/

Reactome, http://www.reactome.org/ HOMD, http://www.homd.org/

Cancer Immunome, http://www.biologydir.com/ cancer-immunome-databaseinfo-550.html

Phenome Networks, J-Phenome, http://phnserver.phenome-networks.com/ http://jphenome.info/ ?page_id=702&lang=en EPIC, NOE, http:// https://www.plant-epigenome.org/links www.epigenome-noe.net/ researchtools/protocols.php.html MyExposome, The exposome, http://www.myexposome.com/#about http://nas-sites.org/ emergingscience/ meetings/exposome/ Infectome, http://www.infectome.org/ https://www.facebook.com/viprbrc/

Food composition database, http://www.eurofir.org/?page_id=96

HUGO, http://www.genenames.org/ useful/genome-databases-and-browsers

Gene Ontology Annotation, http://www.ebi.ac.uk/GOA

Eukaryotic genome annotation, http://www.ncbi.nlm.nih.gov/ genome/annotation_euk/process/ Trans Proteomic Pipeline (TPP) http://tools.proteomecenter.org/ wiki/index.php?title=Software:TPP

X!Tandem protein identification software http://www.thegpm.org/ TANDEM

ChEBI, http://www.ebi.ac.uk/chebi/

Biocyc, http://biocyc.org/

Human Gutome database, http://gutome.com/

GIGADB, http://gigadb.org/dataset/ 100064

IEDB, http://www.iedb.org/

IEDB, analysis resources, http:// tools.iedb.org/main/analysis-tools/

Inter phenome portal, http://www.interphenome.org/

RIKEN, http://rarge-v2.psc.riken.jp/ phenome/

PhenoM, http://phenom.ccbr.utoronto.ca/

IHEC, http://crest-ihec.jp/ english/database/index.html

Blueprint, http://dcc.blueprintepigenome.eu/#/home

EPITRANS, http://epitrans.org/EPITRANS/Service

MRC-PHE, http://www.environment-health.ac.uk/ our-research/exposome-and-health

Public Health Exposome, http://communitymappingfor healthequity.org/ public-health-exposome-data/ GItHub, https:// github.com/pelkmanslab/Infectome

Mapping the human exposome, http://www.futurepostponed.org/ blog/2015/7/22/ mapping-the-human-exposome InfectX, http://www.infectx.ch/databrowser

INFOODS, http:// www.fao.org/infoods/infoods/ tables-and-databases/en/

ESHA, http://www.esha.com/ nutritional-database/

PINA, http://csbi.ltdk.helsinki.fi/pina

PubChem, https://pubchem.ncbi.nlm.nih.gov/ Integrated tools, http://meta.genomics.cn/metagene/ meta/dataTools CID, http://omictools.com/ cancer-immunome-database-tool

Influenza database, http://www.fludb.org/brc/ home.spg?decorator=influenza Food composition database, CalorieKing, http://www.foodcomposition.co.nz/ http://www.calorieking.com/foods/

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Ensembler, http://www.ensembl.org/ index.html PhospoPep MS http://omictools.com/ phospopep-tool

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Fig. 1. Individual genotype–phenotype interactions uncovered by –omics integrated strategies. The flow between genome machinery and phenomic traits is underlined showing the entire downstream complex of molecules regulating DNA and translated into proteins, hence guarantying the genotype-phenotype flow paradigm.

(e.g., cancer, diabetes, obesity, metabolic disorders, aging), through network analysis of disease processes and identification of individualtargeted biomarkers for early diagnosis and prognosis, substantially boosting predictive personalized medicine. Within this scenario an emerging central role is played by the mass spectrometry (MS), an analytical tool for bio-molecules' characterization in either proteomics and metabolomics fields. Proteomics is the large-scale study of all proteins resident in the analytical specimen, not only providing the profiles of expressed gene products but also the actual protein metabolic status, including their post-translational modifications distinctive of cell type, tissue or organism, and/or pathophysiological conditions. Proteins are vital parts of living organisms, as they are the main components of the cell physiological metabolic pathways and actually represent the phenotype performers. Indeed, proteomics can be used to improve existing gene models, to profile molecular phenotypes at the levels of protein abundance, post-translational modifications (PTMs), and protein-protein interactions, and to obtain specific pathway information using targeted MS strategies. As for other ‘omics’ strategies, the challenge lies in obtaining knowledge from the collected data. A promising approach includes the reconstruction of functional molecular networks through the integration of high-quality information from functional genomics and proteomics data (Table 1) [11]. Techniques for the successful integration of large data sets have been proposed and reviewed recently [12,13]. Few examples exist that show the value of integrating genetic information with MS-based proteomics data. This is the case of functional dissection of the small ubiquitin-related modifier (SUMo) system [14]. Yeast SUMo pathway mutants were subjected to synthetic genetic array (SGA) screening against a genome-wide collection of viable yeast mutants. The resulting genetic interaction data were integrated with a proteomic data set, which included known SUMo conjugation targets and data from affinity purification (AP)–MS analyses. The analysis of these integrated data led to the development of a molecular network model for the SUMo pathway that is linked to more than 15 cellular processes [14]. A few pioneering studies have further integrated proteomics and functional genomics data for subsequent network inference analyses, improving the prediction of cancer phenotypes [15,16]. This approach was based on the assumption that the modularity of oncogenic

networks is altered in transformed cells. The authors used curated protein interactions and large mRNA expression data sets to identify coexpression patterns of protein hubs and their interacting partners in groups of patients with good or poor prognosis of breast cancer. They identified 256 hub proteins with an altered gene expression correlation of their binding partners in the patients with a poor breast cancer prognosis, suggesting that altered network modularity can be used as a prognostic signature for cancer. In this respect, the increasing coverage of human protein–protein interaction data sets by future large-scale AP– MS studies will increase the predictive performance of such integrative approaches, through proper annotation and accessibility of MS data through public databases (Table 1). Several initiatives have led to standardize machine-readable formats for the description of MS experiments using controlled vocabularies and the exchange of MS data [17, 18] for either processed and raw data accessible through proteomics databases (Table 1). These standardizing efforts allow the efficient integration of MS experimental data into existing networks of genotype and phenotype databases, filling the genotype–phenotype gap [19]. Panomics refers to the complex combination of genes, proteins, molecular pathways, and unique patient characteristics, as well as the development of preventive and curative strategies. Phenomics is the science of large-scale phenotypic data collection and analysis, whereas the phenome is the actual catalogue of measurements: integrating proteomic and phenomic data fosters clinical medicine, using panomics as driver of changes to interpret clinical phenotypes. Therefore, phenomics should be recognized and pursued as an independent discipline to enable the adoption of high-throughput phenotyping, associating single genetic changes to more than one affected phenotype (i.e., pleiotropy) [20]. Examples of current phenome projects include i) Human Consortium for Neuropsychiatric Phenomics, Northern Finland Birth Cohorts, collecting genomic data, brain structure, function and behavior in case–control studies for psychiatric syndromes' phenotypinggenotyping; ii) UK Biobank, prospective study of 500,000 individuals, Department of Health, Wellcome Trust, including baseline questionnaire and physical measurements, blood and urine samples for analysis and integration with UK NHS health records; iii) Personal Genome Project recruiting volunteers for genome sequencing and phenotype data to collect images, cell lines and medical history (Table 1). Data obtained

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Fig. 2. Integration between omics- matrices and omics-based information types constitute the algorithm able to describe different levels of individual phenotypes. Different levels of genetic information (i.e., genomic sequence, gene structure), under different transcriptional and epigenomic constraints, are associated to specific panomic conditions, which determine protein expression patterns and cell biochemical pathways. The -omics-derived data, generated by different platforms, are normalized, filtered for quality controls and recorded into free-access databases.

from integrative phenomics will feed back to other ‘omics’ and all levels of phenotype, therefore improving their clinical and scientific value. Indeed, genome-wide associations studies (GWSA) have been heralded as a major advance in biomedical discovery, highlighting more than 2000 associations with complex diseases, but the modest size of associated variants has been characterized only by functional clinical features highlighted by phenomics [21]. Concerning advanced technologies, various platforms have contributed to HGP and ENCODE Projects' annotation (Table 1). Among the others, remarkable technological platforms are: i) RNA-SEQ [22], CAGE [23], RNA-PET [24], ChIRP-Seq [25], GRO-Seq [26], NET-seq [27]), RiboSeq, [28] for transcripts' analysis; ii) ChIP-seq, [29], DNAse footprinting [30], DNAse-seq, [31], FAIRE [32], Histone modification [33]) for transcriptional machinery and protein–DNA interactions; iii) RRBS (Reduced Representation Bisulfite Sequencing) for DNA methylation [34]; iv) HT sequencing [35] and ChIA-PET [36,37] for analysis of chromosome interacting sites (Fig. 3). These genome, epigenome and transcriptome approaches are complemented by protein expression patterns, metabolite profiling and metabolite maps [38]. The sets of data generated by these approaches are essential to capture small variations among comparative groups or to detect new unsuspected associations through the entire genotype-phenotype coverage. Although large databases are already widely used to capture social trends, large databases in medical research are just emerging. With the universal use of electronic medical records, vast amounts of health-related information will become available for biomedical research, harboring great potential for advancing knowledge of complex digestive [39] or pediatric diseases [40,41]. However, although the ‘typical’ phenotypic outcome of an individual's genome can be predicted, it is much more difficult to foresee the actual outcome for a particular individual because the outcome of mutations is influenced by stochastic processes. In addition, the genetic variation in one generation can

influence the next generation phenotype, and the environment experienced by one generation can influence the phenotypic variation in the next generation, regardless the absence of variation inheritance. Therefore, the individual genotype and the environment may not be sufficient to determine the phenotype. Since humans can be considered as ‘superorganisms’ with an internal ecosystem of diverse symbiotic microbiota and parasites [42], which is highly variable among individuals [43], the implications of this ecosystem in health and disease are central and can explain transfer of complex traits from parent to offspring by nongenetic inheritance, clearly demonstrating that parental influence goes beyond inherited genes [44]. Since the concept of “superorganism” has diffusely overcome the concept of organism [45,46], the definition of direct or indirect effects of microbiota on human diseases needs appropriate tools, such as meta-omics, taking into account the complexity of the microbial communities [46]. The “exposome” conceptually describes all environmental factors, both exogenous (i.e., food, infectious agents) and endogenous (i.e., microbiota), which we are exposed to in the lifetime (Fig. 3). The exposome represents an important concept also in the study of autoimmunity, complementing classical immunological tools and cutting-edge genome wide association studies (GWAs). Recently, environmental wide association studies (EWAS) have investigated the effect of the environment in the development of diseases. Environmental agents, classified into infectious and non-infectious agents [47], constitute the infectome, directly related to geo-epidemiological, serological and molecular factors, which act as co-occurrence agents in autoimmune diseases, associated to microbiota/host interactions. Finally, the disease-specific infectome can be related to immunome and microbiome (Table 1) [48]. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. Networking the Human Infectome and Diseasome

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unravels the connectivity of viruses to a wide range of diseases, the molecular basis of Hepatitis C virus-induced diseases, as well as thirty-eight genetic predisposition factors involved in type 1 diabetes mellitus. With this purpose, genotype-phenotype associations have been already reported for microorganisms in the GIDEON database [49] (Table 1). This tool allows to correlate presence/absence of microbial proteins with specific phenotypes, associated to biochemical and culture-based features, translating the approach of genome-phenome annotation’ Projects from humans to microbes [50]. 2. Systems medicine: from genotype to phenotype beyond the metagenome 2.1. The human microbiome: the major internal exposome player Specific determinants of variability, related to the host and environment [51,52], act directly on the composition and density of gut microbiota shortly after birth, defining the functional efficiency of the newborn intestine and its metabolic needs [46,53]. The first microbial acquisition appears to be ruled by a vertical transmission from mother to child and only after birth the different microbial ecosystems mature in the various anatomical sites [54]. Microbiology studies, complemented by metagenomic analyses, have established that the first facultative anaerobic microbes do create a suitable environment for the development of the strictly anaerobic metabolism [55]. In this way, the microbial ecosystem of the host selects a group of well-adapted communities, which originate from the colonizing community, while the genetic host features influence the composition of the ecological niches [56]. The increasing availability of systems biology approaches in the study of intestinal microbiota, have progressively generated tools for the analysis of “functional” microbiota in pediatric diseases. These approaches include the understanding of the milk feeding or weaning on the symbiotic balance of microbial ecosystems, but also the evaluation of ecological eubiosis/ dysbiosis conditions [1]. In fact, diet is one of the most important determinants of microbial diversity in the gastrointestinal tract and its effects, in the context of different individual enterotypes, have been widely investigated [57,58]. Diet-driven bacterial populations can, in turn, influence the physiological performances of the human host as it has been demonstrated for breast feeding, which enriches the newborn gut microbiota in the Lactobacilli components [59,60]. Nowadays, the gut microbiota can be analyzed in its whole “ecological” complexity of “microbial organ” located in the superorganism, and can be characterized by a dynamic interaction with the host and the food (Fig. 4). The exhaustive knowledge of the intestinal microbiota in the early stages of life, especially immediately after birth, has major effects in neonatal and pediatric disciplines as milk feeding, weaning and next healthy nutrition play a central role in the physiological programming of neonatal gut microbiota, really affecting next phases of child growth and development. In addition, it is known that some diseases expressed at extraintestinal tract, such as obesity and atopy, are associated with major changes of the gastrointestinal microbial ecosystem [61]. For cystic fibrosis (CF), recurrent respiratory infections and chronic inflammation seem to have dynamic correlations with the gut microbiota, including its association with dysbiosis grading [62]. The high expression of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene in the intestine [63] favors the abnormal mucus production, resulting in malabsorption and bowel obstruction. Consequently, patients with untreated CF display nutritional deficiency and poor prognosis. Several factors predispose CF patients to microbiota disbiosis, including: i) accumulation of ceramide; ii) abnormal mucus thickness and poor intestinal motility; iii) endogenous intestinal inflammation iv) antibiotic therapy; v) low level of pancreatic secretions; vi) altered pH and low level of carbohydrate fermentation. It has been shown that the accumulation of ceramide, a transmitter of cellular signaling and mediator of differentiation, proliferation and cell death, is associated with increased susceptibility to pulmonary

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infections by Pseudomonas aeruginosa in mice knockout for CFTR, in consequence of deposition of apoptotic epithelial cell DNA on the surface of the mucosa, triggering inflammatory process and bacterial overgrowth [64]. Given that the dysregulation of ceramide homeostasis is induced by absence or by modifications of CFTR, it is plausible that ceramide accumulates in the human intestinal tract, where CFTR is strongly expressed, as demonstrated in CF mice [65]. Similarly, the repeated use of antibiotics could alter the ecological homeostasis of the intestinal microbiota, increasing the local adhesion of pathogenic bacteria, including P. aeruginosa, even if the correlation between antibiotics and dysbiosis level needs to be demonstrated. In particular, systems biology investigations on lung and gut microbiota diversity and stability in CF patients will be crucial in predicting clinical outcome, improving the nutritional status and quality of life, in the development of alternative or additional therapies based on the use of probiotics and prebiotics, with immediate outcomes on the daily treatment of the patient. Individuals suffering from inflammatory bowel syndromes (IBS) show substantial changes in the composition of the microbiota: the amount of Bacteroides and Bifidobacteria is significantly reduced while the quantities of Bacilli, Lactobacilli and Streptococci are increased [66]. Ecology of microbial populations can also provide useful information on gut microbiota. Conflicts between younger and older generations occur in communities constituted by organisms with a defined vital cycle. It is well known that the natural selection tends to eliminate the older individuals who have overcome the peak of the reproductive age, in order to assure the resources to the future generations [67]. In fact, populations displaying an increased percentage of elderly individuals are unstable and therefore fragile [68]. To overcome this problem, nature has created biological clocks that keep track of each individual position in the aging matrix and are phylogenetically preserved [69–71]. One clock type includes bifunctional genes (i.e., antagonist pleiotropy) that are useful for young individuals, but may be a burden with age [72]. To explain their role, two models have been proposed: either the selection occurs only in the reproductive age [73] or affects all individuals, through its effects on resource endowment and population structure [74]. Subgroups of the endogenous microbiota may have bifunctional genes inducing a congruence between host and bacterial phylogenies, which allow hostbacteria co-evolution processes [75,76]. If the microbial genes produce adaptive functions during the reproductive age, they may reduce competition for resources and promote the resilients' fraction of the population structure [77,78]. For instance, the gastric bacterium Helicobacter pylori could play such a role. From H. pylori infections the host obtains an advantage in the early stages of the bacterium growth, thus resisting to the infections triggered by other pathogenic bacteria (e.g., diarrheal and tuberculosis agents) [79,80]. During the post-reproductive life, H. pylori is associated with a logarithmic increase conforming with mortality from gastric cancer [80]. This bifunctional relationship could justify the ubiquity of this bacterium [80], at least until antibiotics' administration. Due to the fact that elderly people, as infants, have more diversified gastrointestinal microbiota than adults, the bi-functionality of their microbiota species awaits for being further investigated [81,82]. However, specific species beneficial in childhood and in the pediatric age (e.g., Lactobacilli and Bifidobacteria), if transplanted, could be harmful to elderly populations. These issues have important implications in the design of the probiotic therapies. Probably, healthy elderly people, rather than young, could be the best donor of probiotics for other elderly people, because their microbiota is much diversified compared to that of young subjects [81,82]. 2.2. -Omics-based consortia and clinical applications A network of researchers and clinicians at the Bambino Gesù Children's Hospital and Research Institute, Rome, connected with other academic National and International Institutions, is generating a reference biobank of faecal samples for the study of pediatric diseases associated with intestinal dysbiosis. The Consortium is producing

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Fig. 3. Genomic, epigenomic, transcriptomic, proteomic and metabolomic data generate phenome profiling, under major exposome variability stimulus represented by foodome, infectome and microbiome. Different levels of genomic data can be structured by single-nucleotide polymorphism (SNP), copy number variation (CNV), loss of heterozygosity (LOH) and genomic rearrangement, such as translocation, with generation of rare genomic variants. DNA methylation, histone modification, chromatin accessibility, transcription factor (TF) binding and micro RNA (miRNA) extert regulation at the epigenome level; gene expression rate and alternative splicing act at transcriptome level; protein expression rate and post-translational modification operate at proteome level, occurring by acetylation, methylation, ubiquitination, phosphorylation, including inflammasome patterns; metabolite profiling and metabolite maps modifications affect metabolome biochemistry. At epigenomic, proteomic and metabolomic levels, the exposome drives positive and negative regulations, by inducing transcriptional, post-transcriptional, translational and post-translational modifications.

integrated meta-omics charts for the different diseases for which there is evidence of a direct or indirect link between gut microbiota and clinical features. These maps, obtained by operative packages, describe the operational taxonomic units (OTUs) of microbial communities as a

whole, in term of role and density, classifying them as pathogens, resilient and diners (metagenomics), and relating them to metabolic changes through the description of protein profiling (metaproteomics) and clinical conditions (phenomics). Based on this experience, a Consortium

Fig. 4. Individual genotype–phenotype interactions, including microbiome features, uncovered by –omics and meta-omics integrated strategies. The flow between genome machinery and phenomic traits is underlined showing the entire downstream complex of molecules regulating DNA and translated into proteins, into the context of the “superorganism”, hence explaining the genotype-phenotype flow paradigm into the framework of the host-microbiota functional relationship.

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for gut microbiota meta-omic and phenomic studies in pediatric diseases has been established. (Fig. 5). The translation of these findings into translational results requires access to large clinical populations and an in depth understanding of clinical phenotypes and subphenotypes at level of healthy individuals. The Consortium is performing three main activities: (a) subdivision of the microbiome phenotype into more homogenous subtypes (enterogradients) suitable for genetic analyses; (b) broadening the phenotypic datasets including gut abnormalities, diet characteristics, antibiotic administrations, programming in early life, and gut functioning; (c) development and application of new genetic methods to assess inherited disorders associated with distinct clinical traits. The steps forward rely on the evaluation of gut microbiota dysbiosis index to quantify the alterations of gut microbiota associated to pediatric diseases and healthy programming [83,1] (Fig. 5). Combining the different activities, the project will answer a number of specific goals: i) to define given aspects of the diagnosis and prognosis of intestinal dysbiosis, ii) to develop alternative therapies based on disease-targeted probiotics and prebiotics; iii) to improve the clinical management of the patient, more specifically the nutritional regimen. All biomaterials and phenomic data will become part of the OPBG Genetic Repository, filling the current phenomics initiatives (Table 1). Recently, dysbiosis have been associated with inflammatory events [84], IBD [85] and other diseases such as obesity, hepatic steatosis [86], metabolic syndrome [87], metabolic diseases [88], behavioral abnormalities [89]. In particular, some epidemiological studies have shown associations between certain neurological development disorder, such as autism, schizophrenia, anxiety, and microbial infections in the perinatal period [90]. The same damage to the mucous membranes,

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previously discussed for CF (intestinal phenotype), can serve as a model for understanding other intestinal disorders, such as malabsorption or inflammatory cascades in other diseases [91]. The onset of obesity has shifted dramatically in the early years of life and its prevalence in children has increased significantly on a global scale [92]. Obesity and metabolic diseases associated with obesity, such as metabolic syndrome and diabetes type 2, have been associated to distinct functional and structural traits of the intestinal microbiota [93]. Some research has documented a relative increase of Firmicutes and a decrease in Bacteroidetes in obese subjects, both in human and murine models [94], although there are still conflicting opinions in these reports [95]. On the other hand, transfer of gut microbiota from obese to germ free mice causes an increase in fat mass in the latter, indicating that obese microbiota has an “intrinsic” ability to accumulate energy from the diet [96]. It has been shown also that antibiotic treatment in these mice can dramatically reduce the proportion of Firmicutes and Bacteroidetes and increase Proteobacteria [97]. Based on these results, it seems reasonable that intestinal microbiota regulates the inflammatory response in obesity and metabolic syndrome, and even in subjects at risk of developing overweight related cardiovascular disease [98]. Therefore, biomarker discovery related to microbiota dysbiosis has direct laboratory medicine outcomes. With this aim, advanced metaproteomics, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH) Triple-TOF MS technology, able to produce differentially expressed quantitative profiles of proteins/metabolites, may represent a useful tracer of individual proteomics patterns for each patient [99]. Furthermore, metabolic profiles, obtained in combination with multivariate analyses, are able to examine the co-metabolism of host microbiota, linked to phenotype, pathology and diet [100]. The metabolome

Fig. 5. OPBG Clinical -Omics Consortium for generation of Pediatric Genotype-Phenotype Repository. A network of researchers and clinicians of the Bambino Gesù Children's Hospital and Research Institute, Rome, are generating a reference biobank of faecal samples for the study of pediatric diseases associated with intestinal dysbiosis. The Consortium is generating integrated meta-omics charts of microbiota for the different diseases for which there is evidence of a direct or indirect link between gut microbiota and clinical phenotypes. These maps describe microbial communities as a whole, relating them to their metabolic changes and to patients' clinical conditions (phenomics), compared to healthy subjects (epidemiological survey) –omics data, in age stratified case-control studies. The initiative will comprehensively characterize a large set of children samples (e.g., faecal, salivary and urine samples) by meta-omics parameters and individual diets, searching for: i) potential biomarkers associated to host-gut microbiota-phenome; ii) gut microbiota dysbiosis indexes in the pediatric population. All biomaterials and phenomic data are part of the OPBG Pediatric Genotype-Phenotype Repository.

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analyses of various biological fluids, including feces, plasma and urine, provide an appropriate global strategy to establish the links between bioconversion of non-digestible food ingredients, bio-availability and their effects on host metabolism under specific disease conditions [101–103]. These new technological tools have widened the identification of previously uncharacterized bacterial populations in the microbiota, bypassing the old culture-dependent approach [104]. Although the composition of bacterial species varies among the different individuals and over the time [105], metagenomic analyses of mucosal and faecal samples from healthy subjects have shown the presence of Firmicutes, Bacteroidetes, Proteobacteria, Fusobacterium, Verrucomicrobia, Cyanobacteria, and Actinobacteria Spirochaeates in large populations [66,106–109]. However, species of Bifidobacteria, which are among the most abundant when identified by the traditional microbiological methods, are found with great difficulty in the metagenomic libraries [106,110]. For this reason, microbial culturomics (i.e., observational biology), on culture-based MALDI-TOF MS, is definitely complementing metagenomics [111–117]. While GWAS have found associations between disease genotype and phenotype changes, metabolome wide associations studies (MWAS) have correlated metabolic phenotypes to the disease phenotypes [118]. Through the production of antimicrobial compounds, volatile fatty acids (FAs) and chemically modified bile acids, the gut microbiota creates a metabolically reactive environment, often described as bioreactor [119,120]. Recent studies have shown that 1H NMR, GC–MS metabolic analyses of faecal extracts and urine can clarify the metabolic interspecies differences of the microbiota components [121], thus providing important diagnostic information for major diseases [119, 122–124]. In addition, “meta-omics” technologies (metagenomics, metaproteomics and metabolomics) are significantly contributing to recognize the microbiota ecosystem as a “whole population structure”, and to clarify microbial communities' modulation and their active interaction with external stimuli and with food, with respect to individual host genetic variability [44] (Fig. 4). 3. The foodomics in the new systems medicine 3.1. The interplay between microbiota, nutrients and diet Diet, as major external exposome factor, can also influence epigenetic changes associated with disease and modulate gene expression through epigenetic control. Colonic bacteria metabolize macronutrients, either as individuals or as consortia, in a variety of diverse metabolic pathways. Microbial metabolites of diet can be epigenetic activators of gene expression that may influence cancer risk in humans [125]. As known, epigenetic mechanisms, which affect expression of target genes, include histone modifications, DNA methylation, and noncoding RNAs (Fig. 3). These alterations persist from one cell division to the next, so even though there is no alteration in the gene sequence, epigenetic alterations are heritable, and arisen by infectious pathways, genotoxic effects of microbial metabolites, pathogens and microbiota metabolism. Host's diet may even vary the microbiota composition, geographically adapting itself. De Filippo et al. have shown that the gut microbiome of children living in Burkina Faso was significantly different compared to that of European children of the same age [126]. The former was more rich, produced more butyrate, and had more Prevotella than Bacteroides with a diet high in fiber and non-animal protein, whereas the latter had a diet higher in refined carbohydrates and animal protein. The explanation may lie in the fact that Prevotella species are more suitable to release energy from vegetable food, which is the main constituent of the African children diet. To confirm this, a study carried out on populations of Bacteroides plebeius has shown that this species is able to adapt to the local diet in different groups of people. In particular, Japanese strains of B. plebeius contain a gene, acquired from marine bacteria, necessary for the degradation of porfirano, a carbohydrate present in the edible seaweed, which is not present in the microbiota strains of

North-American populations [127]. Therefore, it is highly probable that the microbiota species adapt themselves to the diet changes through gene rearrangements and this adaptation is the result of gene exchanges with environmental bacteria [44]. Indeed, dietary intervention studies have shown that gut microbiome often responds quickly to changes in diet [128–132] and alter the exposure of the host to microbial metabolites although these changes are often transient [133–136]. For example, in a randomized, controlled cross-over designed dietary intervention, bacterial groups and plasma levels of bacterial phenolic metabolites were altered only in one of the treatments, depending upon the type of fiber ingested in two-three week periods [136], suggesting the complexity and variability of the human exposure to the diet. Increased fiber intake reduces the exposure of gut epithelial cells to toxicants by increasing stool volume which dilutes faecal carcinogens and decreases the transit time. High-fiber foods contain an array of complex phytochemicals that are metabolized by the gut microbiome to short chain FAs (SCFA), isothiocyanates, and polyphenolic derivatives that interact with human gut epithelial cells and may modify epigenetic control of gene expression [125]. The main types of dietary carbohydrate that escape digestion in the small intestine are resistant starches, non-starch polysaccharides, and oligosaccharides primarily from plants. In westernized populations, approximately 40 g/day of carbohydrates enter the large intestine and undergo microbial metabolism [137]. There is a gradient in SCFA production from the proximal (70–140 mmol) to the distal gut (20–70 mmol), largely based on availability of substrate and SCFAs are inversely associated with increased pH in the distal colon [137]. The predominant SCFA from fermentation are acetate, butyrate, and propionate in a ratio of 3:1:1, although formate, caproate, and lactate are also formed [138]. While acetate is a dominant end product of glycolysis, cross-feeding between bacteria groups occurs and can influence the SCFA pools [139]. Butyrate forming bacteria condense acetate from butyrl CoA and external pools of acetate [140,141]. Use of external acetate pools varies among bacterial species and may alter the amount of acetate and butyrate available to the host. For example, Duncan et al. have shown that while Faecalibacterium prausnitizii and Roseburia spp derived the 85% of butyrate carbon from external pools of acetate, Coprococcus spp. derived only the 28% [142]. In addition, different sources of carbohydrates produce different amounts of butyrate ranging from 56% for pectin to 90% for xylan. Propionate is also formed via carbohydrate microbial fermentation. After formation of pyruvate, depending upon the microbial composition, propionate is formed via the succinate or the more minor acrylate pathways from carbohydrates that reach the colon [143]. While butyrate supplies the majority of energy to colonic epithelial cells primarily through beta-oxidation, concentrations of SCFA in the colon are also high enough to influence regulation of colon epithelial gene expression [144–146]. In normal homeostasis, butyrate plays a central role in promoting cell turnover of the colonic epithelium. In contrast, metabolism in cancer cells is dominated by aerobic glycolysis which uses glucose over butyrate as growth substrate. Butyrate can then accumulate in the nucleus where it functions as a histone deacetylase (HDAC) inhibitor regulating cell proliferation, inducing apoptosis and maintaining tolerance to intestinal commensal bacteria [147,148]. Previous studies have shown that certain species of bacteria, such as Escherichia coli, Bacteroides thetaiotaomicron, Enterococcus faecalis, Enterococcus faecium, Peptostreptococcus spp. and Bifidobacterium spp., isolated from human gut or feces can convert glucosinolates, from cruciferous vegetables, into isothiocyanates (ITCs) and other derivatives [149–151]. Other studies have suggested that the invivo exposure to ITCs may induce reduction in tumor growth through the effects on DNA methylation, histone modification, and miRNA. Sulforaphane (SFN), an ITC, prevents carcinogen or genetically induced colon cancer in rodent models. Recent studies have shown that sulforaphane is an HDAC inhibitor, leading to an increase in global and local histone acetylation [152,153]. Polyphenols represent a wide variety of phytochemicals that are divided into several classes according to their chemical structures. They

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Fig. 6. Catalogues of –omics-based observations and measurements to cover genotype-phenotype links. Individual genotype-phenotypes flows, through microbioma enterogradients' effects, allow to establish population models based on molecular epidemiology, by employing: i) Physiology Models, through host microbiome specificity, simulation of metabolic perturbations, gut proteome catalogue; ii) Computational Biology, by integration of complementary meta-omics; iii) Observational Biology, based on microbial colturomics; iv) Biobanks generation and standardization with phenomic databases' reports; v) Databases, based on all panomics information; v) Design of new functional food by foodomics.

include phenolic acids (hydroxybenzoic acids and hydroxycinnamic acids), anthocyanins, flavonoids (flavanols, flavonols, flavones, flavonones, and isoflavones), stilbenes, lignans, and curcuminoids. These compounds undergo extensive microbial transformations in the colon and also enterohepatic circulation influencing host exposure to these compounds and their metabolites. While the dietary polyphenolic compounds are considered epigenetic changes inducers, several microbial polyphenol metabolites have yet to be tested. Polyphenols exhibit differential selection for different species in the microbial community by either acting as antibiotics or prebiotics, which may influence human exposure to microbial metabolites of polyphenols. Recently, van Dorsten et al., have shown, in a simulated gut bioreactor, that the type and distribution of microbial polyphenols' metabolites changed as a function of dosing strategy [154]. For example, valerolactones, one of the initial fission products of microbial metabolism of catechins, disappeared with continuous dosing. In parallel, Kemperman et al., found dramatic shifts in the microbiome composition with continual polyphenol dosing, although it was difficult to relate the disappearance of valerolactones to specific bacteria [154]. Ellagitannins are polyphenols found in fruits, such as pomegranate, raspberries, strawberries, blackberries, and nuts, including walnuts and almonds. Ingested ellagitannins are hydrolyzed in the stomach and small intestine to ellagic acid. The gut microbiome metabolizes ellagitannins to urolithins by removal of one of the lactone rings and subsequent dehydroxylation in the colon. Enterohepatic circulation of urolithins and ellagic acids alters host exposure to these compounds, and there is large interinidividual variation in the urolithin production [155]. Recent studies suggest that bacteria from the Chlamydomonas coccoides group and Actinobacteria are involved in the production of urolithins [155,156]. Ellagitannins have strong antioxidant, radical scavenging, anti-viral, anti-microbial, antimutagenic, anti-inflammatory, anti-tumor promoting, and immunomodulatory properties. Ellagitannins inhibit proliferation and induce apoptosis of cancer cells through modulation of transcription factors and

signaling pathways [157]. The majority of dietary fats, such as triacylglycerol, saturated and unsaturated FAs, and sterols, are absorbed in small intestine. However, recent studies have suggested that 7% of ingested fat is excreted in stool and is likely metabolized by the gut microbiota [158]. In addition, bile acids (BAs) are synthesized in the liver from cholesterol, conjugated with taurine or glycine. Bile is secreted from the gall bladder into the small intestine to help emulsify fats during digestion. Most of the BAs (95%) are absorbed in the ileum and delivered back to the liver. A small amount is delivered to the colon and undergoes anaerobic microbial metabolism to the secondary BAs deoxycholate (DCA), lithocholate (LCA), and ursodeoxycholate (UDCA). It is now clear that BAs have both direct antimicrobial effects on gut microbes [159], and indirect effects through farnesoid X receptor (FXR)-induced antimicrobial peptides, which are nuclear receptor for BAs, inducing genes involved in enteroprotection and inhibiting bacterial overgrowth and mucosal injury in ileum [160]. Indeed, the potency of DCA as an antimicrobial agent, is an order of magnitude greater than cholic acid (CA), owing to its hydrophobicity and detergent properties on bacterial membranes [161]. Significant changes in the gut microbiome have been observed in animal models fed BAs. Islam et al. demonstrated that a medium CA intake and high CA diet resulted in gut microbiome alterations with Firmicutes increasing from 54% in control (CTRLs) rats' microbiome to 93–98% in CA fed rat microbiome [162]. The Clostridia expanded from 39% in CTRLs to roughly 70% and within the Clostridia, the Blautia increased from 8.3% in CTRLs to 55–62% when rats were fed CA [162]. Blautia includes either Clostridium and Ruminococcus spp. goups, many of which are found within Clostridium cluster XIVa, and are characterized as BA 7α-dehydroxylating species, producing secondary BAs [163]. Total BAs in feces increase 6-fold and 20fold in the medium and high CA diet, respectively, with the medium CA diet containing similar levels to those reported in human faecal water on a high-fat diet. These results demonstrate that increased input of BAs result in significant inhibition of Bacteroidetes and Actinobacteria [162]. Expansion of Firmicutes results in significant increase of DCA-producing

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bacteria, and indeed Ridlon et al., observed a 1000 fold increase in the levels of BA 7α-dehydroxylating bacteria by feeding mice CA [163]. If BA feeding results in Clostridium cluster XVIa expansion, then it should be expected to see a decrease in this taxonomic group when BA levels decrese, as actually reported by Kakiyama et al., [164]. Levels of BAs entering the large intestine thus has a profound effect on the major division/ phyla level taxa in the gut lumen. Bajaj et al. [165] also demonstrated that the mucosal microbiome community was significantly different from the community in the lumen and these differences correlated with complications of cirrhosis such as hepatic encephalopathy. Taken together, decreasing levels of BAs in the gut favor Gram-negative bacateria, some of which produce potent LPS, and include potential pathogens. Increased BA levels in the gut appear to favor Gram-positive members of the Firmicutes, including bacteria that 7α-dehydroxylate host primary BAs to toxic secondary BAs. Indeed, intrinsic food functionalities (i.e., presence of bioactive compounds, such as conjugated linoleic acids [CLAs] which are powerful polyunsaturated FAs [PUFAs], prebiotic substances and/or probiotic cells) and food features (i.e., composition, naturally occurring microbiota including starter cultures) may affect gut microbiota composition and physiology. Fish oil from cold water fish and plants provides dietary sources of 3ω long chain PUFAs (3ω LC-PUFA). Epidemiologic studies have shown that these fats are protective against colon and prostate cancer although there are conflicting results [166]. A reduction in histone lysine methylation by 3ω LC-PUFA has been shown to downregulate genes in cancer cell lines [167]. The anaerobic bacteria, Roseburia, Bifidobacteria, and Lactobacillus, found in the distal gut, metabolize 3ω LC-PUFA from dietary intake to CLAs [168,169]. Although 3ω LC-PUFA are not produced by bacteria, the availability of 3ω LC PUFA may be influenced by microbial metabolism and could, in part, explain some of the interindividual variation in the effects of 3ω LC-PUFA on cancer. Milk is certainly a fresh drink but also a fermented food. Spontaneuous milk fermentation occurs as a spontaneous process in which lactic acid bacteria (LAB)-based microbiota, producing lactic acid, are able to coagulate the milk, producing fermented dairy product, even when accompanied with molds and yeasts, producing diverse variety of fermented products. Tipically, dairy yoghurt, most likely originated from the Midlle East, is acidified with LAB Streptococcus thermophilus and Lactobacillus delbrueckii spp. Bulgaricus [170]. In Western Europe very common is buttermilk, while in Scandinavia viili is very famous, which is fermented by using either LAB and molds. Central Asia is typical for koumiss, mare milk fermented by a symbiotic action of LAB and yeasts, trapped in kefir grains. Also cheeses reflect fermentation and ripening processes carried out by LAB communities. Generally Lactococcus spp., Streptococcus tehrmophilus, L. delbrueckii and Lactobacillus helveticus are regarded as typical starter bacteria, deliberately added at the beginning of the manufacture or naturally present in cheese milk. Also seconday microbiota plays major role during ripening and comprises nonstarter LAB (NSLAB) such as Pediococci, Enterococci, propionic acid bacteria, molds, yeasts. The secondary microbiota can enter cheese via deliberate addition as defined cultures or, via environments, as naturally occurring microorganisms. Starter cultures, prepared as blend or defined strains and used for industrial cheese manufacture, are added to the milk before cheese making. For other traditional cheese, starter is not added and the process only depends on microbiota naturally occurring in eaw milk [170]. Certainly, habitual diet plays a major role in shaping the composition of gut microbiota and also affects the repertoire of microbial metabolites that can influence host response. The typical Western diet corresponds to that of an omnivore; however, the Mediterranean diet (MD), common in the Western Mediterranean culture, is to date a nutritionally recommended dietary pattern that includes high-level consumption of cereals, fruit, vegetables and legumes. The potential benefits of the MD was recently investigated in a cross-sectional survey, were gut microbiome and metabolome was addressed in a cohort of 153 Italian individuals, habitually

following omnivore, vegetarian or vegan diets [171]. Significant associations between consumption of vegetable-based diets and increased levels of faecal SCFA, Prevotella and some fiber-degrading Firmicutes were detected. Conversely, higher urinary trimethylamine oxide levels in individuals with lower adherence to the MD was observed. Therefore, high-level consumption of plant foodstuffs consistent with an MD was associated with beneficial microbiome-related metabolomic profiles in subjects ostensibly consuming a Western diet [171]. Recently, 14 Saharawi celiac children following an African-style gluten-free diet for at least two years were subjected to a change of diet to an Italian-style gluten-free diet for 60 days to investigate salivary microbiota modifications [172]. Significant differences were identified in the microbiota and metabolome when the celiac children switched from African- to Italianstyle dietary habits. An Italian-style gluten-free diet caused increases in the abundance of Granulicatella, Porphyromonas and Neisseria and decreases in Clostridium, Prevotella and Veillonella, altering the salivary microbiota “type”. Furthermore, OTUs co-occurrence/exclusion patterns indicated that the initial equilibrium of microbial species was perturbed by a diet change: the microbial diversity was reduced, with a few species, previously absent in the microbiota becoming dominant [172]. Also a change in the microbiota metabolism following the diet change was observed, with increased for amino acid, vitamin and co-factor metabolism. High concentrations of acetone and 2-butanone during treatment with the Italian-style gluten-free diet suggested metabolic dysfunction in the Saharawi celiac children [172]. Hitherto, little is known about specific impact of probiotic consumption on the gut microbial population and the potential effect on chronic diseases. The modulation of the microbial community in the murine small intestine resulting from probiotic feeding was found to be associated with an anti-obesity effect in a recent paper [173]. Changes in the faecal and in the small intestine microbiota were monitored using quantitative realtime PCR and by following the mRNA expression levels of various obesity-related biomarkers following probiotic feeding in a C57BL/6J mouse model. Lactobacillus rhamnosus GG and Lactobacillus sakei NR28 were daily administered with 1 × 108 viable bacteria per mouse for up to three weeks. Feeding these strains resulted in a significant reduction of epididymal fat mass, as well as obesity-related biomarkers like acetylCoA carboxylase, FA synthase, and stearoyl-CoA desaturase-1 in the liver. The relative abundances of the microbial taxa, i.e. Firmicutes, Bacteroidetes, Clostridium cluster I and XIVab, and Lactobacillus spp. were modulated in the small intestine, and the Firmicutes/Bacteroidetes ratio was decreased. In contrast, no noticeable effect of probiotic feeding was detected on the faecal microbiota, neither quantitatively, nor with regard to bacterial taxa composition (Firmicutes, Bacteroidetes, Clostridium cluster I and XIVab, and Lactobacillus spp.) [173]. Also physical and chemical treatment of probiotic can indirectly affect starter microbiotiota, hence effecting human microbiotadependent response. The functional Lactobacilli paracasei A13, treated at 50 MPa sub-lethal High Pressure Homogenization (HPH), is used as co-starter for producing Caciotta cheese. The cell HPH treatment is used because proven to increase the in vitro strain functionality. The starters and L. paracasei A13 viability, the cheese hydrolytic patterns and organoleptic profiles were lately monitored by Burns et al. [174]. After cheesemaking and during ripening, the L. paracasei A13 gastric acid resistance and the ability of the cheese, containing HPH-treated or untreated cells, to modulate the gut mucosal immune system in mice were evaluated. Traditional Caciotta was used as controls. The HPH-treated probiotic strain maintained high viability for 14 days while the physico-chemical analyses on Caciotta cheese containing HPH-treated cells showed a faster ripening, compared to other cheeses. For functional properties, the 50 MPa treatment increased the L. paracasei gastric resistance in Caciotta, maintaining high strain viability, but IL-10 producing capacity was lost by HPH-treatment while IgA production was not modified [174]. Therefore, diet in all its aspects may alter the microbiome, which in turn alters dietary exposure, cometabolism and down-stream host response.

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3.2. Global foodomics strategies Nutritional metabolism and its imbalances are associated with noncommunicable diseases. Recent findings have shown that the epigenome is susceptible to changes and can be shaped by nutritional states, especially in the prenatal period through transgenerational mechanisms and in the early postnatal life when critical developmental processes are taking place. Although more stable, the epigenetic marks in adulthood are also dynamic and modifiable by many environmental factors, including diet. Thus, nutriepigenomics is a promising field in the treatment of complex human diseases. Remely et al. have reviewed the most recent knowledge of epigenetically active nutrients/diets including transgenerational inheritance and prenatal predispositions related to increased risk for cancer, metabolic syndrome, and neurodegenerative diseases [175]. The knowledge that metabolic pathways may be altered in individuals with genetic variants in the presence of certain dietary exposures offers great potential for personalized nutrition advice. Epigenetics and nutrigenetics have been used by microRNAs profiling and GWAS to assess the need and the status of specific nutrients. Since nutritional effects of complex diets emerge only if dietary assessments are validated, nutrimetabolomics offers the validation tool on the basis of food intake biomarkers. Foodomics is the science aiming at studying, through the evaluation of different biomarkers, the entity and direction of the movements across the healthy and unhealthy space, developing models that are able to explain how food components, food, diet and lifestyle can influence our trajectory towards the healthy condition [176]. A growing number of studies on the development and application of non-targeted -omics methods in foodomics, show that this emerging discipline is regarded by the scientific community as a valuable approach for assessing food safety, quality, and traceability, as well as for studying the links between food and health. As a result, high-throughput MS approaches are becoming fundamental for developing and applying non-targeted studies, generating sets of information from the largest possible number of samples in a fast and straightforward way. The use of high- and ultrahigh-resolution MS greatly improves the analytical performance and offers a good combination of selectivity and sensitivity. By using a range of methods for direct sample introduction/desorption/ionization, high-throughput and non-target analysis of a variety of samples can be obtained in a few seconds [177]. As new global methodology, foodomics is based on the combination of several analytical platforms and data processing for transcriptomics, proteomics and metabolomics studies. They allow the determination of changes induced by food ingredients at molecular level, following a hypothesis-free strategy (Fig. 6). For this reason, foodomics represents the most advanced discipline based on integrated –omics. First promising data are available specifically in the cancer panomics field. For example, the chemopreventive anti-proliferative effect of polyphenols, extracted from rosemary and characterized by LC-UV-MS, was examined with respect to the total gene, protein and metabolite expression in human HT29 colon cancer cells [178]. Bioactivity of polyphenols against colon cancer cells, based on the results from each single platform (i.e., Transcriptomics, Proteomics and Metabolomics), was compared with the integration pattern of the whole results from the three platforms. Polyphenol extract was probed against HT29 cancer cells, compared to control cells, without extract addition: RNA analysis, performed by microarrays, generated a pattern of differentially expressed genes, confirmed by real-time quantitative PCR (RT-qPCR) proteomics performed by 2D-PAGE and Matrix-Assisted Laser Desorption/Ionization (MALDI)-Time-of-flight (TOF)-TOF identification of differential proteins; metabolomics was based on capillary electrophoresis (CE)MS, confirming identity by Reverse-Phase Ultra-performance liquid chromatography-mass spectrometry (PR/UPLC)-MS and Hydrophilic interaction ultra performance liquid chromatography (HILIC)/UPLCMS. Pathways analyses underwent quality control, data correction and

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normalization from different platforms, until filtering and final annotation were reached (Fig. 2). A list of 1308 genes were found as differentially expressed, either down- and up-regulated and, among the others, Heme oxygenase 1 (HMOX1), Oxidative stress induced growth inhibitor 1 (OSGIN1), Dual specificity phosphatase 1 (DUSP1), Mucin 1 cell surface associated (MUC1) and Arrestin domain-containing 3 (ARRDC3) expression levels were corroborated by RT-qPCR. Proteomics detected 17 proteins with statistically significant expression pattern, with 10 down- and 7 up-regulated, while metabolomics, after processing, detected 210 metabolites showing significant variation by (RP/UPLCMS), 214 by HILIC/UPLC and 212 by (CE)-MS. After combination, 65 metabolites changed significantly, 10 resulted with the highest fold change (FC), but only there compounds (phenylalanine, tyrosine, leucine) were detected by all methods, underlining the need to use multiple analytical platforms to perform efficient and appropriate metabolomics determinations in foodomics. In particular, the up-regulated genes were HMOX1, associated to cell resistance to oxidative injury; OSGIN1, involved in cell growth reduction; DUSP1, belonging to oxidative stress and apoptosis pathways. Differently, MUC1, usually over-expressed in adenocarcinoma, and inflammatory diseases was down-regulated. Proteomics identified some up-regulated (e.g., cathepsin B, cathepsin D, peroxiredoxin-4, phosphoserine phosphatise), and down regulated proteins (e.g., stathmin A, copper-zinc superoxide dismutase, ATPase, gluthatione-S-transferase), the latter mainly involved in tumorigenesis, cancer proliferation, glycolysis. Metabolomics clearly identified metabolites associated to cellular processes, including proliferation and viability. Based on all these data, Ibáñez et al. [178] developed a data integration strategy based on networking and mapping features of the IPA software to generate the comprehensive cross-platform for biological interpretation. The integration suggested that the main biological functions associted to the network included amino acid metabolism, molecular transport, and small molecole biochemistry. The conclusion was that phenolic extracts may exert cytorpotective effect, promoting elimination of inactivation of toxic reactive species and increasing intracellular glutathione pool. Metatranscriptomic data were used in another study [179] to evaluate the effects of phytochemicals (i.e., more than 23,137 compounds present in plant-based diets [180] and derived from plant's secondary metabolism) on resident microbiota [156,181, 182], evaluating their stressor stimuli on microbial communities. By coupling metatranscriptomics to chemoinformatics (Table 1) [183] and network biology, it was created a molecular-level map of dietbacterium interactions, linking phytochemicals metabolism to gut microbiota [179]. A network consisting of more than 400 compounds present in the administered plant-based diet was linked to 609 microbial targets in the gut. Approximately 20% of the targeted bacterial proteins showed significant changes in their gene expression levels, while functional and topology analyses revealed that proteins belonging to high central metabolic networks were the most “susceptible” targets. This global view and the mechanistic understanding of the associations between microbial gene expression and dietary molecules could be regarded as promising methodological approaches for targeting specific bacterial proteins impacting human health. Hence, this approach can represent a pivotal study in designing diets with potential therapeutic benefits. In particular, the phylum Bacteroidetes was found to have the “broadest responding spectrum” i.e., it contributes to the differential expression of targeted genes more than other phyla. Within this group of genes, it was noticed that most of them were down-regulated at the community level. Interestingly, Gene Ontology (GO) annotation indicated that from this gene group mainly participated in the carboxylic acid metabolic process, cellular amino acid metabolic process, as well as RNA and protein metabolic processes. The phylum Firmicutes, although without the widest spectrum, was responsible for the differential expression of most up-regulated target genes, which were mainly involved in glucose, hexose, carboxylic acid, and FA metabolic processes. In addition, Firmicutes also negatively contributed to a cluster of down

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regulated genes. However, this effect was overridden by the opposite effect from Bacteroidetes, leading ultimately to a community-level down regulation. The phylum Actinobacteria predominantly contributed with a distinct gene profile associated to several regulatory processes not present in the groups of other phyla. All the target genes in the groups for both Actinobacteria and Proteobacteria displayed decreased expression at community level. The SCFA metabolism was also extensively described by Ni and Panagiotou [179], as being intimately associated in the regulating role of the biological processes in the human gut and colon [184,185]. The levels of SCFAs in the gut, especially butyrate and propionate, have been diffusely connected with the development of different diseases, ranging from metabolic and inflammatory diseases to cancers [186,187]. Positive correlations were described between the levels of certain SCFAs and particular microbes belonging to Firmicutes (Roseburia spp., Eubacterium rectale, and Faecalibacterium prausnitzii) [188]. Ni and Panagiotou [179] used an original computational framework (i.e., based on NutriChem and ChEMBL databases) for predicting diets potentially altering the activity of the SCFA-related pathways. In order to identify foods that could change butyrate or propionate metabolism, they adopted two approaches, either searching for food phytochemicals, that were experimentally tested to interact with proteins involved in these two SCFA metabolic pathways, or searching for foods containing phytochemicals, expected to interact with butyrate and propionate metabolic proteins. Although there were no phytochemicals identified in the ChEMBL database targeting SCFA metabolism-related proteins, the second approach yielded a set of 98 foods containing 31 phytochemicals (corresponding to 18 and 19 metabolites from butyrate and propionate metabolism, respectively). Of these 98 foods, 22 contained at least two phytochemicals involved in butyrate/propionate metabolism (e.g., strawberry, mung bean, and soybean). Interestingly, 8 of these foods had been tested also in the original study of David et al. [188], where significantly higher levels of SCFAs were detected in the plant-based diet compared to the animal based diet. To offer additional evidence that dietary interventions containing these 22 foods could potentially trigger changes in the SCFA gut microbiota metabolism, Ni et al., developed a food-disease network based on experimental studies loaded into the NutriChem database [179]. Significantly, 150 disease phenotypes (in terms of Disease Ontology IDs [189]) for these 22 foods were identified. In particular, breast cancer, hepatocellular carcinoma, and diabetes appeared to be connected to the highest number of foods [190,191], whereas sweet peppers, tomatoes, and buckwheat had the highest number of disease linkages. While hepatocellular carcinoma and diabetes have already been linked to gut microbial imbalances [192,193], Ni et al. [179] focused on a subset of the food-disease network, related to colon cancer, a disease highly associated with SCFAs levels [194]. With this aim, Ni et al., found that 9/22 foods were significantly and selectively associated with colon cancer in the NutriChem database (Table 1), showing that these 9 foods were significantly overrepresented in colon cancer, compared to other food-disease associations in the NutriChem database [179]. The methodology presented by Ni et al. [179], which relies on the known phytochemical composition of diet and potential interactions with gut specific bacterial genes, could serve as a preferential route for understanding the observed phenotypic responses to diet. 4. Conclusions Transformation of dietary compounds by the gut microbiome results in additional environmental exposures that may influence gene expression. Anaerobic metabolism by gut bacteria is a dynamic response to ingested substrates from myriad dietary sources. Functional genes measured in the gut microbiome suggest that there are basic metabolic pathways that are conserved across all healthy individuals [51,195], which have the core capacity to generate compounds

that influence epigenetic pathways of gene expression [196,197]. While colonic epithelium may have immediate exposure to microbial metabolites, many microbial products are absorbed into systemic circulation and may alter gene expression in regions distal to the gut. For this reason, besides transcriptomics and proteomics there is a growing interest in applying metabolic profiling to food science for the development of functional foods. One of the major challenges of modern nutrition is to propose a healthy diet to populations worldwide that must respect the high inter-individual variability driven by complex host genemetabolism/nutrients/environment/microbiota gene-metabolism interactions. Therefore, metabolic profiling can assist emerging foodomics, starting from screening for food composition to identification of new biomarkers of food intake. This approach can strongly support diet intervention strategies, epidemiological studies, and controlling of metabolic disorders worldwide spreading, ensuring healthy aging [198]. However, next steps, based on integration of dietary intake, measurements of gut microbiome and epigenome markers in multigenerational human population studies are needed to fully understand the true influence of these environmental factors on human health. To accomplish to this aim, “emerging” and “aspiring” –omics may actually contribute to the knowledge of cell, organisms, systems, populations, only following specific standardization and integration rules driven by “clinical matter solving” hypotheses [199]. Some of the current progress and open questions in nutritionrelated areas are based on the role of the microbiome bioreactor. Indeed, metabolic capabilities of microbial fermentation on nutritional substrates require further mechanistic understanding and advanced systems biology approaches to study functional interactions between diet composition and gut microbiota functional players, finally affecting host metabolism. However, it is essential to understand to what extent the intestinal microbiota is subject to dietary control and to integrate these data with functional metabolic signatures and biomarkers, in the framework of the host-microbiota co-metabolism. With high-throughput molecular technologies driving foodomics, studying bidirectional interactions of host-microbial co-metabolism, epithelial cell maturation, innate immune development, dysfunctional nutrient absorption and processing, signaling pathways in nutritional metabolism, is now possible. In all cases, as microbiome pipeline efforts continue, it is possible that enhanced standardized protocols can be developed, which may lead to new testable biological and clinical hypotheses [200]. It is a general belief that microbiome-derived drugs and therapies will come to the market in the coming years, either in the form of molecules that mimic a beneficial interaction between bacteria and host or molecules that disturb a harmful interaction or change the balance of “good” and “bad” bacteria in the gut microbiome [179]. In line with this, systematic analysis of the interactome between diet nutrients and the gut bacterial proteome/metabolome holds great potential for designing new effective dietary interventions for human health progress. In conclusion, only the fully comprehension of the relationship between food functionalities and the modulation of microbiota at gut or other district levels can be useful to produce targeted foods for specific consumer or patient categories.

Declaration of conflicts of interest The authors declare that they have no conflict of interest.

Acknowledgements The study was funded by Italian Ministry of Health (Ricerca Corrente nos. RC201302P002991 and RC201302G003050–2013, and RC201402G003251 – 2014) from Ospedale Pediatrico Bambino Gesù, Roma.

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