Chapter 1: Foodomics – Fundamentals, State of the Art and Future Trends

Researchers in modern food science and nutrition are moving from classical methodologies – traditionally used e.g., to provide a descriptive view of raw food composition or to investigate functional and nutritional factors – to more advanced and multi-disciplinary strategies. These new approaches adopt well-established methodologies in medical, pharmacological, and/or biotechnological research, making use of advanced omics tools and bioinformatics, along with in-vitro, in-vivo and/or clinical assays.¹  As a result of this trend, new interdisciplinary research areas such as nutrigenomics, nutrigenetics, nutritional genomics, nutritranscriptomics, nutriproteomics, nutrimetabolomics, microbiomics, toxicogenomics, and systems biology have emerged.

The new omics technologies have the potential to widen the scope of traditional targeted analysis and open up impressive possibilities to explore formerly unanswered questions and problems relevant to food science and nutrition. They have become powerful tools to tackle the comprehensive assessment of food safety, the first challenge for food researchers, that largely affects our health as consumers in a globalized market. Since many products contain multiple and processed ingredients, very often shipped from different parts of the world, worldwide movement of food and related raw materials have been repeatedly demonstrated to undergo global contamination episodes. Therefore, ensuring the safety, as well as the quality and traceability of food has never been more complicated and necessary than today.

In this line, European Food Safety Authority (EFSA) has opened a scientific debate on the integration of data produced by omics into the risk assessment of food, including the safety assessment of transgenic or genetically modified (GM) foods among others.^2,3 

Current trends in modern food science and nutrition are increasingly focused on understanding the food and health interplay. Food is now considered not only a source of energy but also an affordable way to prevent future diseases, as many food components are potential sources of health-promoting compounds. The possibility of employing food products tailored to promote the health and well-being of groups of populations identified on the basis of their individual genomes is an impressive opportunity opened by these new omics approaches. However, to scientifically demonstrate the health effect of food and food ingredients, analytical strategies have to face important difficulties derived, among others, from food complexity, the huge natural variability, the large number of different nutrients and bioactive food compounds, their very different concentrations, their bioavailability and transformation in the human digestive tract, the numerous targets with different affinities and specificities that might exist in the human body, etc. Thus, understanding the biochemical, molecular and cellular mechanisms that underlie the beneficial or adverse effects of certain bioactive food components is currently a hot topic in food research considered unapproachable few years ago.

In this context, there is an understandable need for innovative, high-throughput, multi-omics platforms able to provide the necessary data and information to offer real solutions and answers to the actual challenges in food science. Foodomics emerges as an integrative framework that involves not only gathering data coming from the different omics approaches but also the integration of all of them using advanced bioinformatics tools to be able to end up with the whole picture of the food–biological system interaction.⁴ 

In the following sections, the principles of foodomics including the fundamental omic tools employed, and its implications in food science and nutrition will be comprehensively described. Furthermore, an updated evaluation of representative foodomics applications in the field of foods safety, quality and traceability as well as in nutrition and health research will be provided. At the end of the chapter, the future challenges and foreseen trends that will face this promising discipline are discussed.

Foodomics, as defined in 2009,⁵  is ‘a discipline that studies the food and nutrition domains through the application and integration of advanced omics technologies to improve consumer's well-being, health, and confidence’. Foodomics is, therefore, a broad discipline that integrates all the multidisciplinary approaches in modern food science and nutrition (e.g. nutrigenomics, nutrigenetics, microbiomics, toxicogenomics, nutritranscriptomics, nutriproteomics, nutrimetabolomics, etc.). Considering the complexity of the foodome, defined as ‘the collection of all compounds present in any investigated food sample and/or in any biological system interacting with the investigated food at a given time’,⁶  the implementation of omics platforms, such as transcriptomics, proteomics and metabolomics, is essential to conveniently characterize the mentioned foodome. The combination of these techniques produces complementary analytical information, thus allowing a wider foodome coverage at different molecular expression levels (transcripts, proteins and metabolites). A representation of the areas covered by foodomics and the tools usually employed can be seen in Figure 1.1.

By taking advantage of the newest omics methodologies, foodomics is continuously pushing the research into different hot topics in food science and nutrition. One of the main goals and interests in foodomics is in line with medicine and biosciences toward prevention of future diseases through adequate food intake, and the development of the so-called functional foods and nutraceuticals.⁷  In this regard, foodomics covers, for instance, the investigation of the mechanisms that underlie the beneficial or adverse effects of certain bioactive food components at the biochemical, molecular and cellular level;⁸  the gene-based differences among individuals in response to a specific dietary pattern and the roadmap towards personalized nutrition;^9,10  the identity of the genes involved in the previous stage to the onset of a disease, that can lead to the discovery of possible molecular biomarkers; the global role and functions of the gut microbiome and its influence on individuals’ health.¹¹  Furthermore, foodomics can also help to investigate and solve crucial questions in food science and nutrition such as global omics strategies to explore food safety, quality and traceability;^7,12,13  the unintended effects in genetically modified crops or the comprehension of the molecular basis of biological processes with agronomic interest and economic relevance (interaction between crops and their pathogens, postharvest phenomena or physicochemical changes during fruit ripening) among other issues.^4,14 

Since its origin, the interest in foodomics has greatly increased, and many works have already shown the tremendous possibilities of this approach to boost food science investigations.⁶  A good example of the interest of the scientific community in Foodomics is the number of publications that have appeared in the last decade (more than 250 SCI papers). Some representative review papers on foodomics are detailed in Table 1.1, covering aspects related to (i) food quality and traceability, (ii) food safety and (iii) food bioactivity and health. Despite the growing number of papers dealing with applications in the foodomics field, the number of research works showing real experimental data integrating different omics technologies is still limited compared with opinions, comments and review papers, demonstrating the complexity of these multi-omics approaches and the long way that we still have to go.

To face the enormous challenges in different subdisciplines and applications, foodomics involves the use of multiple omics tools capable of providing molecular information on the different expression levels, i.e. gene, transcript, protein, or metabolite. Thus, some fundamentals about the main omics approaches used in foodomics, namely transcriptomics, proteomics, and metabolomics, are provided below.

Gene expression profiling is a useful tool for understanding the mechanisms of interaction between nutrients and genes. Thus, two conceptually different transcriptomics approaches can be applied to identify and quantify changes in mRNA expression levels of hundreds or thousands of genes. One of the approaches is based on gene expression microarrays, whereas the other transcriptomic platform is based on massive sequencing of RNA (RNA-Seq), which makes possible the analysis of thousands of transcribed sequences quickly and efficiently.¹⁵  Afterwards, gene validation, through quantitative polymerase chain reaction (qPCR), is normally employed to confirm the upregulation or downregulation of a selected number of genes,¹⁶  mostly after using microarrays. The fundamental goal of these approaches is to identify differentially expressed genes (DEGs) related to the condition of interest. The discovery of a large number of non-coding RNAs [e.g. microRNA (miRNA), long non-coding RNA (lncRNA), pseudogenes] with regulatory functions opens up a new field of study for nutrient action and emphasizes the study of transcriptomics as an end-point of regulatory control.¹⁷ 

Traditionally, hybridization-based approaches, such as gene expression microarray, have been the standard gene expression profiling technology in transcriptomic studies. This technique is based on specific nucleic acids hybridization to measure the relative quantities of specific messenger RNAs (mRNAs) in two or more samples for thousands of genes simultaneously. The experimental procedure involves RNA extraction from tissue, cells, or other biological samples, labelling (e.g. with a fluorescent marker) and hybridization with their complementary gene-specific probes on the microarray.³  Despite the powerful performance, variability is one of the main drawbacks of this technique, that can mask the biological signals of interest. The huge amount of data generated from microarray experiments requires thorough data processing to extract biologically meaningful information.¹⁸  In most transcriptomics analysis, the tool of choice up to now is the microarray, and Affymetrix platforms are the most preferred. Agilent, Illumina, Applied Biosystems, and home-made low-density arrays are also used.¹⁷ 

RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for conducting transcriptional profiling. The main difference between RNA-Seq and microarrays is that the former allows for full sequencing of the whole transcriptome while the latter only profiles predefined transcripts or genes through hybridization. In practice, RNA-Seq can help with identifying more differentially modulated transcripts of relevance, splice variants, and non-coding transcripts. However, the RNA-Seq approach has a few disadvantages compared with microarrays, namely (1) a lack of optimized and standardized protocols for analysis in spite of the availability of multiple computational tools and (2) the size of RNA-Seq files, which are considerably larger than microarray files. Finally, RNA-Seq requires an extensive and more complex bioinformatics analysis, which results in highly intensive and expensive computation infrastructure and analytics, as well as longer analysis times. However, these limitations are gradually being overcome.¹⁹ 

Proteomics represents a comprehensive scientific study of all expressed proteins or the entire proteome at any given time in an organism. Proteomics can provide details about the changes and comparisons in expression pattern of proteins in a specific physiological or pathological condition. Protein profiling approaches can also be used to analyze quality, origin, or adulterations of food.²⁰ 

The complexity in the proteome is mainly due to a large dynamic range, six to ten orders of magnitude. In plasma, the range is even higher than ten, which makes it difficult to detect very low abundance proteins. Proteomics deals with other problems such as alternative splicing and post-translational modifications (PTM), which play crucial roles in regulating the biology of the cell since they can change the physical or chemical properties, activity, localization or stability of the proteins.⁸ 

The analysis of the complex proteome has been boosted in recent years by the development and improvement of high-resolving separation systems, along with the even more accurate high-resolution (HR) tandem mass spectrometers. When working with complex food or biological samples most protocols usually include depletion of the most abundant interfering proteins, selective enrichment of the low abundance proteins of interest, or even partial purification of the target proteins. Before the final analysis in the mass spectrometer, further separation is performed at the protein and/or peptide level, typically based on two-dimensional gel electrophoresis [(2-DE) gel-based approach] and/or liquid chromatography [(LC) gel-free approach].²¹  Subsequent analysis of the isolated proteins or peptides is mainly based on mass spectrometry (MS) detection, using soft ionization methods, mainly matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). To avoid interferences in the ionization source, the sample clean-up is critical to remove salts, stabilizers, and/or detergents used in the extraction prior to MS analysis.⁸ 

Two different MS-based proteomic workflows can be followed, depending on whether the MS analysis is carried out on the peptide fragments (bottom-up) or on the corresponding intact proteins (top-down). The most widely used strategy is the bottom-up approach, characterized by the proteolytic digestion of the proteins prior to the MS analysis. Proteins can be firstly separated by using gel-based approaches such as 2-DE or sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and subsequently submitted to an in-gel digestion. After separation, the spots of interest are excised from the gel and submitted to analysis by MS. Alternatively, the protein can be enzymatically digested, and separation of the peptides performed using combinations of various chromatographic and/or electrokinetic separation methods, coupled with MS, following the so-called “shotgun” proteomic approach.

The identification of the original protein is carried out by comparison of the experimental mass spectra of the peptides obtained in the digestion, with their corresponding theoretical masses stored in databases. A broad variety of databases for proteins and peptides can be found: NCBI, SwePep, Erop-Moscow, PeptideDB, Peptidome, Pep-Bank, IPI human protein database, BioPep, or BioPD, among others. In addition, different search engine software packages have been developed to facilitate this task, such as MASCOT, SEQUEST, Andromeda, and X!Tandem. Moreover, advances in bioinformatics have enabled the development and the combination of computational tools for in silico prediction and discovery of functional peptides information from the genome sequence (known as “reverse-genome engineering”).²² 

In the top-down approach, the intact proteins isolated from a previous fractionation or purification step, via 2-DE or LC, are directly infused to a high-resolution mass spectrometer (HRMS). The proteins are studied through measurement of their intact mass and further fragmentation inside the mass spectrometer. Typical instruments used for top-down proteomics are MALDI-time-of-flight/time-of-flight (TOF/TOF), ESI-quadrupole (Q)/TOF, ESI-ion trap (IT), Orbitrap and the classical HRMS instrumentation Fourier transform ion cyclotron resonance (FTICR) MS; the latter one offering the highest mass resolution, resolving power, accuracy and sensitivity. This approach allows characterization of the post-transcriptional modifications present in proteins and differentiates biomolecules with a high degree of sequence identity. However, top-down proteomics approaches are usually limited to simple protein mixtures since multiple charged proteins generate very complex spectra.²⁰ 

As one of the most recent post-genomic disciplines, metabolomics has experienced notable progress in the last decade, as a result of the development of analytical platforms [mainly chromatography, nuclear magnetic resonance (NMR) and, mostly, MS-based techniques] and software programs to process the large amount of generated analytical data sets.²³  Metabolomics focuses on the full set of endogenous and small molecules with a relative molecular weight of less than 1000 Da (metabolites), and the small pathway motifs that are present in any biological system (cell, tissue, organ, organism, or species). Unlike nucleic acid or protein-based omics techniques, focused on the determination of a single chemical class of compounds, the huge number of compounds and broad physicochemical diversity of the metabolome (e.g. sugars, amino acids, small peptides, organic acids, lipids, and nucleic acids) entails important analytical challenges. In addition, the relative concentration of metabolites in the biological sample can vary from millimolar level (or higher) to picomolar, exceeding, in most cases, the linear range of the analytical techniques employed.³ 

Due the chemical diversity of the metabolome, no single analytical methodology or platform is applicable to detect, quantify, and identify all metabolites in a certain sample. A group of well-established analytical techniques, mainly based on NMR and MS, are the most frequently used in metabolic profiling and fingerprinting applications in metabolomics. These techniques are used either as stand-alone or, most commonly, combined with different separation techniques [LC-NMR, gas chromatography (GC)–MS, LC–MS, and capillary electrophoresis (CE)-MS]. The combination of techniques produces complementary analytical information, thus allowing a wider metabolome coverage.

The typical workflow in metabolomics research involves experimental design, sampling and storage (the metabolome must remain undamaged), sample preparation, sample analysis, data processing, biomarkers selection and annotation and metabolic pathway analysis for data interpretation. The success of a metabolomics study depends highly on the overall experimental design, which includes the careful consideration of the hypothesis and experimental strategies according to the goal of the study. In this regard, two different types of metabolomics studies can be carried out: ‘metabolic fingerprinting’ and ‘metabolic profiling’. The metabolic fingerprinting approach aims to compare patterns of metabolites that change in response to the cellular environment. In this approach, a generic sample preparation and determination methodology is normally applied in order not to miss any metabolite that can be important for sample classification. Meanwhile, metabolic profiling focuses on the study of a group of related metabolites or a specific metabolic pathway, which includes a more specific extraction procedure, as well as chromatographic separation and detection. Metabolic profiles of a cell give a more accurate description of a phenotype.²⁴ 

Considering the complexity of the metabolomics data matrices, containing thousands of mass to charge ratio (m/z) features, data processing and data pre-treatment, including noise filtering, overlapping the peak resolution, peak alignment, peak matching and normalization, is an essential requirement to allow the identification of significant metabolites. Subsequent multivariate data analysis for pattern recognition usually involves unsupervised models and supervised classification tools. Unsupervised models, including principal component analysis (PCA), cluster analysis (HCA) and nonlinear mapping (NLM), are used as first step in the data analysis to detect sample clustering in the measured data. Afterwards, supervisory models, such as linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) or orthogonal partial least squares discriminant analysis (OPLS-DA), can be used for statistical model validation in order to find differences between the known groups, and to detect the differential metabolites.²³  Finally, annotation of the significant markers is mainly based on searches against HRMS or MS/MS fragmentation databases [e.g. METLIN, Human Metabolome Database (HMBD), MassBank, NIST database, Fiehn Lib, mzCloud], that have been continuously growing during the last decade, both in coverage and chemical diversity.²⁵ 

According to the Chemical Analysis Working Group of the Metabolomics Standards Initiative (MSI), the identification reliability of a metabolite can be classified in four different levels: ‘Identified metabolite’ (level 1), ‘Putatively annotated compounds’ (level 2), ‘Putatively characterized compounds classes’ (level 3) and ‘Unknown’ (level 4). However, confident metabolites identification continuous to be a bottleneck in the metabolomics process. For this reason, a combination of approaches is required, including new analytical strategies, computational algorithms and database resources, as well as a joint effort of the metabolomics community, as recognized with the formation of a scientific task group of the international Metabolomics Society to enhance the characterization of metabolomes by initially focusing on a few model organisms.²⁶ 

Considering the enormous amount of data generated by different omics platforms, the development of bioinformatics tools is necessary in foodomics in order to integrate the complex raw data obtained into useful information. Many tools are available in order to build and visually explore the interaction networks of genes, proteins, and metabolites according to regularly updated databases. Most of these algorithms, such as Ingenuity Pathway Analysis (IPA), Cytoscape or Pathway Studio, work on the basis of a web page where the list of genes, proteins or metabolites of interest can be uploaded and searched for their annotations in databases (in-house built databases or publicly available databases) such as Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) or the Biomolecular Interaction Network Database (BIND) and mapping them to known biological pathways.²¹ 

According to the World Health Organization, food contaminated with bacteria, viruses, toxins or chemicals is responsible for more than 200 diseases, from diarrhea to cancers, affecting more than 600 million people worldwide (10% of the population).⁴⁴  Therefore, one of the main goals of food analysis has always been to guarantee food safety. However, traditional analytical methods are frequently slow and inadequate for the detection of contaminants in complex and diverse food matrices. Foodomics is a perfect tool when applied to this task as it can help to overcome some of the challenges that lie ahead for food safety, such as the fast, multiple and simultaneous detection of allergens and contaminants in complex food matrices.¹  The applications of foodomics to food safety encompass the discovery of biomarkers related to unsafe products and the development of analytical methods for their quick detection. These biomarkers can be metabolites, proteins, peptides or polynucleotides that allow the identification of potential microbial infections, toxins, allergens, veterinary drug, or pesticide residues.³² 

The global food market together with novel nutritional trends, like the increasing consumption of exotic, fresh, and sometimes raw food, are a risk for the appearance of allergies or new food pathogens, as, for example, the outbreak of food poisoning that occurred in Germany and France in 2011 caused by the ingestion of Shiga-toxin producing Escherichia coli from organic Greek sprouts.⁴⁵  Other global problems, such as the increase in pollution and sea microplastics, or global warming, may originate new toxic compounds which could raise more food safety concerns that should be approached from a foodomics perspective. Furthermore, foodomic tools could be used to ensure food safety in all the different steps of the food chain, from the analysis of raw materials, to food processing, distribution, and consumption. It is consequently necessary to develop more efficient, sensitive, and cost-effective analytical methods that ensure food safety in accordance with consumer and regulatory demands.⁴⁶ 

The use of genomics, transcriptomics, proteomics or metabolomics tools for the detection of pathogen biomarkers in food has been increasingly gaining importance, as has been reviewed in several publications in recent years.^47–50  In genomics and transcriptomics, the application of PCR or qPCR methods, microarrays and next generation sequencing (NGS) technologies together with bioinformatics approaches allows the identification and quantification of microbial organisms. Also, the massive analysis of cellular gene expression enables the study of foodborne pathogens’ survival strategies or their response to food preservation technologies or additives.⁵⁰  Figure 1.2 shows the main gene groups modified in the transcriptomic response of different foodborne pathogens to environmental changes in the food chain. Some examples are the use of RNA-Seq for the determination of the transcriptomic response of different strains of E. coli to prolonged cold stress⁵¹  or to acidic pH,⁵²  the response of Staphylococcus aureus to the antibacterial peptide nisin⁵³  and the adaptation mechanisms of Listeria monocytogenes to vacuum packaging⁵⁴  or to the addition of sodium lactate and sodium diacetate.⁵⁵  As for other applications of NGS related to food safety it is also interesting to mention the use of metagenomics for the characterization of the diversity of microbial communities and their ecological interactions within food or to monitor the appearance and evolution of microbiomes under food storage conditions.⁴⁹ 

NMR metabolomics as well as different MS-based approaches in both proteomics and metabolomics have become widely used for the determination and quantification of pathogens, toxins, allergens, and chemical contaminants in food matrices as well as their interactions with the food components and their mechanisms of action.⁵⁶  Examples of these applications in proteomics are the use of MALDI-TOF/TOF MS/MS to identify subtypes of Shiga toxin-producing E. coli or 2-DE coupled with MS for protein spots identification to detect allergens in fish, rice, or breast milk.⁴⁷  Due to the short shelf-life of food products, there is a necessity for faster and non-labor-intensive pathogen detection methods. In this regard, metabolomics has shown great potential, as, for example, with the use of proton transfer reaction MS for the real-time determination of the evolution of organic volatile compounds produced by microorganisms in spoiled milk.⁵⁷  Both targeted and untargeted GC– and LC–MS/MS metabolomics or lipidomics approaches have also been widely used for the determination of chemicals from pesticide or antibiotic residues in tea, wine, meat, coffee, or honey.⁵⁸ 

The integration of different omics technologies not only improves the screening of bacteria in complex food matrices, but also provides a better understanding of the molecular mechanisms behind pathogen survival and niche adaptation, antimicrobial resistance, effects of pesticides on gut microbiota, and discovery of new targets for infection treatment.⁵⁸  An example is the use of MALDI-TOF proteomics together with GC–MS metabolomics approaches for the fast and simultaneous detection of E. coli, L. monocytogenes and Salmonella enterica in red meat.⁵⁹  Also, Mesnage et al. used the combination of shotgun metagenomics and reverse phase ultra-performance liquid chromatography (UPLC)-MS/MS metabolomics to assess the effects of the pesticide glyphosate on the rat gut microbiota, showing alterations in the caecum microbial community structure and dysregulation of metabolites related to redox balance.⁶⁰  Omics integration paired with modern bioinformatics approaches, such as docking, may have a revolutionary effect on food safety, for example in the elucidation of functional sites of novel natural food preservatives or antimicrobial molecules.⁶¹ 

Finally, the enormous amount of data produced by all these technologies is expected to result in a paradigm change in future food safety concepts, and perhaps bring what is termed as “precision food safety” which will include the use of different omics together with bioinformatics, phenotypic and epidemiological data to improve evidence-supported food safety risk assessment for the implementation of new policies and procedures.⁴⁸ 

The assessment of food quality needs to consider multiple aspects, involving the composition, nutritional properties, flavor, origin and appearance of food. These factors are the ones preferentially used by consumers in order to evaluate food attributes.⁴  It is also of great importance to accurately track the source of possible food spoilages in order to determine if it is the result of a sporadic event or a recurrent one and to prevent future contaminations.⁴⁹  Furthermore, it is very interesting not only for consumers but also regulatory agencies to prevent food fraud and confirm food authenticity. Foodomics can be perfectly applied to meet some of the challenges faced by food science in terms of food authentication, traceability and quality.

The applications of foodomics to prevent and control food adulteration have been reviewed.^62–65  The most common issues related to food authenticity are the substitution of a species for one of a lower quality and price, fraud related to protected designation of origin and mislabeling of organically produced food products or GM organisms. Genomics, proteomics, metabolomics, and lipidomics have a great potential to reduce food fraud. For example, the use of ambient MS ionization methods, such as direct analysis in real time (DART) or desorption electrospray ionization (DESI), is especially interesting in this field, as they require minimal sample preparation and have a great potential for on-site real-time analysis.⁶⁶  Also, novel genomic techniques, such as DNA barcoding together with droplet digital PCR and NGS, may revolutionize this area as new species are constantly incorporated into DNA barcode libraries and NGS has the capability to analyze the entire composition of a food product.⁶⁴  Figure 1.3 shows a diagram of the proteomic MS approaches to food authenticity challenges.

A very interesting field where foodomics contributes significantly to food authenticity is the monitoring of the correct labelling, composition, substantial equivalence, and quality of GM foods, using advanced omic technologies, as has been recommended by the EFSA.¹  These advanced analytical tools should enable the specific determination and quantification of the genetically modified organism (GMO) content in food for screening and labeling compliance and also allow a comprehensive compositional determination to evaluate potential adverse effects. The great majority of screening methods are based on PCR and NGS targeted approaches that simultaneously amplify and detect DNA sequences found in as many different GMOs as possible. On the other hand, several studies have developed MS-based untargeted proteomics and metabolomics advanced analytical methodologies to detect unintended effects produced in GM crops as a result of the genetic modification as well as to characterize their molecular composition.⁶⁷  This type of analysis is gaining relevance with the development of the so-called second-generation of GM organisms which incorporate novel traits intended for consumer benefit, such as golden rice, which has been modified to express a vitamin A precursor, and has met the regulatory requirements of target countries such as the Philippines and Bangladesh.⁶⁸ 

With a growing consumer demand for food quality, there is a clear need for the development of novel analytical methods that meet these high standards. Therefore, omics technologies have also been used for the characterization of food quality, as has been reviewed.^39,47,69  Genomics, transcriptomics, proteomics, and metabolomics have been especially employed in the evaluation of the molecular composition related to food consistency, organoleptic properties, and their changes during processing and storage.

Next-generation sequencing together with genome-wide association analysis (GWAS) has been used in genomics to understand the relationship between the genes that control the levels of major biochemical pathways in different crops and the traits that define their quality or improve their yield.^70,71  Also, the sequencing of major crops and their different varieties enables plant breeders to study genetic diversity and perform directed crop improvement that can adapt crop plants to variations in climate, or processing conditions, or improve food quality.⁷²  Transcriptome data obtained with RNA-Seq together with bioinformatics analysis can also be very useful to elucidate regulatory networks that are activated or repressed in food exposed to various storage and processing conditions, or to geographical, soil, feeding, or climate variations. As an example, a recent study used RNA-Seq to evaluate the effects of dietary supplementation of eucalyptus leaf polyphenols extracts on meat taste and color in chicken. The study identified ten genes that were significantly related to the increase in redness and myoglobin redox form content observed in the chicken fed with the polyphenol supplement.⁷³ 

Proteomics and metabolomics are of special interest in food quality, as they represent the major molecular response of the cell once the genome loses its active influence, such as in food processing, pasteurization, fermentation, or cooking conditions. Also, they are the compounds that mainly provide the flavor and color in food. Therefore, correlations between the proteome and metabolome profiles or their interactions with quality traits enable the tailoring of organoleptic and technological properties of food.⁶⁹  Due to the complex and wide diversity in physico-chemical properties of flavor components, there is not a single method that can identify and quantify all of them at the same time. Therefore, different MS-based approaches coupled with separation techniques such as 2-DE gel electrophoresis for proteomics or GC, LC, and CE for metabolomics have been widely used in this field. Bottom-up approaches in proteomics have been used for the evaluation of protein changes during heat treatment processing.⁷⁴  For example, 2-DE with in-gel peptide digestion coupled to MALDI-TOF MS peptide mass fingerprinting was used to characterize protein modifications in cooked and raw pork meat, finding differences in the heat-induced myosin breakdown and the oxidation of methionine. Also, the effect of post-translational modifications in functional and structural properties of the proteome in stored and processed milk has been studied using both LC and 2-DE coupled to high resolution MS. A metabolomics example is the evaluation of the effects of ageing using two-dimensional hydrophilic interaction liquid chromatography (HILIC) and reverse-phase (RP)-LC coupled to quadrupole time of flight (Q-TOF) MS to compare the profile of anthocyanins and their related pigments in young and aged red wines.⁵⁸  Targeted and untargeted NMR metabolomics has also been widely used for the evaluation of food composition, processing and physico-chemical properties as has been reviewed by Trimigno et al.³⁹ 

Omics integration also provides clear advantages in the determination of molecular mechanisms related to variations in food quality during processing, storage, cooking, etc. A combined proteomics and metabolomics approach evaluated the tenderness of Piedmontese meat at different times after slaughter. The use of nanoLC-MS/MS for proteomics together with Q-TOF metabolomics showed a progressive decline in myofibrillar integrity, impaired energy metabolism and accumulation of markers of nitrogen metabolism and glutamate, a marker of the umami taste, throughout the ageing process.⁷⁵  Finally, integrated omics approaches may play an important role in the analysis of the mechanisms of additive production performed by synthetically engineered bacteria consortiums. An example of this application is the integrated use of proteomics and metabolomics for the characterization of the one-step fermentation production of a precursor of vitamin C produced by a synthetic consortium of three bacteria.⁷⁶ 

One of the final goals of foodomics is to understand the bioactivity of food and food ingredients in our body at the molecular level.⁷⁷  This topic has been reviewed a few years ago,⁸  but due to growing interest, here we summarize and present the latest developments, advances, and applications of foodomics in this field (Table 1.2). To achieve this great goal, the holistic omics approach is needed and therefore the integration of the information obtained at the gene, protein and metabolite level is essential. However, and as can be observed in Table 1.2, most of the studies performed in this field have used single omics approaches, and half of these works had the aim of characterizing or identifying potential beneficial compounds in food matrices.

Only a few works have addressed the integration of multi-omics approaches for the study of the effects of dietary components on human health. One of these studies was focused on the evaluation of the anti-inflammatory effect of a combination of resveratrol, green tea extract, alpha-tocopherol, vitamin C, ω-3 polyunsaturated fatty acids, and tomato extract in overweight men.⁷⁸  The metabolomics and proteomics studies were performed on plasma samples while transcriptomics were performed on peripheral blood mononuclear cells (PBMC) and adipose tissue. The results obtained indicated that the mixture of those ingredients induced several subtle changes indicative of modulated inflammation of adipose tissue, improved endothelial function, affected oxidative stress, and increased liver fatty acid oxidation. Other studies have been focused on the metabolic changes in plasma and the changes in gene expression in PBMC after diet supplementation with n-3 polyunsaturated fatty acid and fish gelatin on humans;⁷⁹  or evaluating the influence of Hibiscus sabdariffa polyphenols on humans.⁸⁰  The results of the last study indicated that the ingested polyphenols play a regulatory role in metabolic health and in the maintenance of blood pressure, protecting against metabolic and cardiovascular diseases. Apart from human studies, in vivo and in vitro models have also been used. For instance, the effects of kiwifruit extracts on the colonic gene and protein expression levels were evaluated on interleukin 10 (IL-10)-deficient mice;⁸¹  and the dietary signatures of parsley interactions were studied on a dextran sodium sulphate-induced colitic murine model.⁸²  Due to the downregulation of inflammatory cytokines and the upregulation of fatty-acid synthesis genes, the results of the last study indicate that parsley may be useful as a healthy food against inflammatory bowel diseases. In the case of in vitro models, these have mainly been used to evaluate the health benefits of dietary polyphenols. In a series of studies, different metabolomics platforms (RP-LC-ESI-Q-TOF MS, HILIC-LC-ESI-Q-TOF MS and CE-ESI-TOF MS), in combination with gene expression microarrays and complemented with advanced proteomic techniques (2-DE together with MALDI-TOF/TOF MS) were applied to investigate the effects of rosemary polyphenols against colon cancer or leukemia cells.^83–86  The data integration of the different omic approaches indicated that rosemary polyphenols possess antioxidant activity and induce apoptosis and cell cycle arrest, which could be related to the activation of the nuclear factor erythroid 2–related factor 2 (Nrf2) transcription factor and the unfolded protein response.

The anti-proliferative effect of bioactive extracts from two food by-products (Physalis peruviana L. calyx and Passiflora mollissima seeds) was evaluated against colon cancer cells, and the molecular changes at the transcriptome and metabolome levels were studied.^87,88  In the case of Physalis peruviana calyx extracts, significantly altered genes and metabolites were involved in the inactivation of the tRNA charging signaling pathway, the carnitine shuttle and β-oxidation of fatty acids, and the pyrimidine ribonucleotide interconversion, which are key biochemical processes to sustain cell function (Figure 1.4);⁸⁷  whereas P. mollissima seeds extracts altered the expression of genes and metabolites involved in polyamine and glutathione metabolism.⁸⁸ 

Apart from multi-omics, individual transcriptomics approaches have been used to evaluate the effects of bioactive compound from foods in different disorders. In humans, the effects of virgin olive oil consumption on PBMC gene expression were explored in order to ascertain the molecular mechanisms underlying its beneficial action in the prevention of atherosclerosis;⁹⁰  and the use of PBMC has been reviewed in the study of n-3 fatty acids supplementation.⁹¹  In addition, gene expression in white blood cells (WBC) from healthy adults was investigated after vitamin D3 supplementation diets, suggesting to the authors that vitamin D deficiency is not only related to skeletal health.⁹²  Moreover, different in vitro cell cultured lines have been submitted to transcriptomics studies after treatment with natural compounds. For instance, LNCaP human prostate cancer cells were studied after treatment with genistein;⁹³  HT-29 human colon cancer cells to investigate the effects of epigallocatechin-3 gallate and rosemary polyphenols;^94,95  and Caco-2 human colon adenocarcinoma cells were tested against selenium,⁹⁶  or to study the effect of in vitro-digested yellow and white onion extracts.⁹⁷  This last study was complemented with in vivo studies using rat intestine slices and pig small intestinal segments. Also using a rat model, the cerebral cortex transcriptome was studied after caloric restriction and an α-lipoic acid-supplementation diet, demonstrating the overexpression of neuroprotective genes after the treatment.⁹⁸  Cell cultures from mice have also been used to clarify the similarities and differences between two vitamin D metabolites with respect to global gene expression changes,⁹⁹  and to characterize the potential genotoxic properties of quercetin in the small intestine and liver of mice.¹⁰⁰ 

As well as transcriptomics, proteomics approaches have been mainly used to study the effects of bioactive foods on different models. For example, a small cohort of healthy male volunteers was selected to investigate the alterations in the steady-state levels of PBMC proteins after the daily supplementation of 0.4 g of flaxseed per kg body weight.¹⁰²  By using 2-DE coupled to MALDI-TOF MS, the authors identified 16 proteins affected, some of them atherosclerosis-relevant. On the other hand, more studies have been performed using in vivo models, such as mice or rats. Using mice with xenografted HT-29 human cancer cells, the molecular mechanisms behind the effects of rosemary polyphenols in decreasing tumor growth were evaluated by dimethyl labeling (DML) and nanoLC-ESI-LTQ/Orbitrap MS;¹⁰³  and the effects of cell wall polysaccharides, soy and meat proteins, and Korean red ginseng have been evaluated in rat heart, liver, and spleen and thymus tissues, respectively.^104–106  In this last study more than 2000 proteins were identified in the different tissues using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and nanoLC-ESI-Q/Orbitrap MS, and the molecular signatures and functionality analyses suggested to the authors that Korean red ginseng stimulates the immune responses.¹⁰⁶  Finally, several studies have used in vitro cell culture models, colorectal cancer cells being the most used. For instance, the bioactivity of rosemary polyphenols and liensinine (a constituent of Nelumbo nucifera Gaertn) have been evaluated in HT-29 colon cancer cells.^107–109  In this last study, the use of the advanced Orbitrap Fusion Lumos MS allowed the identification of more than 3300 proteins, and the bioinformatics analyses and complementary experiments indicated the c-Jun N-terminal kinase (JNK)-mitochondrial dysfunction to play a critical role in the anticancer effects of liensinine.¹⁰⁹  Moreover, the human breast adenocarcinoma cell model MCF7 was selected to study the anti-proliferative activity of tiger milk mushroom (Lignosus rhinocerotis) sclerotium,¹¹⁰  and the human hepatoma cell model HepG2 was used to investigate the anti-tumor mechanisms of mistletoe [Viscum coloratum (Kom.) Nakai] polysaccharides.¹¹¹  Furthermore, a novel proteomics approach was chosen to study the possible protein targets of curcumol from Curcuma zedoary in CNE-2 and 5-8f nasopharyngeal carcinoma cells.¹¹²  Using cellular thermal shift assay, molecular docking, cell-based assay, and SDS-PAGE coupled to MALDI-TOF/TOF MS proteomics, the authors identified nucleolin protein as a target of curcumol, indicating that the anti-cancer effects of curcumol are mediated, at least in part, by the loss of nuceolin functions.

In contrast to transcriptomics and proteomics, peptidomics approaches (the analysis of the low-molecular weight subset of the proteome, including peptides and small proteins with molecular weights ranging from 0.5 to 15 kDa) have been mainly used to identify peptides with possible health beneficial effects (bioactive peptides). In dietary proteins, bioactive peptides are encrypted as a part of a protein that remains inactive as long as it is confined within the protein, and later on they are released by in vitro or in vivo proteolysis. The study of bioactive peptides usually requires the development and application of advanced separation methods coupled to MS and bioinformatics tools for the prediction, identification, and characterization of their sequences.²⁰⁰  In addition, these technologies are often complemented with different bioassays to evaluate their bioactive activities (bioavailability assays to test the resistance of peptides to gastric digestion; anti-oxidant, hypocholesterolemic, anti-hyperglycemic or anti-hypertensive in vitro or in vivo assays; or in silico predictions). One of the most studied sources of bioactive peptides are milk and dairy products,²⁰¹  but other sources have been also explored.^{115–118,202,203}  In recent years, food by-products have also gained important interest. For instance, hemp flour and hemp protein isolate have been characterized by 2-DE-LC-ESI-Q-Orbitrap MS,¹¹⁹  and tilapia skin collagen hydrolysates have been investigated in alleviating liver and kidney injuries in aging mice.¹²⁰  Moreover, coffee silverskin protein hydrolysates from coffee beans submitted to different degrees of roasting process have shown antioxidant and hypocholesterolemic activities;¹²¹  and prunus seeds protein hydrolysates have shown angiotensin converting enzyme (ACE)-inhibitory capacity, in vitro cytotoxicity and in vivo antihypertensive activity in rats.¹²²  In this last study, RP and HILIC LC-ESI-Q-TOF MS enabled the identification of 33 peptides, and among them, the oral administration of IYSPH peptide to rats significantly decreased the systolic blood pressure of the animals.

In the case of metabolomics approaches, several studies have used a metabolic profiling approach (focused on the study of a group of related metabolites, such as polyphenols, flavonoids, or carotenoids) or a metabolic fingerprinting approach (for the characterization and comparison of phenotypes between two or more conditions after different diets, as a consequence of a treatment with bioactive compounds, or because of environmental alterations). Due to this heterogeneity, metabolomics studies can be grouped in two main categories: those focused on the evaluation of the metabolomics effects of specific bioactive compounds from food,²⁰⁴  or those focused on the identification and characterization of potential bioactive compounds.²⁰⁵ 

In humans, the metabolomics effects of bioactive compounds or diets have been mainly evaluated in urine, plasma or feces samples, by using diverse technologies. For instance, urine has been analyzed using ¹H NMR to observe the different metabolic responses after intake of red wine polyphenol,¹²⁶  or cholorogenic acids from coffee.¹²⁷  LC-ESI-Q-TOF MS has been used to study the changes due to daily consumption of a dried Curcuma longa L. extract for 28-days,¹²⁸  and to evaluate the effects of long-term cocoa consumption.¹²⁹  The authors of this last study identified three metabolites altered (tyrosine sulfate, butyrylcarnitine, and methylglutarylcarnitine) after cocoa ingestion, but because the absorption, metabolism, and excretion of cocoa metabolites depend on the food matrix, and the dose, age, gender, and overall health status of an individual, more clinical studies are needed to fully understand their possible beneficial effects.^206  1H NMR was also selected to investigate the effects of docosahexaenoic acid supplementation on the plasma metabolome of human volunteers at risk of metabolic syndrome;¹³⁰  and the combination of different separation techniques coupled to MS (LC-ESI-LTQ MS and GC–MS) was applied to investigate the urine and plasma metabolite changes mediated by freeze-dried black raspberries in human colorectal cancer patients.¹³¹ 

The combination of metabolomics and lipidomics platforms based on LC-ESI-LTQ/Orbitrap MS were chosen to characterize Angelica keiskei extracts and to evaluate their beneficial effects on human plasma.¹³²  The results obtained indicated to the authors that five components of Angelica keiskei are responsible of the reduction of bile acids and fatty acids levels after ingestion. Regarding feces samples, even though different metabolomics methods have been developed,¹³³  few studies have been performed. One such study was focused on the evaluation of the effects of four weeks of moderate wine consumption in healthy volunteers by using LC-ESI-Q-TOF MS.¹³⁴  The authors found 37 biomarkers of wine consumption which may reflect changes in microbiota functionality. Other than humans, metabolomics in fluids from rats mimicking different diseases have been used to investigate the biochemical changes and therapeutic effects of a shenfu decoction in chronic heart failure,¹³⁵  crude and processed “Baizhu Shaoyao San” on ulcerative colitis,¹³⁶  green tea polyphenols in ovariectomized rats,¹³⁷  or mango peel and pulp in diabetes.¹³⁸  Taking the last study as an example, 26 and 29 significantly altered metabolites were potentially annotated in serum and liver, respectively, indicating that the mango-supplemented diet exerts significant antioxidant effects due to phenolic compounds, like mangiferin. Moreover, most of the studies aiming to investigate the beneficial effects of food compounds in gastrointestinal diseases have been performed using in vitro models. For instance, the Caco-2 human colon adenocarcinoma model has been used to evaluate the effect of olive pomace on the cell metabolome using ¹H NMR,^139,140  and to investigate the in vitro gastrointestinal protective effects of bee pollen against inflammatory bowel disease using LC-ESI-Q-TOF MS.¹⁴¹  The results of this last study indicate that bee pollen has great therapeutic potential in induced colitis due to the alteration of key metabolites involved in glycerophospholipid metabolism.

Among the studies focused on the identification and characterization of bioactive compound in foods, plants are the most widely studied, and those studies can be grouped according to the metabolomics platforms used. NMR has been used to characterize the bioactive compounds of black raspberry,¹⁴⁷  different date varieties and curcuma species,^148,149  or different plants.^150–152  The combination of NMR and LC with different MS analyzers has been shown to enhance the coverage of compounds identified in argan fruits,¹⁵³  goji berries¹⁵⁴  or Clinacanthus nutans leaves.¹⁵⁵  However, despite the benefits of combining both orthogonal platforms, MS is the most used methodology in metabolomics. It has been used alone to carry out the metabolic profiling of blueberry leaves,¹⁵⁶  but a separation technique before the MS is often desired. CE has been demonstrated to be a very useful analytical tool in food science,²⁰⁷  but only a few studies have coupled it to MS for the identification of bioactive metabolites.^157,158  On the other hand, the use of GC–MS is more widespread, and it has been used to identify and characterize possible bioactive compounds in extracts from mint seeds,¹⁵⁹  nettle leaves,¹⁶⁰  ginseng roots,¹⁶¹  pistachio resin,^162,163  and Nigella sativa seeds.¹⁶⁴  On top of these technologies, the gold standard method of choice is LC–MS. As can be observed from Table 1.2, more than 30 studies have been published on this topic over the last 10 years, and more than half in the last 2 years.^165,194  The starting material is very heterogeneous (fruits, roots, rhizomes, seeds, stems, flowers, or leaves), as are the MS analyzers used (Q, IT, TOF, Orbitrap, or a combination of them: QqQ, QTOF, Q-Orbitrap or IT-Orbitrap). In addition, in several of these studies LC–MS has been complemented by GC–MS to provide a wider picture of the bioactive compounds. For instance, several multi-analytical platforms have been presented for obtaining and characterizing bioactive compounds with anti-proliferative activity in cancer cells from different food by-products (goldenberry calyx,¹⁸⁸  banana passion fruit seeds extracts,¹⁸⁹  or sugar mango seed kernels¹⁹⁰ ); or from different species of algae.^191,192  Apart from plants, another interesting group of foods submitted to metabolomics analyses are beverages such as wine.¹⁹⁵  or infusions from tea leaves or other plants.^196–199 

One of the main challenges in foodomics is related to the great complexity intrinsic to food matrices, and the huge dynamic range of concentrations of food components. The development of novel food analysis methods must face this heterogeneity and avoid the analytical interferences from these matrices in order to improve reproducibility and facilitate biological interpretation of the results.¹  For example, the use of PCR-based methodologies for food safety or authenticity is sometimes limited due to food compounds that inhibit the polymerase reaction.³²  Routine food testing reference methods mostly rely on traditional microbiological analysis techniques. These methods are usually very time-consuming and expensive, as, for example, they need to use bacterial culture-based techniques. However, omics approaches are still underused in food safety mainly due to expensive instrumentation and the high level of experience and technical skills needed for method development as well as for software management and statistical data analysis.^58,66  Moreover, molecular engineering of microorganisms through clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR associated protein 9 (Cas9) and other genome editing methods together with synthetic biology applications have a great potential to modify microbial communities in food, improving processes such as fermentation or generating enhanced probiotic strains. The use of advanced analytical omics technologies must go hand in hand with these technologies in order to evaluate possible unintended effects, ensuring food safety and traceability, and preventing fraud.²⁰⁸ 

Furthermore, the integration of the different omics approaches is still a challenge because of the lack of adequate bioinformatics tools and our limited understanding of the biological and chemical process taking place inside any biological system, which makes the study of the effects of food components on health especially demanding.^58,209,210  In addition, to understand the effects of diet on health as a whole it is necessary to consider many parameters, just to mention a few: the broad nature of food molecules, the microbiota, the inter-individual variability, the food dynamic processing starting from ingestion, and followed by digestion in the gastrointestinal tract, the intestinal transference to the circulation, the transformation by the liver, the usage by every organ, and the final excretion in urine and feces.^211,212 

The achievement of all these goals also requires collaborative work within the scientific community to compare and share data. Therefore, more harmonized and standardized sampling methods, improvements in computational techniques and biological databases (e.g. with functional annotations), and further developments in the analytical technologies used in each specific omics field are essential.

In the transcriptomics field, RNA-seq technology is becoming more affordable and has been applied to the characterization of transcriptomes of different foods, and its wider application in the study of the effects of bioactive food compounds is expected. In the proteomics field, the combination of more sensitive, faster, and higher resolution MS instruments coupled to liquid separations and fractionation techniques will increase the coverage of proteomes, subproteomes and peptidomes. However, there are still some limitations when the time aspect is considered, which is essential for understanding the metabolic and physiological changes occurring during molecular and cellular processes.²¹³  The same technological advances have already improved the peptidomics field,²⁰⁰  but even though bioactive peptides have shown multiple beneficial health activities, the proper exploration of their mechanisms of action and their bioavailability after intake need more clinical trials.^214,215  In the case of metabolomics, great advances in extraction, separation and detection techniques have been performed (such as the introduction of ion mobility analysis), but the main limitations are still the identification and accurate quantification of metabolites. Again, to face these challenges a global scientific effort is required to create, or contribute to the creation of-, standardized and freely available MS and MS/MS spectral databases for the identification of unknown compounds, the development of biostatistical methods, as well as the improvement and application of quantum chemistry and computational methods for elucidating or predicting the structures of novel metabolites^216–218 

Overcoming the above-mentioned limitations will allow scientists to gain a more comprehensive foodomic insight about the relation between food and health, while reinforcing the control of food safety, quality, and traceability.

2-DE

two-dimensional gel electrophoresis

ACE

angiotensin converting enzyme

ASE

accelerated solvent extraction

Cas9

CRISPR associated protein 9

CE

capillary electrophoresis

CE-LIF

capillary electrophoresis with the laser-induced fluorescence detection

CRISPR

clustered regularly interspaced short palindromic repeats

DAD

photodiode array detection

DART

direct analysis in real time

DEGs

differentially expressed genes

DESI

desorption electrospray ionization

DHA

Docosahexaenoic acid

DI

direct injection

DIGE

difference gel electrophoresis

DML

dimethyl labeling

DPPH

2,2-diphenyl-1-picrylhydrazyl staining

EFSA

European Food Safety Agency

EGCG

epigallocatechin-3 gallate

ESI

electrospray ionization

FT-ICR

Fourier transform ion cyclotron resonance

FT-IR

Fourier transform infra-red

GC

gas chromatography

GC-FID

gas chromatography-flame ionization detection

GM

genetically modified

GMO

genetically modified organism

GWAS

genome-wide association study

HILIC

hydrophilic interaction liquid chromatography

HPLC

high performance liquid chromatography

HRMS

high resolution mass spectrometry

IT

ion trap

iTRAQ

isobaric tags for relative and absolute quantitation

LC

liquid chromatography

lncRNA

long non-coding RNA

MALDI

matrix-assisted laser desorption ionization

miRNA

microRNA

mRNA

messenger RNA

MS

mass spectrometry

MS/MS

tandem mass spectrometry

m/z

mass to charge ratio

NGS

next generation sequencing

NMR

nuclear magnetic resonance

PAGE

polyacrylamide gel electrophoresis

PBMC

peripheral blood mononuclear cells

PCR

polymerase chain reaction

PDA

photodiode array

PLE

pressurized-liquid extraction

PTM

post-translational modification

Q

quadrupole

QqQ

triple quadrupole

qRT-PCR

quantitative real-time PCR

Q-TOF

quadrupole-time of flight

RNA-seq

whole transcriptomic sequencing

RP

reverse phase

SDS-PAGE

sodium dodecyl sulfate polyacrylamide gel electrophoresis

TLC

thin layer chromatography

TNF-α

tumor necrosis factor-alpha

TOF

time of flight

TMT

tandem mass tags

UPLC

ultra-performance liquid chromatography

WBC

white blood cells