Role of proteomics in nutrigenomics and nutrigenetics

Nutrition & health

Food is the only physical matter we take into our body, if we disregard the air we inhale. It is, thus, logical and natural that nutrition exerts the strongest life-long environmental impact on human health and this interplay between nutrition and, health has been known for centuries: the Greek doctor Hippocrates (4th Century BC) recommended using food as medicine and vice versa; Sun Si-Miao, a famous doctor of the Tang Dynasty (7th Century AD) stated that, “when a person is sick, the doctor should first regulate the patient’s diet and lifestyle.”

The new era of nutrition research translates this empirical knowledge to evidence-based molecular science, because food components interact with our body at system, organ, cellular and molecular level [1]. Modern nutrition and health research focuses on promoting health, preventing or delaying the onset of disease and optimizing performance [2]. These objectives require holistic approaches, since nutritional improvement of one health aspect must not be compromised by the deterioration of another [3].

Proteomics, nutrigenomics & nutrigenetics

Dietary components appear in complex mixtures and, hence, not only the concentrations of single compounds but also the interactions between them have an impact on final ingredient bioavailability and bioefficacy [4]. Proteins are the key actors in virtually all biological processes in the human body. Proteomics is a central platform in nutrigenomics, which attempts to holistically understand how our genome is expressed as a response to diet [5]. Nutrigenetics focuses on our genetic predisposition and susceptibility towards diet [6], and can be deployed to stratify cohorts of subjects enrolled in nutritional intervention studies and to discern responders from nonresponders among those subjects [7]. Epigenetics encompasses the investigation of DNA sequence-unrelated biochemical modifications of both DNA itself and DNA-binding proteins, and appears to provide a format for metabolic imprinting that can last for a life stage, entire life time or, even, can be inherited from one generation to another [8,9]. Proteomics also plays a key role here, as it can address post-translational modifications (e.g., acetylations) of DNA-packaging proteins and can, thereby, help decipher the so-called histone code [10,11].

Overall, proteomics in nutrition identifies, as well as quantifies, bioactive proteins and peptides, and addresses questions of nutritional bioefficacy by elucidating protein and peptide markers [12,13]. Briefly, it delivers bioactives and biomarkers. We deploy genomic platforms, such as transcriptomics, proteomics and metabonomics, to demonstrate nutritional efficacy; we source genetic techniques to reveal predisposition and susceptibility, and we apply epigenetics to understand metabolic programming and imprinting.

Proteomic tasks

Protein identification

The rate-limiting analytical challenge in proteomics is, in my opinion, neither the mass accuracy (currently down to sub-ppm), nor the mass resolution (currently up to several 100,000), nor the absolute sensitivity (currently down to attomolar range) of current mass spectrometers, but the dynamic range of protein concentrations (e.g., estimated to be 10¹² in human blood) [14], which still leads to under-sampling, redundancy and irreproducibility in protein identifications and quantification [15,16]. Current mass spectrometry (MS)-based proteomic platforms can deliver a dynamic range of 10⁴. This means that the remaining, as such inaccessible, low-abundance proteome has to be addressed by either depletion of the most-abundant proteins (e.g., by the commercially available multiple affinity removal system that specifically removes the top seven or even 14 plasma proteins) [17], or by selective enrichment of low-abundance proteins (e.g., by the immobilized metal affinity chromatography or titanium dioxide techniques for phosphoproteins [18], lectins [19] or the cell-surface capture technique for glycoproteins [20]). Another way of dealing with the dynamic range is ‘focus with a gain in depth at the expense of breadth’, and this is based on targeted MS analysis as discussed in the following sections.

Protein quantification

Current means for protein quantification are either gel- or MS-based [21]. The gold standard for 2DE-rooted proteomics is quantification by DIGE (i.e., by differential staining of the separated proteins and image analysis). Alternatively to DIGE, and compatible with online shotgun liquis chromatography (LC)-MS/MS workflows, stable isotopes can be introduced into the conditions in question. These tagging techniques can be executed either at protein (e.g., AniBAL [22]) or peptide (e.g., ICAT [23], iTRAQ [24] or TMT [25]) level, and can be introduced either chemically into the sample (e.g., ICAT, iTRAQ, TMT or AniBAL) or metabolically by feeding cells or even small animals (i.e., mice and rats) with stable isotopically labeled amino acids in cell culture (SILAC) [26]. The quantification read-out can be obtained either at MS- (ICAT, SILAC, AniBAL) or MS/MS-level (i.e., iTRAQ and TMT).

While it is, on one hand, preferred to introduce a label as upstream as possible in the workflow in order to maximize coprocessing of case(s) and control(s) and minimize bias (e.g., achieved in the cases of the metabolic SILAC and the chemical AniBAL method), it may, on the other hand, be advantageous not to label at all in order to compare samples directly as they are. Therefore, so-called ‘label-free’ approaches have been developed that either deploy spectral counting of peptide assignments for semiquantitative analysis or compare the peak intensities of the very same peptide by overlaying LC-MS runs of control and case sample [27,28].

Particularly in nutrition, it is also desirable to generate information on absolute amounts of proteins present in a given sample, (e.g., the basis of proven ingredient bioavailability and bioefficacy is its absolute amount in the original food matrix, as well as in the relevant body fluids or tissues). The targeted, multiplexed peptide leve version is termed the absolute quantification approach (AQUA) [29], the protein level variant is described as QConCat (artificial, expressed proteins consisting of stable-isotope-coded peptides representing the proteins to be quantified) [30] or protein standard absolute quantification (PSAQ; i.e., spiking of the labeled protein of interest) [31]. The analogous proteome-scale strategy with determination and synthesis of labeled unique peptide identifiers for all proteins is known under the concept of ‘proteotypic’ peptides [32]. Both the labeled proteotypic peptide standard and its unlabeled, natural counterpart are typically identified and quantified by a targeted MS/MS acquisition mode known as ‘selected-’ or ‘multiple-reaction monitoring’ [33,34]. These methods can be understood as highly sensitive, multiplexed ‘MS-based ELISAs’ that do not depend on 3D structure-based recognition of protein epitopes.

Proteomics in nutrition

Our group and others have contributed to the introduction and adaptation of proteomics to the field of nutrition and health [35]: applications were summarized under different topics, including nutritional intervention, elucidation of immune-related gut disorders [36], characterization of functional ingredients, such as probiotics or milk and soy proteins, or the investigation of perturbed energy metabolism, such as in diabetes and obesity [37]. Moreover, numerous articles on nutritional intervention studies [38,39] and mechanistic elucidation of nutrient action [40] were published. However, nutrition is still a relatively young field for proteomics compared with clinical [30] and medical applications [32]. The future development and success of proteomics in nutrition and health will depend on several factors:

• • The proteomic technology platforms, independent of their application, will benefit from ever-improving protein/peptide separation techniques, better depletion and enrichment methods, and more-sensitive and -specific mass determination, and sequencing techniques;

• • The analytical strategy bears great potential for result improvement: intelligent focusing on proteome subsets, such as at cell organelle, protein subclass [41,42], or mass spectral level (proteotypic peptides and selected-reaction monitoring [43]) will yield less complex proteomes but provide deeper insights into molecular networks;

• • The third area of platform-related improvements covers bioinformatics, (i.e., the tools to assess data quality and to convert data into interpretable information [44]). Current ‘gaps’ in ’omics data sets may be elucidated by reconstructing pathways and regulatory networks, even in the presence of fragmentary data [45]. If these network-reconstructing and motif-elucidating tools prove to be successful, they may also shed a different light on the terms ‘reproducibility’ and ‘comparability’ of ’omics studies; rather than searching for the same transcripts/proteins/metabolites found regulated between related studies, one may focus on the common motifs behind these – often at first glance divergent – datasets;

• • Proteomics will largely benefit from cross-correlation with gene-expression analysis and metabolite profiling. However, the inter-related timing of gene and protein expression, as well as metabolite generation, remains to be understood [46]. One possible solution is addressing protein turnover at proteomic scale; rather than taking proteomic ‘snapshots’, it has recently become possible to interpret protein abundance changes as a combined result of protein synthesis and degradation [47]. Proteome turnover experiments performed with stable-isotope-labeled amino acids, peptides and proteins has the potential to deliver a different quality of nutritional biomarkers;

• • Genetic susceptibility may predispose an individual to a diet-induced disease and/or render this individual more or less susceptible to dietary intervention [48,49]. Siffert et al . have, for example, identified and characterized metabolically relevant single nucleotide polymorphisms in G-proteins, the latter representing an important ‘funnel’ of cellular signaling. These polymorphisms predispose individuals of different ethnicity to having a higher risk of developing hypertension, atherosclerosis, metabolic syndrome or functional dyspepsia [48,50];

• • Epigenetic changes, such as DNA methylation (gene silencing) and histone acetylation (chromatin structure) should be considered in long-term nutritional intervention, as these mechanisms strongly influence gene transcription and expression;

• • In humans, dietary changes represent rather subtle interventions, often with many small rather than a few big molecular changes. Improved definition of human cohorts undergoing dietary interventions through both geno- and pheno-typing should deliver clearer readouts from ’omic applications. Nutritional intervention studies, especially those that deploy proteomics, should be based on standardized diets and ingredients, stratified cohorts and, ideally, follow the double-blinded, placebo-controlled, crossover design.

Financial & competing interests disclosure

The author is an employee of the Nestlé Research Center. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.