Introduction

Amino acids (AA) are precursors for the synthesis of protein, other nitrogenous substances (e.g., nitric oxide, urea, uric acid, polyamines, dopamine, thyroxine, heme, and glutathione), glucose, and fatty acids (Galli 2007; Grillo and Colombatto 2007; Li et al. 2007; Sugita et al. 2007). In addition, increasing evidence shows that AA regulate cellular signaling pathways, thereby impacting key metabolic pathways (Jobgen et al. 2006; Kim et al. 2007b; Wang et al. 2008b; Wu et al. 2007a). Thus, the interaction between genes and dietary AA is a complex process involving multi-organ physiology and all levels of regulation (genes, chromatin structure, gene expression, proteins, and metabolites).

The traditional approaches to study AA nutrition include digestibility trials, nitrogen balance, assessments of growth and reproductive performance, isotope tracer techniques (quantification of protein turnover, as well as AA synthesis, catabolism and flux), determination of AA metabolites and their function using various analytical methods, northern and western blots, and evaluation of health status (Fang et al. 2008; Han et al. 2008; Stoll and Burrin 2006; Suryawan et al. 2008; Wu et al. 2008). While these approaches have played a historically important role in protein and AA nutrition research and remain irreplaceable today, they are limited in scope. With the completion of human and other mammalian genome projects, revolutionary technologies in life sciences characterized by high throughput, high efficiency, and rapid computation have been developed. These advanced tools, such as genetics, epigenetics, transcripteomics, proteomics and metabolomics, are now available to determine how AA in the diet may affect both the expression levels and biological activities of DNA, RNA, protein, and low-molecular weight metabolites (Forde and McCutchen-Maloney 2002; Hu et al. 2008a, b; Zhan et al. 2008) in humans and animals (Fig. 1).

Fig. 1
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Omics technologies for nutrition research in the post-genome era. With the completion of human and other mammalian genome projects, revolutionary technologies in life sciences are now available for amino acid nutrition research. These advanced tools include genetics, epigenetics, transcriptomics, proteomics, and metabolomics. These powerful methods can be employed for the analysis of DNA, RNA, protein, and low-molecular-weight metabolites in cells and tissues

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Omics techniques in the post-genome era

Nutrigenetics

It is well known that different individuals (humans or animals) respond differently to the same diet, as indicated by differences in the susceptibility to disease, as well as the efficiency of nutrient absorption and utilization (Kim 2005). Genetic variability is most likely responsible for the diverse biological outcomes. Among genomic variety, single nucleotide polymorphisms (SNPs) are considered to be the major genetic source of phenotypic variability that differentiates individuals within a given species (Gorringe et al. 2007). At the same time, other types of genetic variability (e.g., insertions or deletions of nucleotides and variability in copy number of repeated sequences) also make some contributions to phenotypic changes (Sanders et al. 2008). To address this important issue, a term “nutrigenetics” has been coined, which can be defined as the relationship between diet and genotype as well as its impact on health and disease.

Identifying key SNPs that may influence the nutritional response and health of an individual has been a primary driving force in the development of most nutrigenetic approaches to date (Hu et al. 2008a, b). High-throughput genotyping methods for large-scale association studies include TaqMan®SNP Genotyping Assay®, single-base extension-based assays, mass spectrometry-based methods (e.g., the Sequenom® MassARRAY genotyping system), the Invader assay, Pyrosequencing, and gene chip methods (e.g., the new Affymetrix 500 K SNPChip Array) (Jurinke et al. 2005; Sanders et al. 2008). Accordingly, statistical methods have been developed to analyze this type of experimental data (Fu et al. 2008).

Gene copy number variants, which may be an important source of changes in gene expression that are responsible for much of the variability in an organism’s response to diets, have been recognized (Hu et al. 2008b; Sanders et al. 2008). Thus, future studies will be conducted beyond the consideration of only variability between SNPs, and will also take into account the other types of genetic variability. Genome-wide copy number detection using microarray technologies has been one of the recent research topics on large-scale variations in genomes. Affymetrix GeneChip and Copy Number Analyzer for Affymetrix GeneChip v2.0 allow for not only accurate and high-resolution copy number estimations, but also the analysis of allelic imbalances, thereby providing a powerful platform to explore the complexities of genomes (Ogawa et al. 2007).

Nutriepigenetics

Epigenetics is the study of stable and heritable changes in gene expression or cellular phenotype that occurs without changes in DNA sequence. DNA methylation and covalent and noncovalent modifications of chromatin are well-known epigenetic mechanisms. Noncoding RNA-mediated gene regulation at the levels of DNA and chromatin (e.g., transcriptional gene silencing and activation) is also considered as an epigenetic mechanism. The epigenetic state of the genome is established in early development and can be modified during pregnancy. The fetal genome is generally thought to affect the later life of the organism, which is defined as fetal programming (Kim 2005; Wolff et al. 1998). Nutrition is one of the most important players in the epigenetic repertoire (Wu et al. 2004a). Gene-nutrient interactions can alter embryonic or fetal development, which sets the stage for metabolic regulation, susceptibility to diseases, behavioral adaptation, and nutrient utilization in the adulthood (Wu et al. 2006). Therefore, epigenetic studies aid in explaining how nutrition affects the offspring genome and why individuals respond differently to environmental signals in an inter-generational effect.

Methods commonly used in an epigenetic study are those that map DNA methylation and histone modifications (e.g., bisulfite DNA modification), chromatin immunoprecipitation assay, as well as RNA interference (RNAi). The Methylamp™ Global DNA Methylation Quantification Kit is now developed for detecting global DNA methylation status using genomic DNA (http://www.epigentek.com).

Nutritranscriptomics

The genome is organized in chromosomes within the nucleus of the cell, and is transcriptionally heterogenous. Available evidence shows that transcription of genes is controlled by nutrients (including AA), hormones, and other regulatory factors (Curi et al. 2005; Hu et al. 2008b; Jobgen et al. 2006).

Precursor mRNA splicing is a critical process by which introns are removed and exons are joined together to form a protein-coding mature RNA. There are general four types of alternative splicings: (1) exon skipping, (2) alternative 5′ss selection, (3) 3′ss selection, and (4) intron retention (Kim et al. 2007b). Because of alternative splicing, a single primary transcript can encode multiple protein isoforms. Consequently, more diverse animal proteomes are generated for distinct biological functions.

Short noncoding RNAs in animals also play regulatory roles in gene expression. Most abundant noncoding RNAs are microRNAs (miRNAs), which are small regulatory RNA molecules with 19–25 nucleotides in length (Cullen 2006). Since the discovery of lin-4 and let-7 in Caenorhabditis elegans by Lee et al. (1993), miRNAs have emerged as essential regulators of gene expression in recent years, including the degradation of target mRNA (Giraldez et al. 2006; Zamore et al. 2000), mRNA translation (Lee and Ambros 2001), heterochromatin structure remodeling (Reinhart and Bartel 2002), and transposon silencing (Aravin et al. 2007). Each miRNA has hundreds of evolutionary conserved targets and perhaps more non-conserved targets (Bentwich et al. 2005). Based on a four-genome analysis of 3′-UTRs, approximately 30% of all human genes may be regulated by miRNAs (Lewis et al. 2005). Therefore, miRNAs have regulatory roles in biology and pathophysiology. In addition, miRNA modulation of physiological processes has important implications for nutrition.

Transcriptomics aims at quantifying the levels of expression of all or a selected subset of genes based on the amounts of RNA present in a sample. This technology is useful for assessing simultaneously the relative abundance of transcripts for thousands of genes under various experimental conditions. Transcriptome profiling generally involves DNA, cDNA, oligonucleotide arrays, as well as serial analysis of gene expression (SAGE), and provides a powerful tool to determine effects of nutrients (e.g., AA) on gene expression in cells and tissues (Hu et al. 2008b; Kim 2005). High-throughput technologies, including deep sequencing and splicing-sensitive microarrays, are regular methods for alternative splicing analysis (Ben-Dov et al. 2008). Integration of these technologies with immunoprecipitation and bioinformatic methods will shed light on the regulatory gene network. At the same time, changes in expression of miRNAs can only be detected at the RNA level due to the nature of non-coding miRNAs. In contrast to capillary sequencing, recently available “next generation” sequencing technologies provide inexpensive increases in throughput, thereby offering a more comprehensive view of the miRNA transcriptome.

Most functional genomics experiments, such as gene expression profiling and proteomics, can help us identify genes that are potentially associated with certain biological processes of interest (Hu et al. 2008b). However, reverse genetic approach using RNAi, which was first discovered in C. elegans (Fire et al. 1998) and plants (Jorgensen et al. 1996), has provided a promising method to experimentally test the association of phenotype with genotype of silenced genes. RNAi is post-transcriptional silencing of gene utilizing a homologous double-stranded RNA (dsRNA) (Lehner et al. 2004) and can be delivered in different ways. A small interference (~21 nucleotide) RNA (siRNA) could be synthesized and transfected into cells. To avoid the relatively high cost and transient silencing of this process (Myers et al. 2003), vectors that express short hairpin RNAs have been developed. Subsequently, the nanoparticles generated from cyclodextrin-containing polycations and siRNA were successfully delivered in vivo using the nano technology (Hu-Lieskovan et al. 2005). RNAi has proven to be a powerful method to study gene function and it is expected that cellular mechanism of transcriptional regulation can be further investigated using an RNAi-mediated approach (Kim and Rossi 2008).

Nutriproteomics

Proteins are the final products of gene expression and have important structural and regulatory functions in cells (Forde and McCutchen-Maloney 2002; Liao et al. 2008). Thus, proteomic technology has emerged as a revolutionary discovery tool to study how dietary AA can alter the proteomes of organisms. Using proteomics, researchers can simultaneously display and determine thousands of proteins in a study sample and identify their changes in response to physiological, pathological, and nutritional alterations (Hu et al. 2008a; Wang et al. 2006, 2008c). Currently, the most commonly used proteomics technologies are based on either the specific digestion of proteins by a protease (usually trypsin) or the direct analysis of intact proteins. The protein hydrolysis method, also known as the bottom-up approach, involves 2-dimensional polyacrylamide gel electrophoresis and multidimensional protein identification. In contrast, the analysis of intact proteins requires their chromatographic separation and identification by mass spectrometry (e.g., surface-enhanced laser desorption ionization) (Wang et al. 2006). Recently, several quantitative proteomic techniques have been developed, such as 2D DIGE (difference gel electrophoresis), ICAT (isotope-coded affinity tag), iTRAQ (isobaric tags for relative and absolute quantification) (Chen et al. 2007), and proteolytic O-18- labeling strategies (Miyagi and Rao 2007). Finally, a promising approach for proteomics is the protein microarray technology, which can be used to detect changes in the expression and post-translational modifications of hundreds or even thousands of proteins in a parallel way (Chen et al. 2007). Identification of different phosphoproteins and their phosphorylation sites can be accomplished by combining proteomics with radioactive labeling, phospho-specific staining, immunoprecipitation, or immunoblotting, as well as metal affinity chromatography (Vercauteren et al. 2007; Yan and He 2008). This approach provides informational insights into signaling pathways triggered by nutrients and other factors.

Nutrimetabolomics

Gene expression or protein concentration may only indicate the potential for physiological or metabolic changes in cells, tissues and organs, but does not represent real end-points of the complex regulatory processes in organisms. Nutrimetabolomics, defined as the analysis of effects of diet on the metabolome (a complete set of small-molecule metabolites in a biological sample), is at its infancy in nutrition research. However, this technology has already provided novel and important insights into understanding the metabolic responses of humans or animals to dietary interventions (e.g., He et al. 2008). Currently, the widely used methods for nutrimetabolomics studies involve proton nuclear magnetic resonance (NMR) technology (Bertram et al. 2007) and mass spectrometry (MS). LC-MS, which is complementary to NMR, offers superb sensitivity, but is limited by the essentially nonquantitative nature of the method and the requirement of internal standardization.

Bioinformatics

With the exceedingly large amounts of genome data generated from advanced post-genomics era, great opportunities and challenges subsequently come along the way. Bioinformatics (analysis of metabolic pathways using systems biology approach) plays an essential role in the interface of molecular biology and computational analysis. Biological problems can be approached from cellular and organism levels by studying individual genes and proteins or a gene network using whole genome data (Ideker et al. 2001). Temporal and spatial gene expression changes under different physiological status could also be investigated. Bioinformatics method can help us utilize data on high-throughput DNA sequences, mRNA profile, protein expression, and metabolites to elucidate the mechanisms that regulate the structure and functions of genes, biochemical pathways, and physiological processes.

Application of omics techniques to AA nutrition research

Polymorphism and genetic background related to AA nutrition

Humans or animals in different regions of the world may have different genetic backgrounds. Thus, it is possible that AA metabolism differs among the diverse individuals. Recently studies have been conducted to evaluate the metabolic variability between Swedish and British populations (Lenz et al. 2004), or among American, Chinese and Japanese (Dumas et al. 2006). Cross-population differences were detected in urinary metabolites of AA owing to genetic and cultural differences. The results suggest that polymorphism of a specific gene might result in different patterns of AA utilization among human subjects. In another investigation, Kim (2007) reported that dietary levels of folate and methionine, as well as their interaction with alcohol intake, affected the absorption and availability of dietary methyl donors and, consequently, the development of colorectal cancer. Interestingly, a relatively common polymorphism (C677T) within the MTHFR gene is protective in most of the human population. However, population with this kind of polymorphism may be at increased risk for colorectal cancer when the intake of methyl donors from the diet is low (Kim 2007).

Polymorphism of the genes for AA transporters may affect AA absorption and utilization, and, thus, health, growth and development (Wang et al. 2008d). This is particularly important for the small intestine and kidneys, because they absorb AA from the intestinal lumen and renal tubules into the circulation, respectively, thereby regulating AA homeostasis in plasma. The expression profile of AA transporters was analyzed in the murine duodenum, jejunum, ileum, and colon using high-density microarrays (Anderle et al. 2005). The findings indicated differences in the absorption of carrier-mediated nutrients (including AA) among the different intestinal segments. The most pronounced differences were detected between the adjoining segments of the ileum and colon, reflecting much lower rates of nutrient transport in the colon than in the small intestine. Additionally, the gene profile analysis revealed different expression levels for intestinal AA transporters between animals and humans (Kim et al. 2007a). For example, mice exhibited abundant mRNA levels for peptide transporter HPT1, AA transporters CSNU3, CT1, and ASC1, whereas rats had high expression levels for peptide transporter PEPT1, AA transporters CSNU1, and 4F2HC, in the small intestine (Anderle et al. 2005). In contrast, the highly expressed genes in the human small intestine were peptide transporter HPT1, AA LAT3, 4F2HC, and PROT (Anderle et al. 2005; Kim et al. 2007a). These data are consistent with the known difference in dietary AA intakes per kg body weight among various species (Wu et al. 2007b). Finally, genetic studies established that defects in AA absorption by epithelial cells of the small intestine result in inherited disorders, such as cystinuria, lysinuric protein intolerance, hartnup disorder, iminoglycinuria, dicarboxylic aminoaciduria, hyperammonemia, and hypertension (Bröer 2008). This critical knowledge has led to successful development of effective therapeutic treatments for the affected individuals.

AA nutrition and epigenetics

DNA methylation is an important epigenetic determinant in gene expression, the maintenance of DNA integrity and stability, chromosomal modifications, and the development of mutations (Kim 2005). Early life nutrition has the potential to change chromatin structure, alter gene expression, and affect health throughout the life cycle (Wu et al. 2006). Emerging evidence suggests that alterations in epigenetic marking of the genome may be a key mechanism by which nutritional exposure in utero can influence gene expression, and therefore, phenotype (Wang et al. 2008a; Wu et al. 2006). In addition, maternal nutrition can affect the phenotype of offspring by influencing the degree of CpG methylation (Wu et al. 2004a).

The methyl groups that participate in DNA methylation reactions are ultimately derived from methionine. Since levels of methionine in plasma are influenced by diet, epidemiological data suggest that changes in dietary contents of methyl donors could alter DNA methylation, as well as specific and global gene expression. A recent study with ewes reveals that restricting the dietary provision of methionine and folate during the periconceptional period can lead to widespread epigenetic alterations to DNA methylation in offspring and modify adult health-related phenotypes (Sinclair et al. 2007). In addition, a folate- and methyl-deficient diet can alter the expression of DNA methyltransferases and methyl CpG binding proteins involved in epigenetic gene silencing in livers of F344 rats (Ghoshal et al. 2006). Dietary interventions with AA and other nutrients can affect S-adenosylmethionine availability (Grillo and Colombatto 2007), which can facilitate many crucial methylation reactions in pregnant mice to modulate the epigenetic DNA methylation of specific genes (Waterland 2006). These very complex effects suggest that adequate intake of methionine from the diet can maintain optimal DNA methylation in cells. Similarly, methionine supplementation can induce hypermethylation of DNA in specific genomic regions. When methionine content is low in the diet, it is important that dietary supplementation with this AA can support therapeutic maintenance of DNA methylation without causing excessive or potentially adverse locus-specific hypermethylation (Waterland 2006). For example, feeding pregnant black a/a mice a diet supplemented with methyl group donors may alter epigenetic regulation of agouti expression in their offspring, as indicated by increased agouti/black mottling in the direction of the pseudoagouti phenotype (Waterland 2006). Of particular interest, epigenetic phenotypes are also maternally heritable (Wolff et al. 1998). Furthermore, the inherent change of the epigenomic marks that are established early in life through behavioral programming is potentially reversible in the adult in response to dietary supplementation with methyl group donors (Weaver et al. 2005).

AA nutrition and transcriptomics

Transcriptomics is a powerful tool in discovery research regarding the effect of AA on global gene expression. For example, Fu et al. (2005) reported that dietary arginine supplementation selectively reduced white-fat mass in Zucker-diabetic fatty rats (Fu et al. 2005). Similarly, supplementing arginine to the diet for growing-finishing pigs decreased body white-fat, while increasing muscle protein gain (Tan et al. 2008). At the molecular level, microarray analysis indicated that the arginine treatment increased expression of AMP-activated protein kinase, nitric oxide synthase-1, and peroxisome proliferator-activated receptor γ coactivator-1alpha (a master regulator of mitochondrial biogenesis) by 123, 145, and 500%, respectively, in the adipose tissue of Zucker-diabetic fatty rats (Fu et al. 2005). In addition, AMP-activated protein kinase triggers the oxidation of fatty acids and glucose in skeletal muscle, heart, liver, and adipose tissue, whereas cGMP, whose production from GTP is increased by nitric oxide, stimulates the mitochondrial oxidation of acetyl-CoA in cells (Jobgen et al. 2006). All of these changes are expected to promote the mitochondrial oxidation of energy substrates, thereby decreasing the availability of acetyl-CoA for fatty acid synthesis.

Glutamine is the most abundant free AA in the body and plays a regulatory role in physiology and nutrition at gene and protein levels (Curi et al. 2005). The underlying mechanisms involve changes at metabolism (e.g., oxidative fuel, gluconeogenic precursor and lipogenic precursor), cell integrity (survival, cell proliferation), protein synthesis and degradation, redox potential, respiratory burst, insulin resistance, insulin secretion, and extracellular matrix synthesis (Curi et al. 2005). Dietary supplementation with glutamine has been shown to regulate expression of many genes related to metabolism, signal transduction, cell defense, and repair and to activate intracellular signaling pathways (Wang et al. 2008a). Particularly, the glutamine treatment increased intestinal expression of genes that are necessary for cell growth and removal of oxidants, while reducing expression of genes that promote oxidative stress and immune activation (Wang et al. 2008a). Functionally, glutamine supplementation enhanced intestinal oxidative-defense capacity, prevented jejunal atrophy, and promoted small intestine growth and body weight gain in weaned piglets (Wang et al. 2008a; Wu et al. 1996b).

High-throughput technologies, including deep sequencing and splicing-sensitive microarrays, have provided much evidence that alternative splicing is an important process for gene regulation (Ben-Dov et al. 2008). For example, Drosophila sex determination (McIntyre et al. 2006) and mammalian synaptic transmission (Ule et al. 2005) have been reported to be associated with alternative splicing process. Seventy-five percent of alternative splicing events have been related to subtle AA substitutions, removal of protein motif, or protein truncations of coding regions (Blencowe 2006; Matlin et al. 2005).

AA nutrition and proteomics

Comparative functional proteomics enables the characterization of differentially expressed proteins in intestinal cells under apoptotic conditions (Lenaerts et al. 2006). For example, addition of glutamine to culture medium affected expression of 28 proteins in the human epithelial intestinal cell line HCT-8 (Deniel et al. 2007). Interestingly, 34, 17, and 13% of them are involved in cell cycle and apoptosis, in signal transduction, and in cytoskeleton organization, respectively. Furthermore, glutamine has been reported to regulate expression of several proteins (e.g., p47phox, p22phox, gp91phox, alpha-actin and fibronectin, ASK1, c-myc, c-jun and p70s6 k) in animal cells (Curi et al. 2005).

Methionine restriction can inhibit the growth of cancer cells, which cannot grow in a methionine-depleted medium supplemented with homocysteine, a toxic metabolite of methionine (Perta-Kajan et al. 2007). Comparison of the 2-DE patterns of SGC7901 cells cultured in M + H- or M-H + medium revealed ten differentially expressed proteins in gastric cancer cells SGC7901 (Xin et al. 2007). These proteomics data are invaluable for defining the molecular mechanisms by which methionine restriction can induce cell cycle arrest and apoptosis in human gastric cancer (Xin et al. 2007).

l-Arginine has a beneficial role in angiogenesis and the cardiovascular system (Montanez et al. 2008). Using the proteomics technology, we found that adding a physiological level of arginine (0.1 mM) to an arginine-deficient medium increased expression of structural proteins (vimentin and tropomyosin) and mitochondrial cytochrome bc1 complex iii- chain A (a component of the electron transport chain) but decreased expression of stress-related proteins (PDZ domain containing-3) in coronary vascular endothelial cells (Lei et al. 2008). The analysis of real-time PCR and western blotting confirmed elevated mRNA and protein levels for vimentin in arginine-treated cells. These novel findings aid in elucidating the mechanisms responsible for the beneficial effect of physiological levels of l-arginine on the circulatory system. Additionally, proteomic analysis revealed that arginine deprivation in Caco-2 cells decreased cell proliferation and heat-shock-protein expression, while enhancing the susceptibility of cells to apoptosis (Lenaerts et al. 2007).

AA nutrition and metabolomics

Metabolomics has emerged as a biomarker discovery tool for metabolic profiling in nutritional research. The metabolome in body fluids or tissues may provide a more comprehensive view of cellular metabolic regulation and an opportunity of identifying surrogate markers for nutritional or disease status. For example, an NMR-based metabolomic study was conducted to investigate different biochemical effects of a short-term high intake of milk or meat protein on children (Bertram et al. 2007). The results indicated that the milk diet increased the urinary excretion of hippurate, while the meat diet increased the urinary excretion of creatine, histidine, and urea. This reflects a higher amount of creatine and histidine in meat than in milk, as well as the difference in AA composition between milk and meat proteins. Additionally, the NMR analysis indicated that the milk diet changed the lipid profile of serum, but the meat diet had no effect (Bertram et al. 2007).

Some metabolomics studies have also evaluated the effect of proteins of animal versus plant origin on metabolic profiles in humans. Higher urinary levels in creatine, creatinine, carnitine, acetylcarnitine, taurine, trimethylamine-N-oxide, and glutamine are the metabolic signatures of the high-meat diet (Rezzi et al. 2007). In contrast, higher urinary excretion of p-hydroxyphenylacetate and a lower level of N, N, N-trimethyllysine are associated with the vegetarian diet (Rezzi et al. 2007). Thus, meat intake may promote higher production of ammonia, but consumption of vegetable protein may reduce intestinal microbial activity (Yin et al. 2008).

Using 1H-NMR spectroscopy, He et al. (2008) determined the effect of dietary arginine supplementation on the serum metabolome in growing pigs. Principal component analysis indicated that serum concentrations of low-density lipoprotein, very low-density lipoprotein, and urea were lower, but concentrations of creatinine, tricarboxylic-acid-cycle metabolites, ornithine, lysine, and tyrosine were greater in arginine-supplemented than in control pigs. Additionally, the arginine treatment affected serum concentrations of nitrogenous and lipid signaling molecules (glycerophosphorylcholine and myo-inositol) and intestinal bacterial metabolites (formate, ethanol, methylamine, dimethylamine, acetate, and propionate). These novel findings suggest that dietary arginine supplementation alters the catabolism of AA in the whole body, enhances protein synthesis in skeletal muscle, and modulates intestinal microbial nitrogen metabolism in growing pigs.

The developmental change of metabolites in biological fluid may provide important clues about their nutritional significance (Kong et al. 2008; Li et al. 2008). For example, AA profiling has revealed that arginine (an essential AA for neonates) is remarkably deficient in sow’s milk (Wu and Knabe 1994) and present in reduced levels in plasma of 7- to 21-day-old piglets compared with newborn piglets (Wu et al. 2004b). In contrast, arginine is highly abundant in porcine allantoic fluid during early gestation (Wu et al. 1996a). Based on these findings, a strategy of arginine supplementation was developed to enhance the growth of milk-fed young piglets (Kim and Wu 2004, 2008; Wu et al. 2004b) and improve the reproductive performance of gilts (Mateo et al. 2007, 2008; Wu et al. 2008) and rats (Zeng et al. 2008).

Thus, analysis of AA and their metabolites as a metabolomic subset, together with the correlation-based analyses, can help identify pathways responsible for the effects of excessive intakes of AA from the diet (Noguchi et al. 2003). In contrast to the conventional single metabolite studies (Dekaney et al. 2008; Ou et al. 2007), comprehensive analysis of biomarkers can offer an unbiased strategy for global assessment of AA metabolism and requirement by humans and animals (Ptolemy et al. 2007). In light of such findings, optimal dietary recommendations for AA can be devised for individual humans (personalized nutrition) and animals (targeted feeding) in response to changes in physiological and pathological conditions.

Conclusion and perspectives

To date, the study of biochemical mechanisms that regulate dietary AA metabolism and requirements has been largely based on a black-box approach. Importantly, the recent developments of omics techniques are transforming AA nutrition research in the post-genome era. These powerful discovery tools allow for studies of the function of AA and enable in-depth investigation of the regulatory roles for dietary AA in gene and protein expression (Table 1). Thus, omics and other advanced methodologies potentially play an important role in refining optimal individual requirements of AA for health and production benefits, but they are currently not yet widely applied to nutrition research because of relatively high costs and a lack of the necessary knowledge. There are a very large number of proteins and metabolites, as well as their exceedingly dynamic ranges, diverse physicochemical properties, and complex interactions in cells (Liu et al. 2008; Wang et al. 2008e; Zu et al. 2007). Therefore, integrating various omics technologies and bioinformatics with the traditional techniques will provide comprehensive information about AA metabolism and nutrition in organisms. Such a revolutionary approach is expected to solve major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, aging, and intrauterine growth retardation).

Table 1 Roles of the omics technologies as discovery tools in amino acid nutrition research
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