Written by: | Issue # 18 | 2013
- Metabolomics, the youngster of the “omics” family, offers new opportunities for the dairy industry.
- The milk metabolome is a snapshot of the metabolism of the cow and provides new predictors for a cow’s health.
- Measuring metabolites in milk might help to produce better cheeses and milk rich in oligosaccharides.
- Integrating different “omics” techniques will have a synergetic effect, independent of the original application field.
A quick internet search with the words “genomics” retrieves about 14 million hits, whereas the same search with the word “metabolomics” retrieves a mere 1.1 million hits. This alone is a fair indication that within the “omics” family there are different generations, and metabolomics is one of its youngest members.
Metabolites are small molecules with functions as diverse as signaling, inhibition, and defense. Metabolites can be intermediates or products of synthesis or degradation processes and pathways. A typical example of degradation involving metabolites is drug metabolism; any compounds originating from the initial active compound of a drug is a drug metabolite. On the other hand, metabolites are also amino acids, organic acids, sugars, etc., all of which originate from metabolic processes allowing the degradation of a drug. In other words, metabolites are ubiquitous compounds involved in every process in the body or organism.
The word “metabolism” originates from the Greek word Μεταβολισμος, meaning “change.” It is the dynamics of metabolites, either intermediate or final products of metabolism, that scientists measure. All metabolites—the metabolome—can be measured both in tissues and in biofluids involved in providing and removing metabolites. Examples of biofluids used in metabolomics include saliva, blood, and urine, and also milk.
The catalyst of metabolomics
Identification of metabolites in readily available biofluids is a long-standing practice. In ancient China, sweet-tasting urine was recognized as an indicator of a disease, later recognized as diabetes. Nowadays, cutting edge techniques allow the simultaneous measurement of approximately 200 metabolites, each assigned to a different class. With these resources, metabolomics is attempting to unravel different metabolic pathways as soon as possible, aiming at answering different questions.
Probably due to economic availability, the metabolomics concept and techniques have initially been applied in the field of cancer, where it has provided great help in identifying biomarkers of specific carcinogenic pathways. These have been pinned down and integrated into diagnosis and therapeutic approaches. If these biomarkers for carcinogenicity or therapeutics could be analyzed by a faster, more accessible and affordable technique, this could mean a significant improvement in health care.
Milk, the white gold of metabolomics
So why not adopt the same line of thought to dairy? Milk is a readily available fluid and can be considered to mirror the health status of the cow and milk synthesis process. Therefore, metabolites could be used as biomarkers to better understand both the cow’s health and milk production.
In one line of research, milk metabolites are used to predict economically important milk traits. Poor coagulation properties of milk result in low cheese yields and low-quality cheeses. Looking at particular metabolites, the work of Suskindle et al. (2011) has correlated a higher concentration of citrate, a molecule that binds calcium, with poorer coagulation properties of milk, possibly by disrupting the casein micelle, a protein-structure paramount in cheese making. The same work has correlated the well-studied choline and carnitine with milk coagulation properties.
In another line of research, milk metabolites are used to assess animal health. Metabolic profiles of milk correlate organic acids hippurate and lactate with subclinical mastistis—infection of the udder. Whereas lactate is probably produced by bacteria presence in milk, the presence of hippurate is likely associated with the protective capability of certain organic acids against bacterial growth. Importantly, there is a need to identify biomarkers predictive of metabolic status and health. Klein et al. (2012) identified in healthy cows that the ratio of glycerophosphocholine to phosphocholine can be used to predict ketosis development. This is a major advance since current methods can only identify ketosis during the acute stage.
Metabolomics gets a little help from the other members of the “omic” family
If a metabolic approach is so promising, what are the possibilities if its information is joined with that gathered from other “omic” techniques? Recently, the work of Lu et al. (2013) showed that integrating metabolomics and proteomics provided a better understanding of the mammary physiology of cows in negative energy balance (NEB). In dairy cows, after calving, feed intake cannot support energy needed for body maintenance and sudden requirements for milk production. Cows enter and remain in NEB for several weeks, a status that is often associated with several metabolic diseases. When feed intake can again fulfill energy requirements, cows are at a positive energy balance (PEB). Proteomics and metabolomics results indicated glucose-1-phosphate and stomatin, a protein present in the milk fat globule membrane, as possible biomarkers for NEB. Importantly, it was the synergetic collaboration between these techniques that suggested the integrity and organizational properties of the epithelial cells of cows in NEB and PEB are different.
Further, metabolomics can be integrated with genomics and used in the identification of specific traits of animals. The work of Wittenburg et al. (2013) combined the milk metabolome with genetic information, and although several milk metabolites may not be suited to breeding strategies, some have significant genetic variability. By using metabolite concentration as a selection factor, breeding strategies may be used to improve animal robustness to specific disorders.
While genomics and transcriptomics identify genes and pathways that are important in specific processes, proteomics incorporates the knowledge of function and structure of proteins. Understanding milk synthesis and its relation to animal health (metabolic status) is an intricate and dynamic concept. Metabolomics can be the missing link.
If it is possible to identify specific milk components that are important to human (or cow) health and to better understand how and why they are present in milk, it may be possible to apply specific management or breeding strategies to dairy farming. However, it is imperative that different “omics” techniques be incorporated to allow a greater profit not only for the dairy industry, but also for the improvement of both cow and human nutrition and health. For example, bovine milk oligosaccharides (BMO) resemble human milk oligosaccharides and share similar beneficial properties. Animal management and breeding strategies may favor the presence of important metabolites paramount in the production of BMO. These findings could aid in prioritizing, selecting, and isolating BMO to add to infant formula aiming at mimicking the benefits of human breast milk in infants’ gut flora. Expect applications of milk metabolomics to continue to grow.
1. Klein MS, Buttchereit N, Miemczyk SP Immervoll AK, Louis C, Wiedemann S, Junge W et al. (2012) NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis. J Proteome Res 11:1373-1381.
2. Lu J, Antunes Fernandes E, Páez Cano AE, Vinitwatanakhun J, Boeren S, Van Hooijdonk T, Van Knegsel A et al. (2013) Changes in milk proteome and metabolome associated with dry period length, energy balance, and lactation stage in postparturient dairy cows. J Proteome Res 12:3288–3296.
3. Sundekilde UK, Frederiksen PD, Clausen MR, Larsen LB, Bertram HC. (2011) Relationship between the metabolite profile and technological properties of bovine milk from two dairy breeds elucidated by NMR-based metabolomics. J Agric Food Chem 59:7360-7367.
4. Wittenburg D, Melzer N, Willmitzer L, Lisec J, Kesting U, Reinsch N, Repsilber D. (2013) Milk metabolites and their genetic variability. J Dairy Sci 96:2557-2569.