Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example.

Prediction of nutrient partitioning is a long-standing problem of animal nutrition that has still not been solved. Another substantial problem for nutritional science is how to incorporate genetic differences into nutritional models. These two problems are linked as their biological basis lies in the relative priorities of different life functions (growth, reproduction, health, etc.) and how they change both through time and in response to genetic selection. This paper presents recent developments in describing this biological basis and evidence in support of the concepts involved as they relate to nutrient partitioning. There is ample evidence that at different stages of the reproductive cycle various metabolic pathways, such as lipolysis and lipogenesis, are up or down regulated. The net result of such changes is that nutrients are channelled to differing extents to different organs, life functions and end-products. This occurs not as a homeostatic function of changing nutritional environment but rather as a homeorhetic function caused by the changing expression of genes for processes such as milk production through time. In other words, the animal has genetic drives and there is an aspect of nutrient partitioning that is genetically driven. Evidence for genetic drives other than milk production is available and is discussed. Genetic drives for other life functions than just milk imply that nutrient partitioning will change through lactation and according to genotype - i.e. it cannot be predicted from feed properties alone. Progress in describing genetic drives and homeorhetic controls is reviewed. There is currently a lack of good genetic measures of physiological parameters. The unprecedented level of detail and amounts of data generated by the advent of microarray biotechnology and the fields of genomics, proteomics, etc. should in the long-term provide the necessary information to make the link between genetic drives and metabolism. However, gene expression, protein synthesis etc, have all been shown to be environmentally sensitive. Thus, a major challenge in realising the potential afforded by this new technology is to be able to be able to distinguish genetically driven and environmentally driven effects on expression. To do this we need a better understanding of the basis for the interactions between genotypes and environments. The biological limitations of traditional evaluation of genotype ×  environment interactions and plasticity are discussed and the benefits of considering these in terms of trade-offs between life functions is put forward. Trade-offs place partitioning explicitly at the centre of the resource allocation problem and allow consideration of the effects of management and selection on multiple traits and on nutrient partitioning.