High-throughput spectroscopic phenotyping of milk

Functional genomics offers new opportunities in milk science research. Phenotypic properties can be related to genotypic information statistically, in order to quantify expected relationships and to discover unexpected relationships. Detailed genomic studies can then reveal the causal genetic basis for these relationships, and lead to more efficient animal breeding as well as improved farm management, animal feeding and milk processing.



Figure 1. Integrative functional genomics: Relating traditional genetic data (breeding, management, production) to DNA data (genome), mRNA data (transcriptome), protein composition (proteome), metabolic profile (metabolome), as well as animal or product quality data and production economy, using advanced mathematical and statistical tools, such as bio-chemometrics.

Figure 1 illustrates the conventional causality in functional genomics. The right-pointing arrows linking the boxes show that the genomic information, representing diversity in the DNA sequence of the animals, is transcribed into mRNA to varying degrees. The transcriptome in turn defines the proteome, which includes the enzymes that produces the variety of metabolites – the so-called metabolome. Together, the proteome and metabolome affect the quality and quantity (animal productivity and – health components, taste, smell and appearance, etc), which in turn affect the over-all economy of the agricultural production. But the left-pointing arrows linking the boxes in the figure illustrate that a range of regulatory feedback mechanisms complicates this functionality track.

The figure also outlines how data, obtained at different stages along this causality track, can be related to external data - from environment, farm management or existing data bases in animal breeding / -health/ -production etc.

In order to obtain relevant results with sufficient statistical reliability, it is advantageous to be able to choose cost-effective measurement techniques: On one hand, low-cost high-speed screening methods based on e.g. multivariate Fourier Transform Infra Red (FTIR) biospectroscopy, can be applied to millions of milk samples or thousands of individual animals in order to identify particularly interesting samples or individuals. On the other hand, the most interesting samples or animals can be submitted to higher-cost detailed studies, e.g. in genomics (characterisation of Singe Nucleotide Polymorphism (SNP) - for genome-wide identification of tens of thousands of genetic markers by micro-arrays or for more focused SNP studies by Maldi-tof MS-based), transcriptomics (micro-array or MS-based) , or proteomics (1- or 2D gel electrophoresis or Maldi-tof MS-based). Economically important but more time-consuming quality assessments, such as consumer studies or feeding experiments, can finally be used for a small set of particularly interesting samples or animals. In between, a number of different types of measurements can also be put to use, to reveal systematic patterns of variation.

Once interesting genomic elements and their diversities have been identified, one can start monitoring how their transcriptome, proteome or metabolome dynamically change with time; the data can be studied by multivariate multi-block dynamic modelling, with links to systems biology.

At Campus Ås, the Norwegian University of Life Science (UMB), the Norwegian Food Research Institute (Matforsk) and several other institutions have made a concerted effort over the last couple of years to ensure sufficient measurement capacity at all stages along this functional genomics causality track.

Several milk- and meat-related research projects, using these new facilities, have recently been financed and initiated on the campus.


Involved people
H. Martens^1,2,3,6,8
A. Kohler^1,2,5,6
N.K. Afseth^1,2,3
J. P. Wold^1,2
M. Hersleth²
I. Berget ^1,2,6
T. Ådnøy⁴
M. Skaugen³
T. Isaksson^1,3
G. Vegarud³
A. Criscione³
B.H.Mevik^1,3
M.B. Frøst⁸
Å. Randby⁴
E. Prestløkken^4,7
P. Berg^4,6
M. Kent^4,6
S. Lien^4,6
S.W. Omholt <sup>4,6

1</sup>Centre for Biospectroscopy and Data Modelling, Campus Ås,
²Norwegian Food Research Institute, Matforsk,N-1430 Ås, Norway
³Department of Chemistry, Biology and Food Science (IKBM), Norwegian U. of Life Sciences,
⁴Department of Animal and Aquacultural Sciences (IHA), Norwegian U. of Life Sciences,
⁵Department of Mathematical Sciences and Technology (IMT), Norwegian U. of Life Sciences,
⁶Centre for Integrative Genetics (CIGENE), Norwegian U. of Life Sciences,
⁷Felleskjøpet Feed Development, N-1432 Ås, Norway
⁸Copenhagen University, Faculty of Life Sciences, Denmark.

Financial support
1. Functional genomics for optimized milk and meat quality’ (2006-2009) funded by the Norwegian Research Council.

Related publications
Martens H, Kohler A, Afseth N K, Wold J P, Hersleth M, Berget I, Ådnøy T, Skaugen M, Isaksson T, Vegarud G, Criscione A, Frøst M B, Randby Å , Prestløkken E, Berg P, Kent M, Lien S, Omholt S W (2007) High-throughput measurements for functional genomics of milk. Journal of Animal and Feed Sciences, 16 (1), 172–189.

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