Written by: Peter Williamson, Ph.D. | Issue # 25 | 2014
- Neonatal growth is dependent on lactation sufficiency.
- Mice are an animal model of growth.
- Pre-genome: a broad region of the genome was mapped for neonatal growth.
- Post-genome: we can use integrated strategies to zoom into these regions.
- The study demonstrated how this strategy may be used to identify individual genes.
We all know that newborn babies need frequent and adequate nutrition to get a good start on life. Indeed, it is a particularly susceptible period when insufficient nutrition or complications can be life threatening. In recent years, it has also emerged that nutrition and the pattern of growth in this period can affect lifetime health and well being. By far the best source of nutrition during this period is mother’s milk, which is tuned to the babies nutritional and growth requirements. So as you might expect, there is a close relationship between the volume and composition of milk consumed by babies and their rate of growth.
What makes some babies grow faster than others? A recent publication by my own group has looked into the rates of growth in mice and used an integrated genomics strategy to identify genes that are important in lactation and related to the differences that contribute to growth of neonates (1). This study was conducted by Palani Ramanathan and Jerry Wei when they were graduate students in my lab at the University of Sydney. They were interested in the maternal-neonatal interaction and how milk production contributes to growth across a series of mouse strains, where the rates of growth vary greatly and the capacity of the dam to sustain a litter depends on milk produced.
Before we had genome science, a small number of studies were conducted using traditional genetic approaches in cross-bred mice. These studies and similar approaches in other species, identified regions of the genome that had a statistical association with neonatal growth, but the sizes of the regions were very large and contained so many genes that it was impossible to predict how they were linked. In the post-genomic world, experimental tools have been developed to refine the process of discovery and improve its sensitivity and accuracy.
In this study, the daily weights of litters from inbred strains of lactating mice were measured for the first eight days of life. We previously had established that this period of growth was predictive for the full lactation period in mice and hence the amount of milk that was produced (2). Using inbred strains of mice has the advantage of negating genetic variation within the strain, so we could concentrate on the differences between the strains. The other advantage is that many inbred strains of mice are now fully genotyped; that is, we know their genetic makeup in great detail. By combining this level of detail with measures of genes that are switched on during lactation (3), we could reduce the number of potential genes that were driving this process considerably. This report focused on the regions that were most prominent in neonatal growth, referred to collectively as Neogq. When Palani and Jerry applied the algorithm to these regions, they narrowed down the areas under investigation to much smaller stretches of DNA that each contained only one gene that was switched on during lactation, e.g. in the region of greatest interest the gene Neo1 stood out.
We continue to apply this approach to other measures that are relevant to maternal performance (4-6), and along with colleagues that have approached similar questions in other studies, we now have an intricate picture of genes that are fundamentally important in lactation and how these are crucial to neonatal growth (e.g., (7-9)). The mouse remains an important and informative animal model for these studies and will continue to produce leads for investigation in other species of interest, most notably humans, in trying to elucidate the complexities of neonatal growth and well being (10).
References
1. Ramanathan P, Wei J, Martin IC, Thomson PC, Moran C, Taylor RM, et al (2014). An integrated genomics approach to identify genetic regions associated with neonatal growth trait in mice. Anim Biotechnol 25:85-97.
2. Ramanathan P, Martin I, Thomson P, Taylor R, Moran C, Williamson P (2007). Genomewide analysis of secretory activation in mouse models. J Mammary Gland Biol Neoplasia 12:305-314.
3. Ramanathan P, Martin IC, Gardiner-Garden M, Thomson PC, Taylor RM, Ormandy CJ, et al (2008). Transcriptome analysis identifies pathways associated with enhanced maternal performance in QSi5 mice. BMC Genomics 9:197.
4. Hadsell DL, Wei J, Olea W, Hadsell LA, Renwick A, Thomson PC, et al (2012). In silico QTL mapping of maternal nurturing ability with the mouse diversity panel. Physiol Genomics 44:787-798.
5. Wei J, Ramanathan P, Martin IC, Moran C, Taylor RM, Williamson P (2013). Identification of gene sets and pathways associated with lactation performance in mice. Physiol Genomics 45:171-181.
6. Wei J, Ramanathan P, Thomson PC, Martin IC, Moran C, Williamson P (2013). An integrative genomic analysis of the superior fecundity phenotype in QSi5 mice. Mol Biotechnol 53:217-226.
7. Rudolph MC, McManaman JL, Hunter L, Phang T, Neville MC (2003). Functional development of the mammary gland: use of expression profiling and trajectory clustering to reveal changes in gene expression during pregnancy, lactation, and involution. J Mammary Gland Biol Neoplasia 8:287-307.
8. Lemay DG, Neville MC, Rudolph MC, Pollard KS, German JB (2007). Gene regulatory networks in lactation: identification of global principles using bioinformatics. BMC Syst Biol 1:56.
9. Rudolph MC, McManaman JL, Phang T, Russell T, Kominsky DJ, Serkova NJ, et al (2007). Metabolic regulation in the lactating mammary gland: a lipid synthesizing machine. Physiol Genomics 28:323-336.
10. Neville MC, Anderson SM, McManaman JL, Badger TM, Bunik M, Contractor N, et al (2012). Lactation and neonatal nutrition: defining and refining the critical questions. J Mammary Gland Biol Neoplasia 17:167-88.