Abstract

The question how genetic variability is translated into phenotypes is fundamental in biology and medicine. Powerful genomic technologies now determine genetic variability at a genomic level and at unprecedented speed, accuracy and (low) cost. To date the effects of genomic variability on the expressed information of the cell has been mainly studied by transcript profiling.

In this presentation we will discuss emerging computational and quantitative proteomic technologies to relate genotypic variation to the proteome. Proteomic data to support such correlations need to be quantitatively accurate, highly reproducible across multiple measurements and samples and generated at high throughput. Ideally, the data also would provide information about spatial arrangement of proteins in the cell. Data with these qualities can now be generated by the targeted proteomic methods selected reaction monitoring (SRM) and, at higher throughput, by SWATH -MS (1).

We will discuss the principles of these mass spectrometric methods, discuss the computational challenged they pose for data analysis and demonstrate with selected applications, using genetic reference strain compendia, their ability to determine the effect of genetic variability on the quantitative proteome, thus functionally connecting the genome to the proteome (2,3).

1. Gillet LC, Navarro P, Tate S, Roest H, Selevsek N, Reiter L, Bonner R, Aebersold R. (2012) Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis (2012). Mol Cell Proteomics 11:O111.016717.

2. Picotti P, Clément-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Röst H, Sun Z, Rinner O, Reiter L, Shen Q, Michaelson JJ, Frei A, Alberti S, Kusebauch U, Wollscheid B, Moritz RL, Beyer A, Aebersold R. (2013) A complete mass-spectrometric map of the yeast proteome applied to quantitative tranalysis. Nature. 494(7436):266-70.

3. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population (2014). Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R. Cell. 158(6):1415-30. doi: 10.1016/j.cell.2014.07.039.