Protein database and quantitative analysis considerations when integrating genetics and proteomics to compare mouse...

Area of Science:

• Genomics and Proteomics
• Comparative Mammalian Genetics

Background:

• Genetics research has identified genomic regions linked to quantitative traits in mouse strains.
• Microarrays are commonly used to find differentially expressed genes but are susceptible to false positives due to genetic variants affecting probe hybridization.

Purpose of the Study:

• To compare the robustness of proteomics versus microarrays in identifying differentially expressed proteins between mouse strains.
• To develop and apply a proteomics strategy for analyzing strain-specific differences in brain striata.

Main Methods:

• Utilized spectral counting for quantitative proteomics in mouse brain striata.
• Developed strain-specific protein databases incorporating all putative genetic isoforms.
• Employed quantile normalization, ComBat for batch effect adjustment, and edgeR for differential expression analysis.

Main Results:

• Proteomics proved more robust than microarrays to sequence differences, though some proteins were significantly affected.
• Increased proteome coverage yielded a 6.8% gain in peptide assignments.
• Identified 101 differentially expressed proteins/groups, with 84 located within known genomic regions of interest.

Conclusions:

• Proteomics, particularly with comprehensive strain-specific databases, is a more reliable method for identifying differentially expressed proteins between mouse strains compared to microarrays.
• The developed proteomics strategy successfully identified candidate proteins associated with quantitative trait loci.
• This study highlights the utility of advanced proteomics techniques in genetic research.