Informatics-assisted protein profiling in a transgenic mouse model of amyotrophic lateral sclerosis.

One of the causes of amyotrophic lateral sclerosis (ALS) is due to mutations in Cu,Zn-superoxide dismutase (SOD1). The mutant protein exhibits a toxic gain of function that adversely affects the function of neurons in the spinal cord, brain stem, and motor cortex. A proteomic analysis of protein expression in a widely used mouse model of ALS was undertaken to identify differences in protein expression in the spinal cords of mice expressing a mutant protein with the G93A mutation found in human ALS. Protein profiling was done on soluble and particulate fractions of spinal cord extracts using high throughput two-dimensional liquid chromatography coupled to tandem mass spectrometry. An integrated proteomics-informatics platform was used to identify relevant differences in protein expression based upon the abundance of peptides identified by database searching of mass spectrometry data. Changes in the expression of proteins associated with mitochondria were particularly prevalent in spinal cord proteins from both mutant G93A-SOD1 and wild-type SOD1 transgenic mice. G93A-SOD1 mouse spinal cord also exhibited differences in proteins associated with metabolism, protein kinase regulation, antioxidant activity, and lysosomes. Using gene ontology analysis, we found an overlap of changes in mRNA expression in presymptomatic mice (from microarray analysis) in three different gene categories. These included selected protein kinase signaling systems, ATP-driven ion transport, and neurotransmission. Therefore, alterations in selected cellular processes are detectable before symptomatic onset in ALS mouse models. However, in late stage disease, mRNA expression analysis did not reveal significant changes in mitochondrial gene expression but did reveal concordant changes in lipid metabolism, lysosomes, and the regulation of neurotransmission. Thus, concordance of proteomic and mRNA expression data within multiple categories validates the use of gene ontology analysis to compare different types of "omic" data.