Mass spectrometry and proteomics are continuously evolving methods in systems biology carrying the enormous potential for accurately profiling the proteome of diseased tissues and exploring biomarker molecules. In Huntington"s disease (HD), identifying novel effective biomarkers for monitoring the disease progression from the early asymptomatic stage would be extremely useful for clinical decision-making. Animal models allow us to delve into the molecular profile of asymptomatic HD stages that would not be possible by studies of post-mortem human brains. Several proteomic studies in HD animal models focusing on the elucidation of huntingtin (HTT) and mutant HTT (patho)physiological roles, rather than on biomarker discovery are impeded by heterogeneity of proteomic methods and thus inconclusive overlap of their findings. Integrating the datasets from multiple studies can provide valuable information that a single analysis does not offer. In this study, we performed a meta-analysis of publicly available in-depth proteomic datasets from HD's transgenic and knock-in animal models. The primary objective of our meta-analysis was to identify the degree of overlap and correlation between the different animal models of HD based on the protein identifications and expression data. Our secondary objective was to compare these outcomes with the available clinical proteomic data from the brain and cerebrospinal fluid. We identified four datasets in transgenic and knock-in HD mice using label-free proteomic methods to quantify protein expression. The raw liquid chromatography-mass spectrometry (LC/MS) data were retrieved from the PRIDE repository and re-analysed using Max Quant and the most recent reference proteomes. The datasets from these parallel studies are then combined and meta-analysed using the MetaOmics pipeline that allows the data preprocessing, integration of differential expression data and pathway and network analyses. Two available datasets in human HD patients are correlated with these data. The results of this meta-analysis will be presented and discussed in relevance to the overlap with the original findings and preliminary data from our recently published systematic review summarising the protein identifications from HD patients and experimental models, reporting biomarker candidates verified by independent methods or independent studies. This is the first study attempting to explore the publicly available LS/MS datasets in the context of HD and its conclusions might be beneficial for discovering interesting biomarkers.
This work was supported by the Czech Science Foundation project 22-24983S.