Shared and Unique Disease Pathways in Amyotrophic Lateral Sclerosis and Parkinson's Disease Unveiled in Peripheral Blood Mononuclear Cells
Recent evidence supports an association between amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD). Indeed, prospective population-based studies demonstrated that about one-third of ALS patients develop parkinsonian (PK) signs, even though different neuronal circuitries are involved. In this context, proteomics represents a valuable tool to identify unique and shared pathological pathways. Here, we used two-dimensional electrophoresis to obtain the proteomic profile of peripheral blood mononuclear cells (PBMCs) from PD and ALS patients including a small cohort of ALS patients with parkinsonian signs (ALS-PK). After the removal of protein spots correlating with confounding factors, we applied a sparse partial least square discriminant analysis followed by recursive feature elimination to obtain two protein classifiers able to discriminate (i) PD and ALS patients (30 spots) and (ii) ALS-PK patients among all ALS subjects (20 spots). Functionally, the glycolysis pathway was significantly overrepresented in the first signature, while extracellular interactions and intracellular signaling were enriched in the second signature. These results represent molecular evidence at the periphery for the classification of ALS-PK as ALS patients that manifest parkinsonian signs, rather than comorbid patients suffering from both ALS and PD. Moreover, we confirmed that low levels of fibrinogen in PBMCs is a characteristic feature of PD, also when compared with another movement disorder. Collectively, we provide evidence that peripheral protein signatures are a tool to differentially investigate neurodegenerative diseases and highlight altered biochemical pathways.Keywords: Parkinson’s disease; amyotrophic lateral sclerosis; fibrinogen; peripheral blood mononuclear cells; sPLS-DA; two-dimensional electrophoresis.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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