| Objeetive Metabolic Syndrome(Met S) was a cluster of clinical symptoms including abdominal obesity, hypertension, hyperglycemia, dyslipidemia, however its pathogenesis was unclear. Proteomics technology based on the "black box theory" had advantages in exploring the Met S, which had complex etiology and with multitudinous pathogenic factors. Another advantage of proteomics was its great power in finding specific biomarker candidates for Met S. Protein markers helped to further explain the pathogenesis of Met S during its processes of the occurrence and development. Protein markers were great significance for the early diagnosis of Met S and its endpoint events prevention. This study profiling the serum protein of Met S group and age, sex matched control group. And Met S differentially expressed proteins spectrum was established. Pathway enrichment analysis and protein interaction network analysis were performed to screen important function proteins as biomarker candidates, which played key role in the processes of Met S occurrence and development.Methods 1. 98 Met S patients and 98 age, sex matched controls were recruited. Proteins expressed levels were measured using serum proteomics, which based on two-dimensional liquid chromatography-tandem mass spectrometry. Met S serum differentially expressed proteins spectrum was established. 2. With Met S serum differentially expressed proteins functional annotation, biological process, cellular component, molecular function enrichment analysis were performed based on Gene Ontology. The distributions of differentially expressed proteins were acquired. 3. Using Analyze Network Algorithm to make proteins interaction network analysis. And pathway enrichment analysis was performed in differentially expressed proteins. Screen potential biomarker with Biomarkers Assessment tools of Meta CoreTM software, and building candidate proteins interaction network.Results1. The general clinical data showed Met S case group(N = 98) and a matched control group(N = 98) had no significant difference in smoking and drinking habits. Biology repeated experiment results showed biological samples repetition rate and instrument testing repetition rate approached to 70%, experiment result is stable and reliable with good repeatability. 89 proteins were differentially expressed between groups, including 71 proteins with increased levels and 18 preoteins with decreased levels. 2. Met S serum differentially expressed proteins enrichment analysis results showed the distributions of biological process were ectoderm and epidermis development(23%), intermediate filament cytoskeleton organization(21%), lipid metabolic process(35%), regulation of protein complex assembly(8%), regulation of lipoprotein particle clearance(7%). Cellular component distributions were intermediate filament(28%), intermediate filament cytoskeleton(32%), nuclear periphery, nuclear matrix and nuclear lumen(21%), intermediate-density lipoprotein particle, spherical high-density lipoprotein particle, chylomicron, very-low-density lipoprotein particle and triglyceride-rich lipoprotein particle(12%). Molecular function distributions were structural molecule activity(23%), structural constituent of cytoskeleton(12%), protein binding(3%), structural constituent of epidermis(2%), lipase inhibitor activity(2%), cholesterol binding(2%), sterol binding(2%). 3. Met S serum differentially expressed proteins interaction network analysis results showed 89 proteins were interacted in 7 network mode. Pathway enrichment analysis showed the distributions of differentially expressed proteins were Cytoskeleton remodeling, Cholesterol and Sphingolipid transport / Recycling to plasma membrane in liver, regulation of Bile acids, glucose and lipid metabolism, G-protein signaling pathways. Using Biomarkers Assessment tools of Meta CoreTM software, 9 proteins were related to Metabolic Diseases and 3 proteins were related to Met S. LMNA,APOC3 and CAV1 passed all screening test, and could be the potential serum protein biomarkers in metabolic syndrome.Conclusion Our study found 89 proteins were differentially expressed in Met S, including 71 proteins with increased levels and 18 preoteins with decreased levels. Compaired onproteomics research in other metabolic system disease, we found more differentially expressed proteins, which intuitively reflected the complexity of pathogenesis in Met S. 16 kinds of keratins or cytokeratins were increased levels in Met S group. The results showed Met S patients are already in a state of chronic inflammatory hepatic steatosis. And this result also revealed Met S was a powerful predictor. LMNA, APOC3, CAV1 were seleted in screen analysis, could be biomarker candidates of Met S. |
