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Label-Free LC-MS/MS Proteomic Analysis of Urinary Identification in Diabetic Vascular Dementia

ABSTRACT: Objective: The aim of this study was to identify novel diagnostic biomarkers of diabetic vascular dementia (DVD) and unraveling the underlying mechanisms using mass spectrometry (MS). Methods: Label-Free LC-MS/MS proteomics analysis was applied to urine samples from four groups including 14 vascular dementia CC(VD), 22 type 2 diabetes mellitus (T2DM), 12 DVD, 21 Normal Control (NC). Searching the MS data by Proteome Discoverer software, protein abundances were analyzed qualitatively and quantitatively and compared between these groups. Combining bioinformatics analysis using GO, pathway crosstalk analysis using KEGG, PPI network analysis using STRING and literature searching, the differentially expressed proteins (DEPs) of DVD can be comprehensively judged and were further quantified by receiver operating characteristic (ROC) curve methods. Results: The proteomic findings showed quantitative changes in DVD was compared to NC, T2DM, and VD groups; among 7,527 identified urine proteins, 1222, 1152, and 1180 of the proteins displayed quantitative changes unique to DVD vs NC, T2DM, and VD, respectively, including 481 overlapped common DEPs. Then 9 unique proteins and 2 composite markers (CM) were confirmed by a ROC curve method. Conclusion: This study provided a novel insight into the potential pathogenesis of DVD and elucidated a method for the earlier detection.

INSTRUMENT(S): Q Exactive HF, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Urine

SUBMITTER: Yi Zeng

LAB HEAD: Yi Zeng

PROVIDER: PXD022189 | Pride | 2021-01-14

REPOSITORIES: Pride

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Project description:Type 2 diabetes mellitus (T2DM) is a complex metabolic disease with multiple etiologies, involving both genetic and environmental factors. With changes associated with modern life, increasing attention has been paid to chronic psychological stressors such as work stress. Chronic psychological stress can induce or aggravate diabetes mellitus, and conversely, with the deterioration of T2DM, patients often experience different degrees of depression, anxiety, and other negative emotions. To elucidate the role of ZiBuPiYin recipe (ZBPYR) in regulating the liver mitochondria-associated endoplasmic reticulum membrane proteome to improve T2DM with chronic psychological stress, differentially expressed proteins (DEPs) were identified among Zucker lean littermates (control group), chronic psychological stress T2DM rats (model group), and ZBPYR administration rats (ZBPYR group) via iTRAQ with LC-MS/MS. Using Mfuzz soft clustering analysis, DEPs were divided into six different clusters. Clusters 1-6 contained 5, 68, 44, 57, 28, and 32 DEPs, respectively. Given that ZBPYR can alleviate T2DM symptoms and affect exploratory behavior during T2DM with chronic psychological stress, we focused on the clusters with opposite expression trends between model:control and ZBPYR:model groups. We screened out the DEPs in clusters 1, 3, and 4, which may be good candidates for the prevention and treatment of T2DM with chronic psychological stress, and further conducted bioinformatics analyses. DEPs were mainly involved in the insulin signaling pathway, oxidative phosphorylation, tricarboxylic acid cycle, amino acid metabolism, lysosome-related processes, and lipid metabolism. This may indicate the pathogenic basis of T2DM with chronic psychological stress and the potential therapeutic mechanism of ZBPYR. In addition, two key proteins, a lysosome-associated protein (Lamp2) and tricarboxylic acid cycle-related protein (Suclg1), may represent novel biomarkers for T2DM with chronic psychological stress and drug targets of ZBPYR.

Project description:Protein O-glycosylation has long been recognized to be closely associated with many diseases, particularly with tumor proliferation, invasion and metastasis. The ability to efﬁciently proﬁle the variation of O-glycosylation in large-scale clinical samples provides an important approach for the development of biomarkers for cancer diagnosis and for therapeutic response evaluation. Therefore, mass spectrometry (MS)-based techniques for high throughput, in-depth and reliable elucidation of protein O-glycosylation in large clinical cohorts are in high demand. However, the wide existence of serine and threonine residues in the proteome and the tens of mammalian O-glycan types lead to extremely large searching space composed of millions of theoretical combinations of peptides and O-glycans for intact O-glycopeptide database searching. As a result, exceptionally long time is required for database searching which is a major obstacle in O-glycoproteome studies of large clinical cohorts. More importantly, due to the low abundance and poor ionization of intact O-glycopeptides and the stochastic nature of data-dependent MS2 acquisition, substantially elevated missing data levels are inevitable as the sample number increases, which undermines the quantitative comparison across samples. Therefore, we report a new MS data processing strategy that integrates glycoform-specific database searching, reference library-based MS1 feature matching and MS2 identification propagation for fast identification, in-depth and reproducible label-free quantification of O-glycosylation of human urinary proteins. This strategy increases the database searching speeds by up to 20-fold and leads to a 30-40% enhanced intact O-glycopeptide quantification in individual samples with an obviously improved reproducibility. In total, we obtained quantitative information for 1068 intact O-glycopeptides across 36 healthy human urine samples with a 30-40% reduction in the amount of missing data. This is currently the largest dataset of urinary O-glycoproteome and demonstrates the application potential of this new strategy in large-scale clinical investigations.

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Label-Free LC-MS/MS Proteomic Analysis of Urinary Identification in Diabetic Vascular Dementia

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