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Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

ABSTRACT: Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

SUBMITTER: Cai X

PROVIDER: S-EPMC10518601 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

Cai Xue X Xue Zhangzhi Z Zeng Fang-Fang FF Tang Jun J Yue Liang L Wang Bo B Ge Weigang W Xie Yuting Y Miao Zelei Z Gou Wanglong W Fu Yuanqing Y Li Sainan S Gao Jinlong J Shuai Menglei M Zhang Ke K Xu Fengzhe F Tian Yunyi Y Xiang Nan N Zhou Yan Y Shan Peng-Fei PF Zhu Yi Y Chen Yu-Ming YM Zheng Ju-Sheng JS Guo Tiannan T

Cell reports. Medicine 20230830 9

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developin ...[more]

PMID: 37652016

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Project description:Leishmania species are responsible for a broad spectrum of diseases, denominated Leishmaniasis, affecting over 12 million people worldwide. During the last decade, there have been impressive efforts for sequencing the genome of most of the pathogenic Leishmania spp. as well as hundreds of strains, but large-scale proteomics analyses did not follow these achievements and the Leishmania proteome remained mostly uncharacterized. Here, we report a comprehensive comparative study of the proteomes of strains representing L. braziliensis, L. panamensis and L. guyanensis species. Proteins extracted by SDS-mediated lysis were processed following the multi-enzyme digestion-filter aided sample preparation (FASP) procedure and analysed by high accuracy mass spectrometry. "Total Protein Approach" and "Proteomic Ruler" were applied for absolute quantification of proteins. Principal component analysis demonstrated very high reproducibility among biological replicates and a very clear differentiation of the three species. Our dataset comprises near 7000 proteins, representing the most complete Leishmania proteome yet known, and provides a comprehensive quantitative picture of the proteomes of the three species in terms of protein concentration and copy numbers. Analysis of the abundance of proteins from the major energy metabolic processes allow us to highlight remarkably differences among the species and suggest that these parasites depend on distinct energy substrates to obtain ATP. Whereas L. braziliensis relies the more on glycolysis, L. panamensis and L. guyanensis seem to depend mainly on mitochondrial respiration. These results were confirmed by biochemical assays showing opposite profiles for glucose uptake and O2 consumption in these species. In addition, we provide quantitative data about different membrane proteins, transporters, and lipids, all of which contribute for significant species-specific differences and provide rich substrate for explore new molecules for diagnosing purposes. Data are available via ProteomeXchange with identifier PXD017696.

Dataset Information

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

Publications

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

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