Plasma proteome fingerprint in kidney diseases

IntroductionKidney diseases pose a serious healthcare problem because of their high prevalence, worsening of patients’ quality of life, and high mortality. Patients with kidney diseases are often asymptomatic until disease progression starts. Expensive renal replacement therapy options, such as dial...

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Main Authors: Kirill S. Nikolsky, Arthur T. Kopylov, Valeriya I. Nakhod, Natalia V. Potoldykova, Dmitry V. Enikeev, Tatiana V. Butkova, Liudmila I. Kulikova, Kristina A. Malsagova, Vladimir R. Rudnev, Denis V. Petrovskiy, Alexander A. Izotov, Anna L. Kaysheva
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Molecular Biosciences
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Online Access:https://www.frontiersin.org/articles/10.3389/fmolb.2024.1494779/full
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Summary:IntroductionKidney diseases pose a serious healthcare problem because of their high prevalence, worsening of patients’ quality of life, and high mortality. Patients with kidney diseases are often asymptomatic until disease progression starts. Expensive renal replacement therapy options, such as dialysis or kidney transplant, are required for end-stage kidney disease. Early diagnosis of kidney pathology is crucial for slowing down or curbing further damage. This study aimed to analyze the features of the protein composition of blood plasma in patients with the most common kidney pathologies: kidney calculus, kidney cyst, and kidney cancer.MethodsThe study involved 75 subjects. Proteins associated with kidney pathologies (CFB, SERPINA3, HPX, HRG, SERPING1, HBB, ORM2, and CP) were proposed. These proteins are important participants of complement and coagulation cascade activation and lipid metabolism.ResultsThe revealed phosphorylated proteoforms (CFB, C4A/C4B, F2, APOB, TTR, and NRAP) were identified. For them, modification sites were mapped on 3D protein models, and the potential role in formation of complexes with native partner proteins was assessed.DiscussionThe study demonstrates that the selected kidney pathologies have a similar proteomic profile, and patients can be classified into kidney pathology groups with an accuracy of (70–80)%.
ISSN:2296-889X