Protein C-Mannosylation and C-Mannosyl Tryptophan in Chemical Biology and Medicine

Molecules, Год журнала: 2021, Номер 26(17), С. 5258 - 5258

Опубликована: Авг. 30, 2021

C-Mannosylation is a post-translational modification of proteins in the endoplasmic reticulum. Monomeric α-mannose attached to specific Trp residues at first Trp-x-x-Trp/Cys (W-x-x-W/C) motif substrate proteins, by action C-mannosyltransferases, DPY19-related gene products. The acceptor are included thrombospondin type I repeat (TSR) superfamily, cytokine receptor family, and others. Previous studies demonstrated that C-mannosylation plays critical roles folding, sorting, and/or secretion proteins. A C-mannosylation-defective mutation was identified humans as disease-associated variant affecting W-x-x-W ADAMTSL1, which suggests involvement defects protein human diseases such developmental glaucoma, myopia, retinal defects. On other hand, monomeric C-mannosyl (C-Man-Trp), deduced degradation product C-mannosylated occurs cells extracellular fluids. Several showed level C-Man-Trp upregulated blood patients with renal dysfunction, suggesting metabolism may be involved kidney diseases. Together, considered play important biosynthesis functions altered regulation C-manosylation pathophysiology In this review, we consider biochemical biomedical knowledge C-Man-Trp, introduce recent concerning their significance biology medicine.

Язык: Английский

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Metabolite Biomarkers of CKD Progression in Children

Clinical Journal of the American Society of Nephrology, Год журнала: 2021, Номер 16(8), С. 1178 - 1189

Опубликована: Авг. 1, 2021

Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.We evaluated an untargeted metabolomics quantification stored plasma samples from 645 Chronic Kidney Disease in Children (CKiD) participants. Metabolites were standardized logarithmically transformed. Cox proportional hazards regression examined association between 825 nondrug metabolites progression to composite outcome KRT or 50% reduction eGFR, adjusting age, sex, race, body mass index, hypertension, glomerular versus nonglomerular diagnosis, proteinuria, baseline eGFR. Stratified analyses performed within subgroups glomerular/nonglomerular diagnosis eGFR.Baseline characteristics 391 (61%) male; median age 12 years; eGFR 54 ml/min per 1.73 m2; 448 (69%) diagnosis. Over a follow-up 4.8 years, 209 (32%) participants developed outcome. Unique signals identified Among with ≥60 m2, two-fold higher levels seven significantly associated KRT/halving events: three involved purine pyrimidine metabolism (N6-carbamoylthreonyladenosine, hazard ratio, 16; 95% confidence interval, 4 60; 5,6-dihydrouridine, 17; 5 55; pseudouridine, 39; 8 200); two amino acids, C-glycosyltryptophan, 24; interval 6 95 lanthionine, 3; 2 5; tricarboxylic acid cycle intermediate 2-methylcitrate/homocitrate, 4; 7; gulonate, 10; 3 29. those <60 level tetrahydrocortisol sulfate was lower risk (hazard 0.8; 0.7 0.9).Untargeted metabolomic profiling facilitated novel metabolite associations children that independent established clinical predictors highlight role select biologic pathways.

Язык: Английский

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Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

npj Digital Medicine, Год журнала: 2022, Номер 5(1)

Опубликована: Ноя. 2, 2022

Diabetic kidney disease is the leading cause of end-stage worldwide; however, integration high-dimensional trans-omics data to predict this diabetic complication rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training testing cohorts 557 61 subjects, respectively. Three developed. In model 1, top 20 features selected by AI give an accuracy rate 0.83 area under curve (AUC) 0.89 when differentiating DM non-DM individuals. 2, among patients, 10 AI-selected gives 0.70 AUC 0.76 identifying subjects at renal impairment. 3, 25 0.82 pinpointing chronic disease. addition, performance three rigorously verified independent validation cohort. Intriguingly, analysis protein-protein interaction network genes containing identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP FAM53A) reveals that, molecular level, there seems be interconnected factors have effect progression impairment patients. conclusion, our findings reveal potential employing augment traditional methods suggest what mechanisms may underlie complex between Moreover, development AI-assisted will improve precision diagnosing in predisposed both non-DM. Finally, large prospective cohort study needed validate utility mechanistic implications these signatures.

Язык: Английский

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Metabolomic profiles to explore biomarkers of severe sarcopenia in older men: A pilot study

Experimental Gerontology, Год журнала: 2022, Номер 167, С. 111924 - 111924

Опубликована: Авг. 10, 2022

Язык: Английский

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Decoding the ‘Fifth’ Nucleotide: Impact of RNA Pseudouridylation on Gene Expression and Human Disease

Molecular Biotechnology, Год журнала: 2023, Номер 66(7), С. 1581 - 1598

Опубликована: Июнь 21, 2023

Язык: Английский

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