Urinary proteins detected using modern proteomics intervene in early type 2 diabetic kidney disease - a pilot study.
Alina Golea-SecaraCristian V A MunteanuMirela SarbuOctavian M CretuSilvia VelciovAdrian VladFlaviu BobFlorica GadaleanCristina GluhovschiOana MilasAnca SimulescuMaria Mogos-StefanMihaela PatruicaLigia PetricaAlina Diana ZamfirPublished in: Biomarkers in medicine (2020)
Aim: An advanced proteomics platform for protein biomarker discovery in diabetic chronic kidney disease (DKD) was developed, validated and implemented. Materials & methods: Three Type 2 diabetes mellitus patients and three control subjects were enrolled. Urinary peptides were extracted, samples were analyzed on a hybrid LTQ-Orbitrap Velos Pro instrument. Raw data were searched using the SEQUEST algorithm and integrated into Proteome Discoverer platform. Results & discussion: Unique peptide sequences, resulted sequence coverage, scoring of peptide spectrum matches were reported to albuminuria and databases. Five proteins that can be associated with early DKD were found: apolipoprotein AI, neutrophil gelatinase-associated lipocalin, cytidine deaminase, S100-A8 and hemoglobin subunit delta. Conclusion: Urinary proteome analysis could be used to evaluate mechanisms of pathogenesis of DKD.
Keyphrases
- end stage renal disease
- chronic kidney disease
- mass spectrometry
- high throughput
- peritoneal dialysis
- type diabetes
- newly diagnosed
- ejection fraction
- patient reported outcomes
- machine learning
- artificial intelligence
- wound healing
- electronic health record
- prognostic factors
- liquid chromatography
- metabolic syndrome
- healthcare
- deep learning
- binding protein
- anti inflammatory
- insulin resistance
- adipose tissue
- tandem mass spectrometry
- genetic diversity
- high resolution mass spectrometry
- ultra high performance liquid chromatography