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PXD041884-1

PXD041884 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleQuantifying Protein Acetylation in Diabetic Nephropathy from FFPE Explants
DescriptionBackground: Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus and a leading cause of chronic kidney disease and end stage renal disease. One potential mechanism underlying cellular dysfunction contributing to kidney disease is aberrant post-translational modifications, such as lysine acetylation. Lysine acetylation is associated with cellular metabolic flux and is thought to be altered in patients with diabetes and dysfunctional renal metabolism. Methods: A novel technique was adapted to quantify sites of N-acetylation from formalin-fixed paraffin-embedded kidney explant tissue from patients with diabetic kidney disease and non-diseased donors (n=5 and n=7, respectively). Multiple methodologies were integrated to extract viable proteins from formalin-fixed paraffin-embedded kidney explant tissue followed by enrichment and isolation. Proteomic and acetylomic profiles were then quantified via LC-MS/MS analysis. Results: Quantitative proteomic analysis of FFPE tissues identified 840 total proteins with 260 of those significantly changing and our acetylomic analysis quantified 290 acetylated peptides with 98 of those significantly changing in human diabetic kidneys. Lysine acetylation is known to regulate protein function, providing a mechanism by which proteins respond to cellular metabolic status. Protein pathways found to be impacted in DKD patients revealed an association with numerous metabolic pathways, specifically mitochondrial function, EIF2 signaling, oxidative phosphorylation, lipid metabolism, sirtuin signaling, and LXR/RXR activation, each of which are intimated to play a significant role in the pathogenesis of DKD. Differential protein acetylation in DKD patients reflected biochemical processes associated with sirtuin signaling, valine, leucine, and isoleucine degradation, lactate metabolism, oxidative phosphorylation, and ketogenesis. Conclusions: Here, we establish a quantitative acetylomics platform for protein biomarker discovery in formalin-fixed and paraffin-embedded biopsies of kidney transplant patients suffering from diabetic kidney disease. Our analysis provides a novel platform for quantifying enriched post-translational modifications in preserved explant tissues and includes optimizations for addressing protein crosslinking, paraffin removal, and formalin-derived formaldehyde protein adducts combined with immunoprecipitation of acetyl-peptides and total protein quantitation.
HostingRepositoryPRIDE
AnnounceDate2025-11-10
AnnouncementXMLSubmission_2025-11-09_16:17:26.251.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterCole Michel
SpeciesList scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606;
ModificationListacetylated residue; monohydroxylated residue; deamidated residue; iodoacetamide derivatized residue
Instrument6550 iFunnel Q-TOF LC/MS
Dataset History
RevisionDatetimeStatusChangeLog Entry
02023-04-28 17:38:22ID requested
12025-11-09 16:17:27announced
Publication List
Schwab SK, Harris PS, Michel C, McGinnis CD, Nahomi RB, Assiri MA, Reisdorph R, Henriksen K, Orlicky DJ, Levi M, Rosenberg A, Nagaraj RH, Fritz KS, Quantifying Protein Acetylation in Diabetic Nephropathy from Formalin-Fixed Paraffin-Embedded Tissue. Proteomics Clin Appl, 18(6):e202400018(2024) [pubmed]
10.1002/prca.202400018;
Keyword List
ProteomeXchange project tag: Biology/Disease-Driven Human Proteome Project (B/D-HPP), Diabetes (B/D-HPP), Human Proteome Project
submitter keyword: proteomics, formalin-fixed paraffin-embedded tissue, mass spectrometry, kidney disease,Acetylation, diabetes
Contact List
Kristofer Fritz
contact affiliationUniversity of Colorado - Skaggs School of Pharmacy
contact emailkristofer.fritz@cuanschutz.edu
lab head
Cole Michel
contact affiliationUniveristy of Colorado - Skaggs School of Pharmacy
contact emailcole.michel@cuanschutz.edu
dataset submitter
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