Identification of a Non-Invasive Urinary Exosomal Biomarker for Diabetic Nephropathy Using Data-Independent Acquisition Proteomics
Abstract
:1. Introduction
2. Results
2.1. Characterization of the Exosomes
2.2. Proteomic Analysis
2.3. Bioinformatic Analysis of Differentially Expressed Proteins
2.4. Screening for Potential Biomarkers
2.5. Validation of Biomarker
2.6. Clinical Correlation
3. Discussion
Study Limitations
4. Materials and Methods
4.1. Description of the Cohort
4.2. Urine Exosomes Isolation
4.3. Characterization of the Exosomes
4.4. Proteomic Analyses
4.5. Bioinformatics Analyses and Statistical Rationale
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACN | Acetonitrile |
ADA | American Diabetes Association |
AMBP | Alpha-1-microglobulin/bikunin precursor |
AUC | Area under the curve |
DIA | Data-independent acquisition |
DN | Diabetic nephropathy |
DPP-4 | Dipeptidyl peptidase-4 |
eGFR | Estimated glomerular filtration rate |
ESRD | End-stage renal disease |
FA | Formic acid |
FDR | False discovery rate |
FSGS | Focal segmental glomerulosclerosis |
GO | Gene ontology |
HC | Healthy control |
IAM | Iodoacetamide |
IgAN | IgA nephropathy |
IL-6 | Interleukin-6 |
ISEV | International Society for Extracellular Vesicles |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
LASSO | Least absolute shrinkage and selection operator |
MN | Membranous nephropathy |
MS | Mass spectrometry |
NDRD | Non-diabetic renal disease |
NTA | Nanoparticle tracking analysis |
PBS | Phosphate-buffered saline |
PHYHD1 | Phytanoyl-CoA dioxygenase domain containing 1 |
PLA | People’s Liberation Army |
PPI | Protein–protein interaction networks |
ROC | Receiver operating characteristic curve |
T1DM | Type 1 diabetes mellitus |
T2DM | Type 2 diabetes mellitus |
TEM | Transmission electron microscope |
TGF-β | Transforming growth factor-β |
UACR | Urinary albumin-to-creatinine ratio |
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DN (n = 12) | NDRD (n = 15) | T2DM (n = 9) | HC (n = 12) | |
---|---|---|---|---|
Age (years) | 49.08 ± 3.21 | 51.29 ± 2.50 | 60.11 ± 3.09 a | 47.83 ± 1.90 |
Sex (male/female) | (6/6) | (12/3) | (4/5) | (8/4) |
Serum albumin (g/L) | 32.24 ± 2.75 ab | 31.16 ± 3.82 a | 42.43 ± 1.04 | 44.35 ± 1.06 |
HbA1c (%) | 7.54 ± 0.53 a | 6.3 ± 0.27 b | 9.56 ± 0.62 a | 5.44 ± 0.069 |
Plasma glucose (mmol/L) | 8.20 ± 1.63 a | 5.42 ± 0.29 | 10.33 ± 1.57 a | 5.07 ± 0.14 |
Urinary albumin (g/24 h) | 2.42 ± 0.59 b | 1.77 ± 0.50 b | 0.04 ± 0.03 | |
Serum creatinine (μmol/L) | 169.10 ± 16.73 ab | 107.53 ± 18.32 ab | 69.01 ± 7.01 | 64.77 ± 3.64 |
BUN (mmol/L) | 10.08 ± 1.47 ab | 6.15 ± 0.66 a | 5.18 ± 0.58 | 4.38 ± 0.17 |
eGFR (mL min−1 1.73 m−2) | 32.23 ± 3.46 b | 68.41 ± 14.44 | 120.34 ± 10.96 |
Number of Differential Proteins | Upregulated Proteins | Downregulated Proteins | |
---|---|---|---|
HC vs. DN | 1263 | 497 | 766 |
HC vs. NDRD | 1177 | 508 | 669 |
HC vs. T2DM | 632 | 462 | 170 |
T2DM vs. DN | 1200 | 337 | 863 |
T2DM vs. NDRD | 1260 | 347 | 913 |
NDRD vs. DN | 557 | 156 | 401 |
Protein Names | Protein Descriptions | DN vs. NDRD | ||
---|---|---|---|---|
Genes | AVG Log2 Ratio | Q-Value | ||
PEPC | Gastricsin | PGC | 5.45 | 0.01 |
PLCL1 | Inactive phospholipase C-like protein 1 | PLCL1 | 5.20 | 0.01 |
PIP | Prolactin-inducible protein | PIP | 4.84 | <0.01 |
SEMG2 | Semenogelin-2 | SEMG2 | 4.15 | 0.01 |
SEMG1 | Semenogelin-1 | SEMG1 | 3.39 | <0.01 |
GTR14 | Solute carrier family 2, facilitated glucose transporter member 14 | SLC2A14 | 3.32 | 0.01 |
PLA1A | Phospholipase A1 member A | PLA1A | 3.13 | 0.03 |
ILDR1 | Immunoglobulin-like domain-containing receptor 1 | ILDR1 | 2.65 | 0.03 |
CRCT1 | Cysteine-rich C-terminal protein 1 | CRCT1 | 2.35 | <0.01 |
SIDT1 | SID1 transmembrane family member 1 | SIDT1 | 2.29 | <0.01 |
PERM | Myeloperoxidase | MPO | −3.50 | 0.02 |
FIG4 | Polyphosphoinositide phosphatase | FIG4 | −3.38 | <0.01 |
M4K4 | Mitogen-activated protein kinase kinase 4 | MAP4K4 | −3.34 | 0.03 |
TETN | Tetranectin | CLEC3B | −3.26 | 0.02 |
LV746 | Immunoglobulin lambda variable 7-46 | IGLV7-46 | −3.22 | <0.01 |
PKP3 | Plakophilin-3 | PKP3 | −3.13 | 0.02 |
GNTK | Probable glucokinase | IDNK | −3.09 | 0.04 |
H3PS2 | Histone HIST2H3PS2 | H3-2 | −3.06 | 0.04 |
PERE | Eosinophil peroxidase | EPX | −3.04 | 0.02 |
SP100 | Nuclear autoantigen Sp-100 | SP100 | −3.03 | 0.01 |
Variable | DN (n = 8) | NDRD (n = 5) | T2DM (n = 7) | HC (n = 9) |
---|---|---|---|---|
Age (years) | 57.50 ± 7.71 | 58.00 ± 5.00 | 54.29 ± 11.64 | 47.67 ± 6.00 |
Sex (male/female) | (5/3) | (3/2) | (4/3) | (5/4) |
Serum albumin (g/L) | 34.20 ± 7.47 a | 34.92 ± 12.43 | 42.49 ± 4.20 | 45.03 ± 2.55 |
HbA1c (%) | 7.39 ± 1.60 a | 6.62 ± 0.99 | 9.57 ± 2.32 a | 5.43 ± 0.24 |
Plasma glucose (mmol/L) | 6.99 ± 2.21 | 6.50 ± 2.88 | 11.21 ± 5.24 a | 5.09 ± 0.36 |
Urinary albumin (g/24 h) | 2.71 ± 1.98 ab | 2.83 ± 4.11 a | 0.02 ± 0.05 | |
Serum creatinine (μmol/L) | 150.44 ± 53.27 ab | 114.40 ± 35.06 | 71.94 ± 23.20 | 65.89 ± 10.67 |
BUN (mmol/L) | 9.51 ± 2.53 ab | 7.97 ± 1.89 | 4.90 ± 1.80 | 4.67 ± 0.96 |
eGFR (mL min−1 1.73 m−2) | 39.85 ± 17.76 b | 50.48 ± 17.91 | 97.55 ± 25.17 |
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Ding, X.; Zhang, D.; Ren, Q.; Hu, Y.; Wang, J.; Hao, J.; Wang, H.; Zhao, X.; Wang, X.; Song, C.; et al. Identification of a Non-Invasive Urinary Exosomal Biomarker for Diabetic Nephropathy Using Data-Independent Acquisition Proteomics. Int. J. Mol. Sci. 2023, 24, 13560. https://doi.org/10.3390/ijms241713560
Ding X, Zhang D, Ren Q, Hu Y, Wang J, Hao J, Wang H, Zhao X, Wang X, Song C, et al. Identification of a Non-Invasive Urinary Exosomal Biomarker for Diabetic Nephropathy Using Data-Independent Acquisition Proteomics. International Journal of Molecular Sciences. 2023; 24(17):13560. https://doi.org/10.3390/ijms241713560
Chicago/Turabian StyleDing, Xiaonan, Dong Zhang, Qinqin Ren, Yilan Hu, Jifeng Wang, Jing Hao, Haoran Wang, Xiaolin Zhao, Xiaochen Wang, Chenwen Song, and et al. 2023. "Identification of a Non-Invasive Urinary Exosomal Biomarker for Diabetic Nephropathy Using Data-Independent Acquisition Proteomics" International Journal of Molecular Sciences 24, no. 17: 13560. https://doi.org/10.3390/ijms241713560