This vignette from the R package JMDplots version 1.2.19-9 shows chemical metrics for proteins that are differentially expressed in high-glucose compared to control conditions. The analysis is described in more detail in a paper (Dick, 2021). Abbreviations:
Differences are calculated as (median value for up-regulated proteins) - (median value for down-regulated proteins). Dashed lines enclose the 50% confidence region for highest probability density.
In the table, values of ΔZC and ΔnH2O are multiplied by 1000, values of ΔMW are multiplied by 100, and negative values are shown in bold. Abbreviations:
| set | reference (description) | ndown | nup | ΔZC | ΔnH2O | ΔnAA | ΔMW |
|---|---|---|---|---|---|---|---|
| a |
PW08 (Saccharomyces cerevisiae in very high glucose (300 g/L) vs control (20 g/L) for 2 h)
|
38 | 44 | -4 | -45 | -8 | 188 |
| b |
PW08 (Saccharomyces cerevisiae in very high glucose (300 g/L) vs control (20 g/L) for 10 h)
|
33 | 62 | -2 | -37 | 104 | 9 |
| c |
PW08 (Saccharomyces cerevisiae in very high glucose (300 g/L) vs control (20 g/L) for 12 h)
|
18 | 65 | 8 | -29 | 76 | 69 |
| d |
WCM+09 (mouse pancreatic islets in 16.7 mM vs 5.6 mM glucose)
|
63 | 94 | -13 | -43 | 36 | -15 |
| e |
WFSL09 (bovine aortal endothelial cells in 22 mM vs 5 mM glucose)
|
67 | 46 | -26 | 9 | 26 | 306 |
| f |
MFD+10 (rat INS-1E cells in 25 mM vs 11 mM glucose)
|
51 | 20 | -34 | 13 | 12 | -74 |
| g |
CCC+12 (retinal pigmented epithelium in 25 mM glucose vs 5.5 mM glucose)
|
17 | 11 | -16 | -38 | -39 | -165 |
| h |
CCC+12 (retinal pigmented epithelium in 100 mM glucose vs 5.5 mM glucose)
|
21 | 24 | -4 | -18 | -16 | -126 |
| i |
SFG+12 (human pancreatic islets in 15 mM vs 5 mM glucose)
|
34 | 57 | 5 | 11 | 116 | -7 |
| j |
CCCC13 (Chang liver cells in 25 mM vs 5.5 mM glucose)
|
32 | 39 | 2 | 0 | -270 | -73 |
| k |
CCCC13 (Chang liver cells in 100 mM vs 5.5 mM glucose)
|
19 | 50 | 32 | -38 | 98 | -51 |
| l |
CCW+13 (rat INS-1beta cells in 27 mM vs 11 mM glucose)
|
126 | 60 | -6 | -6 | -4 | 78 |
| m |
LDB+15 (Chinese hamster ovary cells in 15 g/L vs 5 g/L glucose)
|
294 | 205 | -23 | -15 | -44 | 26 |
| n |
BTX+17 (HUVEC eMPs in 5.6 mmol/l glucose + 19.4 mmol/l D-glucose vs 5.6 mmol/l glucose)
|
28 | 333 | 4 | 31 | -17 | -17 |
| o |
BTX+17 (HUVEC eMPs in 5.6 mmol/l glucose + 19.4 mmol/l L-glucose vs 5.6 mmol/l glucose)
|
24 | 377 | -3 | -20 | -22 | 95 |
| p |
SFKD17 (secretome of murine islets of Langerhans in 25 mM vs 11 mM glucose for 1 day)
|
59 | 54 | 4 | -28 | 111 | -28 |
| q |
SFKD17 (secretome of murine islets of Langerhans in 25 mM vs 11 mM glucose for 2 days)
|
82 | 104 | -1 | 20 | 12 | -63 |
| r |
IXA+19 (human aortal endothelial cells in 20 mM vs 5 mM glucose)
|
49 | 331 | 6 | -39 | 127 | -19 |
| s |
MHP+20 (rat H9c2 cells in 30 mM vs 5 mM glucose)
|
17 | 20 | 22 | -40 | -94 | 101 |
| t |
MHP+20 (human embryonic kidney cells in 30 mM vs 5 mM glucose)
|
127 | 39 | -21 | -10 | 56 | 126 |
| u |
MPR+20 (human aortic endothelial cells in 3h.high.glucose)
|
109 | 133 | 14 | 5 | 53 | -32 |
| v |
MPR+20 (human aortic endothelial cells in 24h.high.glucose)
|
224 | 98 | 7 | 0 | -46 | 17 |
| w |
MPR+20 (human aortic endothelial cells in 3h.high.mannitol)
|
154 | 127 | 10 | -6 | -36 | -2 |
| x |
MPR+20 (human aortic endothelial cells in 24h.high.mannitol)
|
178 | 128 | 10 | -23 | 19 | 5 |
a. b. c. Supporting Information Table of Pham & Wright (2008), filtered to include proteins with expression ratios < 0.9 or > 1.1 and with p-values < 0.05. d. Supplemetary Table ST4 of Waanders et al. (2009), filtered to include the proteins with ANOVA p-value < 0.01 (red- and blue-highlighted rows in the source table), and applying the authors’ criterion that proteins be identified by 2 or more unique peptides in at least 4 of the 8 most intense LC-MS/MS runs. e. Supplementary Table of Wang et al. (2009), filtered to include proteins with fold change > 1.2 or < 0.8. f. Table 1 of Maris et al. (2010). g. h. Table 1 of Chen et al. (2012). i. Proteins identified as differentially abundant in Supporting Information Table S5 of Schrimpe-Rutledge et al. (2012), filtered to include proteins with fold change > 2 or < 0.5. j. k. Table 1 of Chen et al. (2013a). l. Supplementary Table 1 of Chen et al. (2013b), filtered to include proteins with average fold change > 2.5 or < 0.4. m. Supporting Information Table S4 of Liu et al. (2015) for up- (Cluster 1) and down- (Cluster 5) regulated proteins. n. o. Electronic supplementary material Table 1 of Burger et al. (2017). p. q. Supplementary Table S1 of Schmudlach et al. (2017), filtered to include proteins with fold change > 2 or < 0.5 (ratios were computed from medians of iBAQ values for three replicates after quantile normalization). r. Supplementary Tables 1 and 2 of Irshad et al. (2019). s. t. Supplementary Table 2 of Meneses-Romero et al. (2020) (sheets “H9c2” and “HEK”). u. v. w. x. Treatment with high glucose (12.5 mmol/L) or high mannitol (7.0 mmol/L + 5.5 mmol/L glucose) followed by insulin, compared to normal glucose (5.5 mmol/L) followed by insulin. Source: Supplementary Tables S2–S6 of Madonna et al. (2020) (proteins uniquely identified in treatment and control conditions).
Burger D, Turner M, Xiao F, Munkonda MN, Akbari S, Burns KD. 2017. High glucose increases the formation and pro-oxidative activity of endothelial microparticles. Diabetologia 60:1791–1800. DOI: 10.1007/s00125-017-4331-2.
Chen Y-H, Chen J-Y, Chen Y-W, Lin S-T, Chan H-L. 2012. High glucose-induced proteome alterations in retinal pigmented epithelium cells and its possible relevance to diabetic retinopathy. Molecular Biosystems 8:3107–3124. DOI: 10.1039/C2MB25331C.
Chen J-Y, Chou H-C, Chen Y-H, Chan H-L. 2013a. High glucose-induced proteome alterations in hepatocytes and its possible relevance to diabetic liver disease. Journal of Nutritional Biochemistry 24:1889–1910. DOI: 10.1016/j.jnutbio.2013.05.006.
Chen X, Cui Z, Wei S, Hou J, Xie Z, Peng X, Li J, Cai T, Hang H, Yang F. 2013b. Chronic high glucose induced INS-1β cell mitochondrial dysfunction: A comparative mitochondrial proteome with SILAC. Proteomics 13:3030–3039. DOI: 10.1002/pmic.201200448.
Irshad Z, Xue M, Ashour A, Larkin JR, Thornalley PJ, Rabbani N. 2019. Activation of the unfolded protein response in high glucose treated endothelial cells is mediated by methylglyoxal. Scientific Reports 9:7889. DOI: 10.1038/s41598-019-44358-1.
Liu Z, Dai S, Bones J, Ray S, Cha S, Karger BL, Li JJ, Wilson L, Hinckle G, Rossomando A. 2015. A quantitative proteomic analysis of cellular responses to high glucose media in Chinese hamster ovary cells. Biotechnology Progress 31:1026–1038. DOI: 10.1002/btpr.2090.
Madonna R, Pieragostino D, Rossi C, Confalone P, Cicalini I, Minnucci I, Zucchelli M, Del Boccio P, De Caterina R. 2020. Simulated hyperglycemia impairs insulin signaling in endothelial cells through a hyperosmolar mechanism. Vascular Pharmacology 130:106678. DOI: 10.1016/j.vph.2020.106678.
Maris M, Ferreira GB, D’Hertog W, Cnop M, Waelkens E, Overbergh L, Mathieu C. 2010. High glucose induces dysfunction in insulin secretory cells by different pathways: A proteomic approach. Journal of Proteome Research 9:6274–6287. DOI: 10.1021/pr100557w.
Meneses-Romero E, Hernández-Orihuela L, Pando-Robles V, López TD, Oses-Prieto JA, Burlingame AL, Batista CVF. 2020. Quantitative proteomic analysis reveals high interference on protein expression of H9c2 cells activated with glucose and cardiotonic steroids. Journal of Proteomics 211:103536. DOI: 10.1016/j.jprot.2019.103536.
Pham TK, Wright PC. 2008. The proteomic response of Saccharomyces cerevisiae in very high glucose conditions with amino acid supplementation. Journal of Proteome Research 7:4766–4774. DOI: 10.1021/pr800331s.
Schmudlach A, Felton J, Kennedy RT, Dovichi NJ. 2017. Bottom-up proteomics analysis of the secretome of murine islets of Langerhans in elevated glucose levels. Analyst 142:284–291. DOI: 10.1039/C6AN02268E.
Schrimpe-Rutledge AC, Fontès G, Gritsenko MA, Norbeck AD, Anderson DJ, Waters KM, Adkins JN, Smith RD, Poitout V, Metz TO. 2012. Discovery of novel glucose-regulated proteins in isolated human pancreatic islets using LC–MS/MS-based proteomics. Journal of Proteome Research 11:3520–3532. DOI: 10.1021/pr3002996.
Waanders LF, Chwalek K, Monetti M, Kumar C, Lammert E, Mann M. 2009. Quantitative proteomic analysis of single pancreatic islets. Proceedings of the National Academy of Sciences 106:18902–18907. DOI: 10.1073/pnas.0908351106.
Wang X-L, Fu A, Spiro C, Lee H-C. 2009. Proteomic analysis of vascular endothelial cells: Effects of laminar shear stress and high glucose. Journal of Proteomics & Bioinformatics 2:445–445. DOI: 10.4172/jpb.1000104.