Hyperosmotic Stress

This vignette from the R package canprot reproduces calculations of compositional oxidation state and hydration state that are described in a preprint posted on bioRxiv (Dick, 2017).

Abbreviations

VHG (very high glucose), ARPE-19 (human retinal pigmented epithelium cells), ECO57 (Escherichia coli O157:H7 Sakai), IOBA-NHC (human conjunctival epithelial cells), CAUCR (Caulobacter crescentus), tr. (transcriptome), pr. (proteome), CHO (Chinese hamster ovary cells).

Data Sources

PW08 VHG (300 g/L) vs control (20 g/L). The comparisons here use proteins with expression ratios < 0.9 or > 1.1 and with p-values < 0.05. Source: SI Table of Pham & Wright (2008).

WCM+09 24 h at 16.7 mM vs 5.6 mM glucose. Source: extracted from Suppl. Table ST4 of Waanders et al. (2009); including the red- and blue-highlighted rows in the source table (those with ANOVA p-value < 0.01), 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.

OBBH11 300 mOsm (control) or 400 mOsm (NaCl treatment). Source: Suppl. Table 1 of Oswald et al. (2011).

CCC+12 Mannitol-balanced 5.5 (control), 25 or 100 mM ᴅ-glucose media. Source: Table 1 of Chen et al. (2012).

KKG+12 Temperature and NaCl treatment (control: 35 °C, aw = 0.993). Source: Suppl. Tables S13–S16 of Kocharunchitt et al. (2012).

CCCC13 5.5 (control), 25 or 100 mM ᴅ-glucose. Source: Table 1 of Chen et al. (2013).

TSZ+13 Gill proteome of Japanese eel (Anguilla japonica) adapted to seawater or freshwater. Source: Protein IDs from Suppl. Table 3 and gene names of human orthologs from Suppl. File 4 of Tse et al. (2013).

GSC14 30 min in YNB (2% glucose) vs YPKG (0.5% glucose) media. Source: extracted from Suppl. Files 3 and 5 of Giardina, Stanley & Chiang (2014), using the authors’ criterion of p-value <0.05.

CLG+15 280 (control), 380, or 480 mOsm (NaCl treatment) for 24 h. Source: Table 2 of Chen et al. (2015).

KLB+15 Overnight treatment with a final concentration of 40/50 mM NaCl or 200 mM sucrose vs M2 minimal salts medium plus glucose (control). Source: Additional file Table S2 of Kohler et al. (2015).

LDB+15 15 g/L vs 5 g/L (control) glucose at days 0, 3, 6, and 9. The comparisons here use all proteins reported to have expression patterns in Cluster 1 (up) or Cluster 5 (down), or only the proteins with high expression differences (ratio ≤-0.2 or ≥0.2) at all time points. Source: SI Table S4 of Liu et al. (2015).

YDZ+15 4.21 osmol/kg vs 3.17 osmol/kg osmotic pressure (NaCl treatment). Source: Table 1 of Yang et al. (2015).

RBP+16 0.1 M KCl (treatment) vs medium with no added KCl (control). Source: Suppl. Tables 2 and 3 of Silva Rodrigues et al. (2016).

Summary Table

This table compares the chemical compositions of groups of proteins that are relatively down- and up-expressed (n1 and n2, respectively) in cells grown in hyperosmotic stress compared to control conditions.

library(canprot)
data(canprot)
datasets <- pdat_osmotic()
comptab <- lapply_canprot(datasets, function(dataset) {
  pdat <- get_pdat(dataset, "pdat_osmotic")
  ZC_nH2O(pdat, plot.it=FALSE)
}, varlist="pdat_osmotic")
library(xtable)
xsummary(comptab)
ZC nH2O
set reference (description) n1 n2 MD ES p-value MD ES p-value
a PW08 (S. cerevisiae VHG 2h) 38 44 -0.010 44 3e-01 -0.029 39 1e-01
b PW08 (S. cerevisiae VHG 10h) 33 62 0.001 51 9e-01 -0.020 40 1e-01
c PW08 (S. cerevisiae VHG 12h) 18 65 0.008 57 4e-01 -0.017 42 3e-01
d WCM+09 (mouse pancreatic islets) 63 94 -0.007 46 4e-01 -0.038 40 4e-02
e OBBH11 (adipose-derived stem cells) 148 144 -0.006 46 2e-01 0.021 57 5e-02
f CCC+12 (ARPE-19 25mM) 17 11 -0.046 32 1e-01 -0.052 34 2e-01
g CCC+12 (ARPE-19 100mM) 21 24 -0.011 48 8e-01 -0.023 43 4e-01
h KKG+12 (ECO57 25C_aw0.985) 114 61 0.002 53 5e-01 -0.016 45 3e-01
i KKG+12 (ECO57 14C_aw0.985) 238 61 -0.008 49 8e-01 -0.025 42 5e-02
j KKG+12 (ECO57 25C_aw0.967) 263 56 -0.003 47 5e-01 0.032 59 3e-02
k KKG+12 (ECO57 14C_aw0.967) 372 73 -0.002 49 7e-01 -0.008 48 6e-01
l CCCC13 (Chang liver cells 25mM) 32 39 -0.014 45 4e-01 0.001 49 9e-01
m CCCC13 (Chang liver cells 100mM) 19 50 0.022 61 2e-01 -0.029 40 2e-01
n TSZ+13 (eel gill) 49 28 0.000 55 4e-01 -0.026 43 3e-01
o GSC14 (S. cerevisiae t30a) 78 77 0.003 53 5e-01 -0.024 41 7e-02
p GSC14 (S. cerevisiae t30b) 67 67 -0.002 49 9e-01 -0.017 45 3e-01
q GSC14 (S. cerevisiae t30c) 87 87 -0.001 47 6e-01 -0.014 45 2e-01
r CLG+15 (IOBA-NHC) 25 38 -0.012 40 2e-01 0.010 53 7e-01
s KLB+15 (CAUCR succinate tr.) 105 96 0.022 63 1e-03 -0.060 35 3e-04
t KLB+15 (CAUCR NaCl tr.) 209 142 0.007 56 5e-02 -0.040 41 3e-03
u KLB+15 (CAUCR succinate pr.) 33 33 0.019 65 3e-02 -0.045 35 4e-02
v KLB+15 (CAUCR NaCl pr.) 33 27 0.018 65 5e-02 -0.040 36 7e-02
w LDB+15 (CHO all) 294 205 -0.027 36 3e-07 -0.020 46 1e-01
x LDB+15 (CHO high) 66 75 -0.032 35 3e-03 -0.003 52 7e-01
y YDZ+15 (Yarrowia lipolytica) 14 28 0.033 61 2e-01 -0.032 42 4e-01
z RBP+16 (Paracoccidioides lutzii) 160 141 0.002 52 5e-01 -0.044 36 1e-05

Mean Differences

The dataset for adipose-derived stem cells is highlighted in orange.

col <- rep("black", length(datasets))
col[grepl("=ASC", datasets)] <- "orange"
diffplot(comptab, col=col)

References

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. 2013. 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 L., Li J., Guo T., Ghosh S., Koh SK., Tian D., Zhang L., Jia D., Beuerman RW., Aebersold R., Chan ECY., Zhou L. 2015. Global metabonomic and proteomic analysis of human conjunctival epithelial cells (IOBA-NHC) in response to hyperosmotic stress. Journal of Proteome Research 14:3982–3995. DOI: 10.1021/acs.jproteome.5b00443.

Giardina BJ., Stanley BA., Chiang H-L. 2014. Glucose induces rapid changes in the secretome of Saccharomyces cerevisiae. Proteome Science 12:9. DOI: 10.1186/1477-5956-12-9.

Kocharunchitt C., King T., Gobius K., Bowman JP., Ross T. 2012. Integrated transcriptomic and proteomic analysis of the physiological response of Escherichia coli O157:H7 Sakai to steady-state conditions of cold and water activity stress. Molecular & Cellular Proteomics 11:M111.009019. DOI: 10.1074/mcp.M111.009019.

Kohler C., Lourenço RF., Bernhardt J., Albrecht D., Schüler J., Hecker M., Gomes SL. 2015. A comprehensive genomic, transcriptomic and proteomic analysis of a hyperosmotic stress sensitive \(\alpha\)-proteobacterium. BMC Microbiology 15:1–15. DOI: 10.1186/s12866-015-0404-x.

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.

Oswald ES., Brown LM., Bulinski JC., Hung CT. 2011. Label-free protein profiling of adipose-derived human stem cells under hyperosmotic treatment. Journal of Proteome Research 10:3050–3059. DOI: 10.1021/pr200030v.

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.

Silva Rodrigues LN da., Almeida Brito W de., Parente AFA., Weber SS., Bailão AM., Casaletti L., Borges CL., Almeida Soares CM de. 2016. Osmotic stress adaptation of Paracoccidioides lutzii, Pb01, monitored by proteomics. Fungal Genetics and Biology 95:13–23. DOI: 10.1016/j.fgb.2016.08.001.

Tse WKF., Sun J., Zhang H., Law AYS., Yeung BHY., Chow SC., Qiu J-W., Wong CKC. 2013. Transcriptomic and iTRAQ proteomic approaches reveal novel short-term hyperosmotic stress responsive proteins in the gill of the Japanese eel (Anguilla japonica). Journal of Proteomics 89:81–94. DOI: 10.1016/j.jprot.2013.05.026.

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.

Yang L-B., Dai X-M., Zheng Z-Y., Zhu L., Zhan X-B., Lin C-C. 2015. Proteomic analysis of erythritol-producing Yarrowia lipolytica from glycerol in response to osmotic pressure. Journal of Microbiology and Biotechnology 25:1056–1069. DOI: 10.4014/jmb.1412.12026.