JMDplots vignettes

Cell Extracts in Hypoxia

This vignette from the R package JMDplots version 1.2.19-9 shows chemical metrics for proteins that are differentially expressed in hypoxia compared to control conditions. The analysis is described in more detail in a paper (Dick, 2021). Abbreviations:

datasets <- pdat_hypoxia(2020)

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 SBB+06 (mouse malignant melanoma plasma membrane) 574 289 -2 10 112 -50
b FWH+13 (THP-1 macrophages) 56 40 8 14 18 -92
c RHD+13 (A431 epithelial carcinoma cells Hx48) 211 92 9 -1 78 -73
d RHD+13 (A431 epithelial carcinoma cells Hx72) 88 67 -19 -21 72 -86
e VTMF13 (SH-SY5Y neuroblastoma cells) 141 64 -13 31 -4 1
f DCH+14 (mouse 4T1 cells) 71 60 16 -33 -300 7
g DYL+14 (A431 epithelial carcinoma cells Hx48-S) 65 34 30 -45 122 -51
h DYL+14 (A431 epithelial carcinoma cells Hx72-S) 137 61 2 -28 18 -50
i DYL+14 (A431 epithelial carcinoma cells Hx48-P) 74 44 7 -6 124 113
j DYL+14 (A431 epithelial carcinoma cells Hx72-P) 67 53 -9 -13 20 2
k BSA+15 (HeLa cervical cancer cells) 53 72 6 -17 225 -155
l HWA+16 (U87MG and 786-O cancer cells) 137 164 0 -23 22 -75
m LCS16 (HCT116 colon cancer translation) 469 1024 -21 -21 323 38
n CGH+17 (mouse cardiac fibroblasts whole) 38 48 -12 16 -374 33
o ZXS+17 (U87 and U251 glioblastoma cells) 143 122 26 -57 61 3
p CLY+18 (HCT116 colon cancer cells proteome) 108 127 -6 8 -117 -159
q GBH+18 (SW620 colorectal cancer cells) 237 67 9 -4 -12 -1
r LKK+18 (hUCB mesehnchymal stem cells) 44 62 8 38 65 73
s WTG+18 (adipose-derived mesehnchymal stem cells) 163 57 9 -32 49 10
t CSK+19 (HeLa cervical cancer cells) 36 45 4 1 6 -47
u GPT+19 (MIAPaCa-2 pancreatic cancer cells pulse/trace Light serum replete) 62 180 2 19 -44 -43
v GPT+19 (MIAPaCa-2 pancreatic cancer cells pulse/trace Heavy serum replete) 196 57 -10 7 -160 -106
w KAN+19 (cancer-associated fibroblasts proteome) 221 124 20 -4 114 86
x LLL+19 (human periodontal ligament cells) 67 153 -27 32 -216 -9
y BCMS20 (MCF-7 breast cancer cells) 153 161 19 -38 -192 -108
z RVN+20 (PC-3 prostate cancer cells in DMSO) 116 86 -40 -20 230 32
A RVN+20 (PC-3 prostate cancer cells in NO.sul) 28 33 -15 -7 247 82
B RVN+20 (PC-3 prostate cancer cells in sul) 17 54 -47 -11 -203 34
C RVN+20 (PC-3 prostate cancer cells in DMSO.4Gy) 24 38 -20 -21 40 -119
D RVN+20 (PC-3 prostate cancer cells in NO.sul.4Gy) 21 52 46 -4 88 82
E RVN+20 (PC-3 prostate cancer cells in sul.4Gy) 34 25 15 -46 -54 -110
F SPJ+20 (canine POS cells) 43 102 4 -22 -30 7
G SPJ+20 (canine HMPOS cells) 62 69 5 -15 -74 -8

Data Sources

Where given, gene names or other identifiers were converted to UniProt accession numbers using the UniProt mapping tool.

a. Supplemental Table 1 of Stockwin et al. (2006), filtered to include proteins with expression ratio ≥ 1.7 or ≤ 0.58. b. Supplemental Table 2A of Fuhrmann et al. (2013) (control virus cells). c. d. Supplemental Table S1 of Ren et al. (2013), filtered to include proteins with iTRAQ ratios < 0.83 or > 1.2 and p-value < 0.05. e. Supporting Information table of Villeneuve et al. (2013), filtered to include proteins with a normalized expression ratio of > 1.2 or < 0.83. f. Supporting Information Table S1 Djidja et al. (2014). g. h. i. j. Supplemental Table S1 of Dutta et al. (2014), filtered to include proteins with p-value < 0.05 (-S: supernatant fraction; -P: pellet fraction). k. Supplementary Table S1 of Bousquet et al. (2015). l. Supplemental Information Table S1 of Ho et al. (2016), filtered to include proteins with a fold change of < 0.5 or > 1 and that were detected in only hypoxic or only normoxic conditions. m. Gene names from Lai, Chang & Sun (2016) (data files provided by Ming-Chih Lai). n. Extracted from Table S2E (whole cell lysate) of Cosme et al. (2017), keeping proteins with FDR < 0.05. o. Supplemental Table S4 of Zhang et al. (2017). p. Supplementary Tables S6-S7 (proteome) of Chen et al. (2018). q. List of up- and down-regulated proteins from Greenhough et al. (2018) (provided by Alex Greenhough), filtered to include proteins with average fold change ≥ 1.5 or ≤ 2/3. r. Gene names from Figure 1F of Lee et al. (2018). s. Supplemental file 1 of Wobma et al. (2018) (provided by Gordana Vunjak-Novakovic), filtered to include proteins with Normalized Ratio [Hypoxia MSC/Control MSC] ≥ 1.5 or ≤ 2/3 and p-value < 0.05. t. Gene names from Supplementary file S1 of Chachami et al. (2019) for the two replicates labelled as “input_Log2ratioHL_firstIP” and “input_Log2ratioLH_secondIP” (soluble extracts before immunoprecipitation), filtered to include proteins where Log2ratio is > 0.2 or < -0.2 for both replicates. u. v. Supplementary Data 1 of Gupta et al. (2019), filtered to include proteins with p-value < 0.05 and fold-change > 2 or < 0.5 w. Proteins identified as up- or down-regulated > 1 SD in Data File S1 of Kugeratski et al. (2019) (sheet “Proteome”). x. Additional file 1: Table S1 of Li et al. (2019). y. Gene names from Supplementary Information Tables S6 and S7 of Bush et al. (2020). z. A. B. C. D. E. Supplementary Table 1A of Ross et al. (2020), filtered to include proteins with median fold change between normoxic and hypoxic conditions in any individual treatment > 1.5 or < 2/3. F. G. Supplementary Tables S3b (sheet “HPNP”) and S3c (sheet “HHNH”) of Song et al. (2020).

Acknowledgements

Thanks to Alex Greenhough, Ming-Chih Lai, and Gordana Vunjak-Novakovic for providing data files.

References

Bousquet PA, Sandvik JA, Arntzen MØ, Jeppesen Edin NF, Christoffersen S, Krengel U, Pettersen EO, Thiede B. 2015. Hypoxia strongly affects mitochondrial ribosomal proteins and translocases, as shown by quantitative proteomics of HeLa cells. International Journal of Proteomics 2015:678527. DOI: 10.1155/2015/678527.

Bush JT, Chan MC, Mohammed S, Schofield C. 2020. Quantitative MS-based proteomics comparing the MCF-7 cellular response to hypoxia and a 2-oxoglutarate analogue. ChemBioChem 21:1647–1655. DOI: 10.1002/cbic.201900719.

Chachami G, Stankovic-Valentin N, Karagiota A, Basagianni A, Plessmann U, Urlaub H, Melchior F, Simos G. 2019. Hypoxia-induced changes in SUMO conjugation affect transcriptional regulation under low oxygen. Molecular & Cellular Proteomics 18:1197–1209. DOI: 10.1074/mcp.RA119.001401.

Chen J-T, Liu C-C, Yu J-S, Li H-H, Lai M-C. 2018. Integrated omics profiling identifies hypoxia-regulated genes in HCT116 colon cancer cells. Journal of Proteomics 188:139–151. DOI: 10.1016/j.jprot.2018.02.031.

Cosme J, Guo H, Hadipour-Lakmehsari S, Emili A, Gramolini AO. 2017. Hypoxia-induced changes in the fibroblast secretome, exosome, and whole-cell proteome using cultured, cardiac-derived cells isolated from neonatal mice. Journal of Proteome Research 16:2836–2847. DOI: 10.1021/acs.jproteome.7b00144.

Djidja M-C, Chang J, Hadjiprocopis A, Schmich F, Sinclair J, Mršnik M, Schoof EM, Barker HE, Linding R, Jørgensen C, Erler JT. 2014. Identification of hypoxia-regulated proteins using MALDI-mass spectrometry imaging combined with quantitative proteomics. Journal of Proteome Research 13:2297–2313. DOI: 10.1021/pr401056c.

Dutta B, Yan R, Lim SK, Tam JP, Sze SK. 2014. Quantitative profiling of chromatome dynamics reveals a novel role for HP1BP3 in hypoxia-induced oncogenesis. Molecular & Cellular Proteomics 13:3236–3249. DOI: 10.1074/mcp.M114.038232.

Fuhrmann DC, Wittig I, Heide H, Dehne N, Brüne B. 2013. Chronic hypoxia alters mitochondrial composition in human macrophages. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1834:2750–2760. DOI: 10.1016/j.bbapap.2013.09.023.

Greenhough A, Bagley C, Heesom KJ, Gurevich DB, Gay D, Bond M, Collard TJ, Paraskeva C, Martin P, Sansom OJ, Malik K, Williams AC. 2018. Cancer cell adaptation to hypoxia involves a HIF-GPRC5A-YAP axis. EMBO Molecular Medicine 10:e8699. DOI: 10.15252/emmm.201708699.

Gupta N, Park JE, Tse W, Low JK, Kon OL, McCarthy N, Sze SK. 2019. ERO1α promotes hypoxic tumor progression and is associated with poor prognosis in pancreatic cancer. Oncotarget 10:5970–5982. DOI: 10.18632/oncotarget.27235.

Ho JJD, Wang M, Audas TE, Kwon D, Carlsson SK, Timpano S, Evagelou SL, Brothers S, Gonzalgo ML, Krieger JR, Chen S, Uniacke J, Lee S. 2016. Systemic reprogramming of translation efficiencies on oxygen stimulus. Cell Reports 14:1293–1300. DOI: 10.1016/j.celrep.2016.01.036.

Kugeratski FG, Atkinson SJ, Neilson LJ, Lilla S, Knight JRP, Serneels J, Juin A, Ismail S, Bryant DM, Markert EK, Machesky LM, Mazzone M, Sansom OJ, Zanivan S. 2019. Hypoxic cancer-associated fibroblasts increase NCBP2-AS2/HIAR to promote endothelial sprouting through enhanced VEGF signaling. Science Signaling 12:eaan8247. DOI: 10.1126/scisignal.aan8247.

Lai M-C, Chang C-M, Sun HS. 2016. Hypoxia induces autophagy through translational up-regulation of lysosomal proteins in human colon cancer cells. PLOS One 11:e0153627. DOI: 10.1371/journal.pone.0153627.

Lee J, Kim H-S, Kim S-M, Kim D-I, Lee C-W. 2018. Hypoxia upregulates mitotic cyclins which contribute to the multipotency of human mesenchymal stem cells by expanding proliferation lifespan. Molecules and Cells 41:207–213. DOI: 10.14348/molcells.2018.2231.

Li Q, Luo T, Lu W, Yi X, Zhao Z, Liu J. 2019. Proteomic analysis of human periodontal ligament cells under hypoxia. Proteome Science 17:3. DOI: 10.1186/s12953-019-0151-2.

Ren Y, Hao P, Dutta B, Cheow ESH, Sim KH, Gan CS, Lim SK, Sze SK. 2013. Hypoxia modulates A431 cellular pathways association to tumor radioresistance and enhanced migration revealed by comprehensive proteomic and functional studies. Molecular & Cellular Proteomics 12:485–498. DOI: 10.1074/mcp.M112.018325.

Ross JA, Vissers JPC, Nanda J, Stewart GD, Husi H, Habib FK, Hammond DE, Gethings LA. 2020. The influence of hypoxia on the prostate cancer proteome. Clinical Chemistry and Laboratory Medicine 58:980–993. DOI: 10.1515/cclm-2019-0626.

Song Z, Pearce MC, Jiang Y, Yang L, Goodall C, Miranda CL, Milovancev M, Bracha S, Kolluri SK, Maier CS. 2020. Delineation of hypoxia-induced proteome shifts in osteosarcoma cells with different metastatic propensities. Scientific Reports 10:727. DOI: 10.1038/s41598-019-56878-x.

Stockwin LH, Blonder J, Bumke MA, Lucas DA, Chan KC, Conrads TP, Issaq HJ, Veenstra TD, Newton DL, Rybak SM. 2006. Proteomic analysis of plasma membrane from hypoxia-adapted malignant melanoma. Journal of Proteome Research 5:2996–3007. DOI: 10.1021/pr0601739.

Villeneuve L, Tiede LM, Morsey B, Fox HS. 2013. Quantitative proteomics reveals oxygen-dependent changes in neuronal mitochondria affecting function and sensitivity to rotenone. Journal of Proteome Research 12:4599–4606. DOI: 10.1021/pr400758d.

Wobma HM, Tamargo MA, Goeta S, Brown LM, Duran-Struuck R, Vunjak-Novakovic G. 2018. The influence of hypoxia and IFN-γ on the proteome and metabolome of therapeutic mesenchymal stem cells. Biomaterials 167:226–234. DOI: 10.1016/j.biomaterials.2018.03.027.

Zhang K, Xu P, Sowers JL, Machuca DF, Mirfattah B, Herring J, Tang H, Chen Y, Tian B, Brasier AR, Sowers LC. 2017. Proteome analysis of hypoxic glioblastoma cells reveals sequential metabolic adaptation of one-carbon metabolic pathways. Molecular & Cellular Proteomics 16:1906–1921. DOI: 10.1074/mcp.RA117.000154.