Multiple-Omics Techniques Reveal the Role of Glycerophospholipid Metabolic Pathway in the Response of Saccharomyces cerevisiae Against Hypoxic Stress.

Zhengchao Xia,Lei Li,Xuelin Zhou,Yi Ma,Yi Wu,Zhong Huang,Xiaorong Li,Pingxiang Xu,Ming Xue,Jingyi Li

doi:10.3389/fmicb.2019.01398

Abstract

Although the biological processes of organism under hypoxic stress had been elucidated, the whole physiological changes of Saccharomyces cerevisiae are still unclear. In this work, we investigated the changes of biological process of S. cerevisiae under hypoxia by the methods of transcriptomics, proteomics, metabolomics, and bioinformatics. The results showed that the expression of a total of 1017 mRNA in transcriptome, 213 proteins in proteome, and 51 metabolites in metabolome had been significantly changed between the hypoxia and normoxia conditions. Moreover, based on the integration of system-omics data, we found that the carbohydrate, amino acids, fatty acid biosynthesis, lipid metabolic pathway, and oxidative phosphorylation were significantly changed in hypoxic stress. Among these pathways, the glycerophospholipid metabolic pathway was remarkably up-regulated from the mRNA, protein, and metabolites levels under hypoxic stress, and the expression of relevant mRNA was also confirmed by the qPCR. The metabolites of glycerophospholipid pathway such as phosphatidylcholine, phosphatidylethanolamine, phosphoinositide, and phosphatidic acids probably maintained the stability of cell membranes against hypoxic stress to relieve the cell injury, and kept S. cerevisiae survive with energy production. These findings in the hypoxic omics and integrated networks provide very useful information for further exploring the molecular mechanism of hypoxic stress.

Highlights

Oxygen plays an important role in the survival of many eukaryotic cells
All proteins were screened with the threshold of p-value < 0.05 and fold Change (FC) > 1.2 or FC < 0.83 to gain 607 differentially expressed proteins (DEPs), 789 DEPs, and 214 DEPs in the results of Hpx1, Hpx5, and Hpx10 compared with Con21, respectively (Supplementary Data Sheet S2)
2826 phosphoproteins were identified in different groups (Con21, 2496; Hpx10, 2428; Hpx5, 2423; Hpx1, 2386) (Supplementary Data Sheet S3). 100 communal phosphoproteins were screened between 213 gradually up-/down-regulated proteins and Hpx1 phosphoproteins by Venny 2.1 (BioinfoGP, CNB-CSIC, Spain) (Supplementary Data Sheet S3)

Summary

Introduction

Oxygen plays an important role in the survival of many eukaryotic cells. Hypoxia of cells, tissues, organs, and organisms causes a series of pathophysiological processes and plays important role in different diseases such as altitude sickness, stroke, asthma, angina pectoris, myocardial infarction, and tumor (Stub et al, 2015; McBrien et al, 2018). Metabolomics is Abbreviations: 2-PGA, 2-phosphoglycerate; 3-PGA, 3-phosphoglycerate; ADH, alcohol dehydrogenase; CDP, cytidine diphosphate; Ch, choline; CK, choline kinase; CL, cardiolipin; CoA, coenzyme A; Con, control; CS, citrate synthase; DEGs, differentially expressed genes; DEPs, differentially expressed proteins; DG, diacylglycerol; DHAP, dihydroxyacetone phosphate; DMs, differential metabolites; DTT, dithiothreitol; EPT1, choline/ethanolamine phosphotransferase; ETA, ethanolamine; FAs, fatty acids; FASP, filter-aided sample preparation; FDR, false discovery rate; G3P, glycerol 3-phosphate; G3PC, glycerol-3-phosphocholine; G3PE, glycerol-3-phosphoethanolamine; GC, gas chromatography; Glu, glucose; GPL, glycerophospholipid; GPM2, phosphoglycerate mutase 2; GPMP, glycerophospholipid metabolic pathway; HCD, higher-energy collisional dissociation; HIF, hypoxic-inducible factor; HPLC, high performance liquid chromatography; Hpx, hypoxia; IAA, iodoacetamide; iTRAQ, isobaric tags for relative and absolute quantification; KEGG, kyoto encyclopedia of genes and genomes; KGD2, alpha-ketoglutarate dehydrogenase; LPA, lysophosphatidic acid (1-acyl-G3P); LPCAT, LPEAT, LPGAT, LPIAT, LPSAT, lysophospholipid acyltransferase; OPLS-DA, orthogonal partial least-squares discriminant analysis; PA, phosphatidic acid (1,2-diacylG3P); PC, phosphatidylcholine/lecithin; P-Ch, phospho-choline; PDME, phosphatidyl-N-dimethylethanolamine; PE, phosphatidylethanolamine; P-ETA, phosphor-ethanolamine; PG, phosphatidylglycerol; PGAL, 3-phosphoglyceraldehyde; PGP, phosphatidylglycerolphosphate; PI, phosphatidylinositol; PMME, phosphatidyl-N-methylethanolamine; PS, phosphatidylserine; PUFAs, polyunsaturated fatty acids; S. cerevisiae, Saccharomyces cerevisiae; SER2, phosphoserine phosphatase; SER3, phosphoglycerate dehydrogenase; SILAC, stable isotope labeling with amino acids in cell culture; SLC1, lysophosphatidate acyltransferase; SOD, superoxide dismutase; UFAs, unsaturation fatty acids; YMDB, yeast metabolome database

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Discussion

Conclusion