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. 2023 Dec 18;9(1):1643-1655.
doi: 10.1021/acsomega.3c08243. eCollection 2024 Jan 9.

Proteomics and Metabolomics Analysis Reveal the Regulation Mechanism of Linoleate Isomerase Activity and Function in Propionibacterium acnes

Ying Liu  1 , Yeping Chen  1 , Xiqing Yue  1 , Yingying Liu  1 , Jianting Ning  1 , Libo Li  1 , Junrui Wu  1 , Xue Luo  1 , Shuang Zhang  2
Affiliations

Proteomics and Metabolomics Analysis Reveal the Regulation Mechanism of Linoleate Isomerase Activity and Function in Propionibacterium acnes

Ying Liu et al. ACS Omega. .

Abstract

Conjugated linoleic acid (CLA) holds significant application prospects due to its anticancer, anti-atherosclerosis, lipid-lowering, weight-loss, and growth-promoting functions. The key to its efficient production lies in optimizing the biocatalytic performance of linoleic acid isomerase (LAI). Here, we constructed a Propionibacterium acnes mutant library and screened positive mutants with high linoleate isomerase activity. The proteomics and metabolomics were used to explore the mechanism in the regulation of linoleic acid isomerase activity. High-throughput proteomics revealed 104 differentially expressed proteins unique to positive mutant strains of linoleic acid isomerase of which 57 were upregulated and 47 were downregulated. These differentially expressed proteins were primarily involved in galactose metabolism, the phosphotransferase system, starch metabolism, and sucrose metabolism. Differential metabolic pathways were mainly enriched in amino acid biosynthesis, including glutamate metabolism, the Aminoacyl-tRNA biosynthesis pathway, and the ABC transporter pathway. The upregulated metabolites include dl-valine and Acetyl coA, while the downregulated metabolites include Glutamic acid and Phosphoenolpyruvate. Overall, the activity of linoleic acid isomerase in the mutant strain was increased by the regulation of key proteins involved in galactose metabolism, sucrose metabolism, and the phosphotransferase system. This study provides a theoretical basis for the development of high-yield CLA food.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Flow cytometry sorting. (A) Sorting of the control group. (B) Sorting of experimental groups.
Figure 2
Figure 2
Statistical histogram of the identification and quantitative results.
Figure 3
Figure 3
Identify proteins for functional annotation and bioinformatics analysis. (A) Gene ontology classification of proteins between infected and control groups, including biological processes (BP), molecular functions (MF), and cellular components (CC). (B) The volcano plot shows up- (red) or downregulated (blue) proteins between mutated and control groups. (C) Differentially expressed protein subcellular location pie chart.
Figure 4
Figure 4
Enrichment analysis of differentially abundant proteins. (A) Enrichment analysis of protein domains. (B) KEGG pathway enrichment analysis. The size and color of each bubble represent the number of proteins enriched in the pathway and the enrichment significance, respectively.
Figure 5
Figure 5
Volcano map of differential metabolites and differential metabolite cluster analysis. (A) Control group. (B) Experimental group. (C) Volcano-pos. (D) Volcano-neg.
Figure 6
Figure 6
Differential metabolite clustering heatmap of the KEGG pathway.
Figure 7
Figure 7
Metabolic pathway map of differential metabolites. (A) TCA cycle metabolic pathway. (B) Alanine, aspartate, and glutamate metabolic pathways.
Figure 8
Figure 8
KEGG pathway notes.
See this image and copyright information in PMC

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