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. 2018 Jan 19;8(1):1246.
doi: 10.1038/s41598-017-18889-4.

Comparative transcriptomic and proteomic analysis of Arthrobacter sp. CGMCC 3584 responding to dissolved oxygen for cAMP production

Affiliations

Comparative transcriptomic and proteomic analysis of Arthrobacter sp. CGMCC 3584 responding to dissolved oxygen for cAMP production

Huanqing Niu et al. Sci Rep. .

Abstract

Arthrobacter sp. CGMCC 3584 is able to produce high yields of extracellular cyclic adenosine monophosphate (cAMP), which plays a vital role in the field of treatment of disease and animal food, during aerobic fermentation. However, the molecular basis of cAMP production in Arthrobacter species is rarely explored. Here, for the first time, we report the comparative transcriptomic and proteomic study of Arthrobacter cells to elucidate the higher productivity of cAMP under high oxygen supply. We finally obtained 14.1% and 19.3% of the Arthrobacter genome genes which were up-regulated and down-regulated notably, respectively, with high oxygen supply, and identified 54 differently expressed proteins. Our results revealed that high oxygen supply had two major effects on metabolism: inhibition of glycolysis, pyruvate metabolism, nitrogen metabolism, and amino acid metabolism (histidine, branched-chain amino acids and glutamate metabolism); enhancement of the tricarboxylic acid cycle and purine metabolism. We also found that regulation of adenylate cyclase and phosphodiesterase was not significant under high oxygen supply, suggesting efficient cAMP export might be important in cAMP production. These findings may contribute to further understanding of capacities of Arthrobacter species and would be highly useful in genetic regulation for desirable production.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
The time courses of DCW (a) and cAMP (b), and DO (c) and glucose (d) in the 5-L bioreactor fermentation with low oxygen supply (L) and high oxygen supply (H).
Figure 2
Figure 2
Venn diagram of the up-regulated (a) or down-regulated (b) genes (more than twofold) with high oxygen supply of 12 h, 24 h, 36 h and 48 h samples.
Figure 3
Figure 3
Function classifications of differentially expressed genes (fold changes of at least 2) at 12 h (a), 24 h (b), 36 h (c) and 48 h (d), respectively. Abbreviations: J, Translation, ribosomal structure and biogenesis; A, RNA processing and modification; K, Transcription; L, Replication, recombination and repair; B, Chromatin structure and dynamics; D, Cell cycle control, cell division, chromosome partitioning; Y, Nuclear structure; V, Defense mechanisms; T, Signal transduction mechanisms; M, Cell wall/membrane/envelope biogenesis; N, Cell motility; Z, Cytoskeleton; W, Extracellular structures; U, Intracellular trafficking, secretion, and vesicular transport; O, Posttranslational modification, protein turnover, chaperones; C, Energy production and conversion; G, Carbohydrate transport and metabolism; E, Amino acid transport and metabolism; F, Nucleotide transport and metabolism; H, Coenzyme transport and metabolism; I, Lipid transport and metabolism; P, Inorganic ion transport and metabolism; Q, Secondary metabolites biosynthesis, transport and catabolism; R, General function prediction only; S, Function unknown.
Figure 4
Figure 4
2D gel maps of proteins extracted from Arthrobacter sp. CGMCC 3584 at 12 h with low oxygen supply (a), at 12 h with high oxygen supply (b), at 36 h with low oxygen supply (c) and at 36 h with high oxygen supply (d). Differentially expressed protein spots which were identified successfully for each treatment are marked with numbers.
Figure 5
Figure 5
Function classifications of differentially expressed proteins (fold changes of at least 2.5) at 12 h (a) and 36 h (b). For abbreviations, see the legend to Fig. 3.
Figure 6
Figure 6
The central carbon metabolic network of Arthrobacter sp. CGMCC 3584 (A) and 2-fold changes in expression levels of the related genes and proteins responding to different oxygen supply (B). The pathway is based on the KEGG pathway database (http://www.genome.jp/kegg/pathway.html).

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