| Bacillus licheniformis(B. licheniformis) is a Gram-positive bacterium that is widely used in multiple fields: in agriculture as a probiotic and microbial fertilizer, and in the biotechnology industry for production of enzymes, antibiotics, acetoin, 2,3-butanediol, and poly-γ-glutamic acid(γ-PGA). γ-PGA is a kind of polymer, which is harmless to the environment and human body, and can act as targeted drug release agent, heavy metal adsorption agent, humectant, fertilizer absorption accelerator, feed additives. It is widely used in agriculture, food, feed, cosmetics, pharmaceuticals and sewage treatment and other fields. In this study, B. licheniformis strain WX-02, which was isolated from saline soil in Yingcheng, China, can produce high levels of γ-PGA under stress conditions such as high salt, high temperature, caustic alkali, and ultrasonic shock. We comprehensively study the genome, transcriptome, biological network, and molecular mechanism of high production of γ-PGA under salt stress.(1) We firstly obtained the complete genome of B. licheniformis WX-02, which comprises a circular chromosome of 4,286,821 base-pairs containing 4,512 protein-coding genes with an average GC content of 46.1%.(2) Based on the complete genome information of B. licheniformis WX-02, we detected and compared the transcriptome profiles under normal and high salt environment, using a strand-specific RNA-sequencing(ssRNA-seq) method. The ssRNA-seq libraries were constructed from three sample points: 11 th h(exponential phase, 0 h before the onset of 6% NaCl), 22 th h(11th h after onset of exposure to 6% NaCl), and 33 th h(22th h after onset of exposure to 6% NaCl). Based on the transcriptome data, we identified 90 small RNAs, 1,169 antisense transcripts and 871 operon structures. In addition, we identified DEGs using the DEGseq package plus fold-change ranking(FDR ≤ 0.001 and |log2FC| ≥ 1). To further explore the gene-expression profiles, we analyzed genes whose expression changed significantly among the three time points. In total, 234 genes were up-regulated and 1,439 genes were down-regulated at 22 th h relative to 11 th h, and 229 genes were induced and 1,427 genes were repressed at 33 th h relative to 11 th h; by contrast, only 50 genes were up-regulated and 83 were down-regulated at 22 th h relative to 33 th h. The above results revealed that genes involved in multiple functions were significantly repressed in long-term high salt adaptation process. The difference in transcriptional activity between early and late salt adaptation(22th h vs. 33 th h) was very small compared to the difference between normal and high salt conditions. Genes in most of the functional categories were repressed than induced under salt conditions. While the number of upregulated and downregulated genes related to ‘energy production and conversion(C)’ and ‘amino acid transport and metabolism(E)’ were almost consistent, indicating that this bacterium needs to balance certain biological pathways under salt environment and provided some necessary substrates or energy to maintain cellular survival. Compared to 11 th h, transcription levels of many genes involved in amino-acid biosynthesis, especially those related to glutamic acid and proline metabolism, were significantly different at 22 th h and 33 th h. Genes related to gltA, gltB, and gltC(which together encode Glutamate-oxoglutarate amidotransferase), symport of exogenous protons and sodium glutamate(gltP and glt T), ycgMNO were all induced by 6% NaCl. While the gene encoding GDH(gud B) was significantly down-regulated, suggesting that genes related to promotion of glutamic acid synthesis were activated by 6% NaCl, potentially explaining the high yield of γ-PGA under salt condition.(3) We constructed the protein-protein interaction(PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. We confirmed the high accuracy of PPI network from three perspectives: local structural features, functional similarities and transcriptional correlations. By incorporating the transcriptome data(11th h, 22 th h, 33 th h), we found that the topological properties of the PPI network were robust under normal and high salt conditions, it was not influenced by external environment. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02.(4) To further investigate the basic metabolism and important genes and metabolic pathways related to γ-PGA synthesis in B. licheniformis WX-02, we constructed the genome-scale metabolic network of B. licheniformis WX-02 by combining genomic annotation, high-throughput phenotype microarray(PM) experiments and literature-based metabolic information. The final metabolic model iWX1009 contains 1,009 genes, 1,141 metabolites and 1,762 reactions. The accuracy of the metabolic network was assessed by OmniLog PM experiment and the predicted metabolic phenotypes showed an agreement rate of 76.8% with the experimental PM data, suggesting that the metabolic network was reliable and can be used to simulate the real metabolic situation of B. licheniformis WX-02. In addition, 195 essential genes were predicted from LB medium, among which 149 could be verified with the experimental essential gene set of Bacillus subtilis(B. subtilis) 168. With the removal of 5 reactions from the network, the pathways for γ-PGA synthesis were optimized. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. We applied the system biology method to find the potential regulated factors, metabolites and metabolic pathways of the γ-PGA synthesis, then further to elucidate the metabolic regulation mechanism of γ-PGA and laid the theoretical foundation for the construction of efficient large-scale engineering strain to produce γ-PGA. |
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