Analysis of glutamate-dependent mechanism and optimization of fermentation conditions for polyglutamic acid production by Bacillus subtilis SCP017-03

doi:10.21203/rs.3.rs-4005655/v1

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Analysis of glutamate-dependent mechanism and optimization of fermentation conditions for polyglutamic acid production by Bacillus subtilis SCP017-03

https://doi.org/10.21203/rs.3.rs-4005655/v1

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Poly-gamma-glutamic acid (γ-PGA) is mainly synthesized by glutamate-dependent strains in the manufacturing industry. Therefore, understanding glutamate-dependent mechanisms is imperative. In this study, we first systematically analyzed the response of Bacillus subtilis SCP017-03 to glutamate addition by comparing transcriptomics and protein genomics. The introduction of glutamate substantially altered the gene expression within the central metabolic pathway of cellular carbon. Most genes in the pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and energy-consuming phase of the glycolysis pathway (EMP) were down-regulated, whereas genes in the energy-producing phase of glycolysis and those responsible for γ-PGA synthesis were up-regulated. Based on these findings, the fermentation conditions were optimized and the γ-PGA production was improved by incorporating oxygen carriers. In a batch-fed fermentor with glucose, the γ-PGA yield reached 95.2 g/L, demonstrating its industrial production potential. This study not only elucidated the glutamic acid dependence mechanism of Bacillus subtilis but also identified a promising metabolic target for further enhancing γ-PGA production.

Poly-gamma-glutamic acid

Bacillus subtilis

glutamate-dependent mechanism

transcriptomics and protein omics

fermentation optimization

Poly-gamma-glutamic acid (γ-PGA) is a biopolymer naturally synthesized by microorganisms (Hsueh et al. 2017). It possesses exceptional biochemical characteristics, including water absorption, moisture retention, and biocompatibility. As a novel, environmentally friendly biomaterial (Ashiuchi 2002), it has extensive applications across various fields, including food, cosmetics, agriculture, medicine, and environmental fields (Hu et al. 2023). Therefore, γ-PGA biosynthesis has garnered significant attention. γ-PGA-producing strains can be categorized into two groups: glutamic acid-dependent and glutamic acid-independent. The former requires an external supply of glutamate to generate γ-PGA, whereas the latter can produce γ-PGA without additional glutamate supplementation (Jose et al. 2018). Notably, glutamic acid-dependent strains exhibit higher γ-PGA production than their glutamic acid-independent strains, making them the primary choice for industrial γ-PGA production (Jose et al. 2018).

The microbial γ-PGA synthesis pathway comprises three primary stages: precursor synthesis, polymerization transfer, and catabolism. Key metabolic pathways, such as glycolysis (EMP), pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, play essential roles in γ-PGA biosynthesis (Li et al. 2022). L- glutamate, as a precursor, can be converted into D-glutamate through multiple enzymatic routes (Ko et al. 1998). These L-glutamate and D-glutamate molecules undergo polymerization facilitated by the PgsBCA multi-protein complex to produce γ-PGA, which is subsequently secreted from the cell (Ashiuchi et al. 2001a; Ashiuchi et al. 2001b). This polymerization process requires ATP consumption. The polyglutamic acid synthase (Pgs) is encoded by the pgs operon and encompasses four genes: pgsB, pgsC, pgsA, and pgsE (Shih and Van 2001). In addition, the gene encoding γ-PGA hydrolase (PgdS) is located downstream of the pgs operon. Specifically, PgsB is an ATP-dependent amino ligase that catalyzes γ-PGA formation (Kimura et al. 2009; Tomosho et al. 2008). PgsC is a cell membrane component within the γ-PGA synthesis system, featuring structural resemblance to the N-acetyltransferase domain of N-acetylglutamic acid synthase (Yamanaka et al. 2008). PgsA includes a cell membrane-anchoring region (Ashiuchi 2002) that may have implications for γ-PGA synthesis or transport (Nordlund and Eklund 1995; Rusnak and Mertz 2000).

The regulatory mechanisms governing γ-PGA biosynthesis remain incompletely understood, with limited research in this area. Zeng et al. compared the genomic differences between glutamate-dependent and glutamate-independent strains, indicating 13 genes related to γ-PGA biosynthesis are mutated in glutamate-dependent strains. However, the relationship between these mutations and glutamate dependence has not yet been conclusively established (Zeng et al. 2017). Sha et al. explored the dependence mechanism of Bacillus subtilis NX-2 on glutamic acid during γ-PGA production through transcriptome analysis. Their findings revealed that the addition of glutamate significantly up-regulated genes associated with glycolysis, PPP, TCA cycle, glutamic acid synthesis, and γ-PGA synthesis. Overexpression of these genes caused the accumulation of γ-PGA, demonstrating the pivotal role of intracellular glutamic acid synthesis in regulating γ-PGA production in glutamate-dependent strains (Sha et al. 2000). Li et al. preliminarily investigated the co-production mechanism of γ-PGA and nattokinase by transcriptome technology. Their study revealed the up-regulation of gene expression related to carbohydrate metabolism. Furthermore, by analyzing major metabolic pathways, such as carbohydrate metabolism, potential target genes for enhancing γ-PGA production were observed (Li et al. 2021).

Previous studies have shown that γ-PGA biosynthesis is regulated by two intracellular signal transduction mechanisms. ComA is a DNA-binding transcription factor and is the corresponding regulator of the two-component ComP-ComA system. At high cell densities, the cell density signal of ComX is transferred from phosphorylated ComP to phosphorylated ComA. This event induces the expression of DegQ, which transfers the cell density signal to the DegS-DegU binary system. This can promote DegU phosphorylation, and the combination of phosphorylated DegU to the pgsBCA gene promoter activates its expression (Msadek et al. 1991; Dubnau et al. 1993; Tran et al. 2000). Phosphorylated DegU directly engages the pgsBCA promoter, thereby activating gene expression. Consequently, knockout of DegU and SwrA results in the loss of γ-PGA synthesis capability (Ohsawa et al. 2009; Stanley et al. 2005). SwrA and DegU facilitate the expression of the pgsBCA operon through protein-protein interactions, serving as key factors in activating its expression. However, the relationship between these regulatory factors and the addition of glutamate remains unclear (Ogura and Tsukahara 2012).

To elucidate the regulatory mechanisms governing γ-PGA biosynthesis, this study focused on the glutamate-dependent strain, B. subtilis SCP017-03. The effects of exogenous glutamate supplementation on γ-PGA production and cell growth were investigated. Moreover, a comparison and analysis of the differential expression of bacteria at the protein and transcription levels in the presence and absence of glutamate were conducted using transcriptome and proteomics technologies. This analysis focused on the differential expression observed in glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, glutamic acid metabolism, and γ-PGA synthesis pathway resulting from the addition of glutamate. Based on these findings, fermentation conditions were optimized, with a significant improvement in the yield of γ-PGA. This study can provide a profound and systematic understanding of the molecular mechanisms underlying glutamate dependence in various γ-PGA strains. Furthermore, this study can offer a valuable theoretical foundation for future gene modification endeavors aimed at achieving high-yield γ-PGA production.

Strain and media

Bacillus subtilis SCP017-03 (CGMCC 60083) was isolated and preserved in our laboratory. A single colony of SCP017-03 was cultivated in LB liquid medium and cultured overnight at 37℃. The resulting

culture was subsequently transferred to a triangular flask containing a fermentation medium with the following composition: glucose 60.0 g/L, yeast powder 12.0 g/L, sodium glutamate 50.0 g/L, KH₂PO₄ 0.5 g/L, K₂HPO₄ 0.5 g/L, and MgSO₄ 0.1 g/L. The initial fermentation conditions were as follows: initial pH of 7.0, culture temperature of 37℃, agitation at 220 rpm, culture medium containing 10% liquid content, inoculation amount of 4%, and fermentation period of 72 h.

Determination of related indexes in fermentation process

Glutamate content in the fermentation broth was quantified using an enzyme-based method employing a biosensor (SBA-40c, Shan-Dong Academy of Sciences, China). Determination of the total sugar content in the fermentation broth involved diluting the broth to appropriate concentrations and employing the sulfuric acid-acetone method (Cuesta et al. 2003). The purification and concentration determination of γ-PGA referred to the method proposed by Chang (Chang et al. 2022). The steps comprised 5 mL of the fermentation broth, adjusted to pH 3 with 50% trichloroacetic acid, centrifugation at 10,000 rpm for 10 min to eliminate bacteria, collection of the supernatant, restoration of the pH to 7, addition of fourfold anhydrous ethanol, and precipitation overnight at 4℃. The precipitate was collected by centrifugation at 10000 r/min, and the 10,000 rpm, resulting in a crude γ-PGA product that was further obtained by freeze-drying. Subsequently, the crude γ-PGA product was dissolved in 5 mL of ddH₂O, and its absorbance at 400 nm was measured using the CTAB method. Finally, the molecular weight of γ-PGA was determined by SDS-PAGE (Yamaguchi et al. 1996).

Transcriptome analysis

Fresh seed liquid of strain SCP017-03 was inoculated into two fermentation media: one without glutamate and the other with glutamate. Cultures were maintained at 37℃ and agitated at 220 rpm. After 20 h of fermentation, bacterial cells were collected by centrifugation and subsequently sent to Beijing Nuohe Zhiyuan Technology Co., Ltd. for Qualcomm RNA sequencing (RNA-Seq). A cDNA library was successfully constructed and sequenced using the Illumina NovaSeq 6000 high-throughput sequencing platform. The sequence data was deposited to the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) with the accession number PRJNA 1057989. Quality control and data filtering were performed using FastQC software (Version 0.11.5). Low-quality RNA-Seq reads (reads with Qpred ≤ 20, representing more than 50% of the total reads) were removed, and the filtered reads were subjected to genome mapping analysis using Bowtie 2 software (Langmead and Salzberg 2012) against the B. subtilis subsp. natto Best 195 for genome mapping analysis. The transcript assembly and quantification were performed using featureCounts in the subread software. The relative quantitative value of transcripts was measured using fragments per kilobase of transcript per million mapped reads (FPKM), with transcripts having an FPKM value ≥ 1 considered effectively expressed. From the RNA-seq results, certain genes related to γ-PGA synthesis were selected for validation using qRT-PCR. The 16S rRNA internal reference gene served as the normalization standard, and the specific primers used are listed in Table S1. Three replicates were performed for each sample.

Proteomic analysis

The fermentation broth collected at the 6th and 20th hour underwent centrifugation and was subsequently sent to Beijing Nuohe Zhiyuan Technology Co., Ltd. for further processing. Data are available via ProteomeXchange with identifier PXD049242. This process included protein quality assessment, enzyme digestion, desalting, iTRAQ labeling, chromatographic separation, and mass spectrometry analysis. The mass spectrometry data were matched against the B. subtilis168 protein database from NCBI, and the retrieval results were filtered using MaxQuant software. This filtering process selected spectral peptides with a reliability exceeding 99%, while peptides and proteins with an FDR exceeding 0.01 were eliminated. Proteins showing up-regulated expression were identified when FC > 2.0, and PALUE < 0.05. Conversely, down-regulated proteins were identified when FC < 0.50 and PALUE < 0.05.

Effects of glutamate addition on cell growth and γ-PGA synthesis

To assess the effect of glutamate addition on fermentation, B. subtilis SCP017-03 was cultured under two conditions: one with glutamate addition (experimental group: GA) and another without glutamate addition (control group: CK). The differences in glucose and glutamate consumption, cell growth, and γ-PGA yields were compared. As depicted in Fig. 1a, the results revealed no significant differences in cell growth or carbon source consumption between both groups during the initial 0–4 h of fermentation. However, after 10 h, cell growth in the GA group lagged significantly behind that in the CK group. Between 18 and 48 h of fermentation, the differences in cell growth and glucose consumption between the experimental and control groups were more pronounced. At the 36-hour point, the OD₆₀₀ value difference between the two groups reached the maximum, with CK in the control group at 6.4, while GA in the experimental group measured 4.8. After 12 h, glucose consumption in the GA group slowed, with the glucose content stabilizing at 25 g/L after 72-hour fermentation. However, the control group exhibited faster sugar consumption, exhausting the glucose supply after 60th hour of fermentation. Sugar consumption correlated precisely with differences in cell growth. Furthermore, the GA group initiated γ-PGA synthesis after 8 h of fermentation and continued to accumulate. After 18 h, the γ-PGA content reached 16.7 g/L, peaking at 36.5 g/L after 48 h of fermentation, remaining relatively stable until the end of fermentation. The residual concentration of glutamate in the fermentation broth of the GA group gradually decreased from 50 g/L to 35 g/L after 60 h of fermentation, indicating a modest reduction of 15 g/L over the course of fermentation. This observation suggested that the bacterial cells of this strain synthesized a certain amount of glutamate.

The γ-PGA generated in the fermentation broth was assessed using SDS-PAGE (Fig. 1b). The figure illustrates that the molecular weight of γ-PGA produced by this strain significantly exceeded 200 kD. However, as fermentation progressed, there was a slight reduction in molecular weight.

Transcriptome analysis of strain scp017-03 in response to glutamate addition

The RNA samples from each group were subjected to sequencing using an Illumina NovaSeq 6000 high-throughput sequencing platform. The sequencing output data exhibited base quality values of Q30 and Q20 exceeding 95% and 98%, respectively, with a sample sequencing error rate of less than 0.03%. These metrics complied with stringent quality standards for Illumina sequencing. Table 1 shows the alignment results for each sample against the reference genome. All six samples achieved a total mapping rate, were successfully aligned to the genome via sequence mapping, and each exhibited unique alignment positions on the reference sequence.

Table 1

Mapping results of strain SCP017-0 transcriptome
Sample.name	CK1	CK2	CK3	GA1	GA2	GA3
Total reads	8754356	7523564	7800772	7724204	7734902	7438034
Total mapped reads	8621421	7418727	7684771	7636601	7638796	7325040
Uniquely mapped reads	8425126	7275289	7526589	7500556	7495239	7168957
Multiple mapped reads	196295	143438	158182	136045	143557	156083
Total mapping rate	98.48%	98.61%	98.51%	98.87%	98.76%	98.48%
Uniquely mapping rate	96.24%	96.7%	96.49%	97.1%	96.9%	96.38%
Multiple mapping rate	2.24%	1.91%	2.03%	1.76%	1.86%	2.1%

Statistical analysis of differentially expressed genes was conducted using the following screening criteria: log2 (FoldChange) > 0 or padj < 0.05. A total of 1867 differentially expressed genes were identified, comprising 862 up-regulated and 1005 down-regulated genes. Subsequently, ClusterProfiler software was utilized to perform GO function enrichment analysis and KEGG pathway enrichment analysis on the sets of differential genes, with a significance threshold set at padj < 0.05. GO function enrichment analysis indicated the presence of both up-regulated and down-regulated genes within three categories: biological process (BP), cellular component (CC), and molecular function (MF) (Fig. 2). These findings suggested that the addition of exogenous glutamic acid significantly affected various metabolic pathways in this strain. Furthermore, the enrichment results of the KEGG pathway analysis encompassed the differentially expressed genes and certain genes related to γ-PGA synthesis (Fig. 3).

The phosphotransferase system (PTS) is the primary mechanism employed by bacteria to uptake hexose, hexitol, disaccharides, and other carbohydrates. As shown in Fig. 3, the addition of exogenous glutamate led to the differential expression of ybbF and sacX genes within the sucrose metabolism system (EIIBCA or EIIBC component). Their log₂FC values were − 2.03 and + 2.65, respectively. The carbohydrates transported into the membrane entered not only the central metabolic pathway but also the polysaccharide synthesis (EPS). Within the EMP pathway consisting of energy consumption and productivity stages, gene expression in the energy consumption stage, including pgi (glucose-6-phosphate isomerase), glpX (fructose 1,6-diphosphatase), and pfkA (ATP-dependent phosphofructokinase), was slightly down-regulated. Conversely, during the production stage of Pyr synthesis from G-3-P, the expression of these five genes was significantly up-regulated. These genes include fbaA (fructose-diphosphate aldolase), pgm (glycerophosphate mutase), eno (enolase), gapA, and gapB (glyceraldehyde-3-phosphate dehydrogenase). Consequently, the addition of exogenous glutamate enhanced the EMP pathway. In the PPP pathway, the expression of zwf (glucose-6-phosphate dehydrogenase) and ykgB (glucose-6-phosphate esterase) was down-regulated by factors of 1.07 and 2.03, respectively, while ganZ (6-glucose-6-phosphate dehydrogenase) witnessed an up-regulation in expression.

The TCA cycle is initiated by the condensation of acetyl-CoA and oxaloacetic acid to produce citric acid. Acetyl-CoA is formed through the oxidative decarboxylation of pyruvate under aerobic conditions. Figure 4 illustrates that most genes within the TCA cycle pathway were down-regulated in the GA group. Notably, the log₂FC values for citZ and icd were − 1.76 and − 0.72, respectively, with only the sdhABC gene (encoding succinate dehydrogenase) displaying up-regulation. In addition, the coding genes responsible for the pyruvate dehydrogenase complex, which catalyzed the decarboxylation of pyruvate to synthesize acetyl coenzyme A, were down-regulated. For example, the acoC gene (encoding dihydrooctylamide transacetylase) exhibited a down-regulation of 4.69 times, while the idh gene (encoding lactate dehydrogenase), responsible for transforming pyruvate into lactic acid, displayed an up-regulation of 2.74 times. These findings suggested that the synthesis of high-viscosity γ-PGA by cells resulted in reduced dissolved oxygen levels in the culture solution, thereby reducing the flow of substances into the tricarboxylic acid pathway.

L-glutamic acid was converted into D-glutamic acid through the action of RacE (glutamate racemase), and these compounds were subsequently utilized in the synthesis of γ-PGA by γ-PGA synthetase. Figure 3 demonstrates an up-regulation in the expression of the glnA gene (encoding glutamine synthetase) and racE, with log₂FC values of 0.86 and 1.77, respectively. This upregulation enhanced the production of the precursor D-glutamic acid for γ-PGA, thereby strengthening γ-PGA synthesis. Notably, the pgsBCA gene cluster (encoding γ-PGA synthetase) was significantly up-regulated, with log₂FC values of 2.63, 2.83, and 2.78, respectively. The pgsBCA operon was regulated by DegS-DegU and ComA-ComP two-component systems. In the CK group without γ-PGA synthesis, the FPKM value of the degU gene was 2396, and the FPKM values of the pgsBCA gene cluster were 859, 744, and 1243, indicating baseline expression of the γ-PGA synthesis system in the absence of exogenous glutamate. However, the pgsBCA operon was further activated upon addition of exogenous glutamate, with the FPKM value of the pgsA gene reaching 8533. These two-component systems can interact with phosphorylated ComA to induce degQ expression. Nevertheless, the expression levels of comA, comX, comQ, and degQ genes in this strain remained low. For example, the FPKM value of degQ in the GA group was only 69. After γ-PGA synthesis, the cells required the action of γ-PGA hydrolase to release γ-PGA. This process may involve three genes, pgdS, cwlO, and ggt. Transcriptome analysis revealed that these three genes exhibited low expression in the GA group, with FPKM values of 27, 282, and 266, respectively. Furthermore, the expression of ggt in the CK group was significantly higher than that in the GA group.

qPCR analysis of genes related to γ-PGA biosynthesis

Selected genes related to γ-PGA biosynthesis were subjected to qPCR analysis to validate the findings of transcriptome analysis. To investigate dynamic changes in key genes and regulatory factors during fermentation, samples were collected for qPCR analysis at three time points: 10 h (immediately following γ-PGA synthesis initiation), 20 h, and 48 h (when γ-PGA accumulation peaked). The results are presented in Table 2. After 10-hour fermentation, most genes in the TCA cycle, PPP pathway, and glutamic acid synthesis pathway exhibited slight down-regulation, except for a few genes such as citA, gltP, and rocA. In the γ-PGA synthesis module, only the expression of the regulator DegQ showed a 0.67-fold decrease, and the genes encoding γ-PGA synthetase were significantly up-regulated. Specifically, pgsB expression increased by 4.94 times, and pgsA expression increased by 4.63 times. Moreover, the genes pgdS and cwlO, associated with γ-PGA hydrolysis, were up-regulated, with fold changes of 2.73 and 1.47, respectively. At the 20-hour point, genes in the central metabolic pathway generally showed down-regulation, except for the eno gene in the EMP pathway and the sdhA gene in the TCA cycle, consistent with the transcriptome analysis results. Notably, pgdS exhibited slight downregulation. In addition, the γ-PGA synthesis module consistently displayed up-regulated expression. In the late stage of fermentation (48 h), owing to significant nutrient depletion in the culture medium, gene expression in the central pathway decreased. Concurrently, as γ-PGA accumulation peaked, some γ-PGA was hydrolyzed by bacterial cells to maintain its state, as evidenced by the substantial upregulation of cwlO gene expression, with a fold change of 21.93. Throughout the fermentation process, the regulatory factor degQ was consistently down-regulated, whereas degS and degU were up-regulated, indicating complex interactions within the DegS-DegU two-component system.

Table 2

Comparison of expression of key genes in γ-PGA synthesis by qPCR of strain *SCP017-03* grown with glutamate and without glutamate at different fermentation times
Gene	10h	20h	48h
pfkA	4.30	0.60	2.18
pgi	2.93	0.75	1.63
eno	6.30	2.97	2.18
pdhA	2.52	0.05	0.11
citB	1.87	1.99	0.54
icd	1.50	0.40	0.53
fumC	2.69	0.44	0.43
ykgB	1.06	0.23	0.22
zwf	1.97	0.88	3.34
ganZ	2.80	0.47	0.20
gltP	0.67	0.16	0.04
rocA	0.20	2.86	5.96
racE	4.42	0.35	0.01
degQ	0.67	0.32	0.05
degU	1.36	1.02	1.41
degS	7.59	2.03	1.13
pgsA	4.63	2.45	18.83
pgsB	4.94	4.82	21.26
pgsC	3.23	9.38	12.21
glnA	0.92	1.64	0.94
putM	1.55	0.03	0.06
pgdS	2.73	0.76	1.71
comA	2.45	0.58	6.70
cwlO	1.47	4.89	21.93
Notes: The numbers in the table represent gene expression levels, which have been normalized to the 16S rRNA internal reference gene.

Proteomic analysis of strain SCP017-03 in response to glutamic acid addition

Strain SCP017-03 was subjected to fermentation in culture media with or without exogenous glutamate (GA group). Cell samples were collected at 6 and 20 h after fermentation. Protein analysis was conducted using 4D-label-free technology, leading to the identification of 2,161 proteins and 13,243 peptides. Each sample (CK6h, GA6h, CK20h, and GA20h) was then quantified. Differentially expressed proteins were identified based on criteria where the FC > 2.0 or < 0.5 and Pvalue < 0.05, with a significance level of P < 0.05. After 6 h of fermentation, a comparison between the GA and CK groups (Fig. 4a) revealed 328 differentially expressed proteins. These included 162 up-regulated and 166 down-regulated proteins. After 20 h of fermentation (Fig. 4b), 279 differentially expressed proteins were identified, comprising 142 up-regulated upregulated and 137 down-regulated proteins. A comparison within the GA group, specifically between 20 and 6 h of fermentation (Fig. 4c), yielded 297 differentially expressed proteins. Among them, 158 were up-regulated and 139 were down-regulated. Similarly, 296 differentially expressed proteins were observed in the CK group (Fig. 4d), with 162 up-regulated and 136 down-regulated proteins. The volcano plot of the differential proteins visually illustrated that the majority of proteins (represented in black) exhibited no significant differences. Proteins with differences in expression are denoted in red (up-regulated) and green(down-regulated) (Fig. 4).

The enrichment category of differential proteins GO in the GA group exhibited significant changes between the 6-hour and 20-hour fermentation periods, whereas the corresponding changes in the CK group were relatively minor (Fig. 5a and 5b). Compared to differentially expressed proteins associated with BP in the CK group, those in the GA group at 6 h of fermentation were primarily involved in processes related to localization (25 proteins), transportation (21 proteins), and coenzyme biosynthesis (6 proteins). Furthermore, differentially expressed proteins linked to “cell composition (CC)” were predominantly associated with specific cellular structures. As the fermentation duration reached 20 h, the BP processes in the GA group emphasized transmembrane transport (13 proteins), transport (23 proteins), and localization (24 proteins), while the CC processes centered on membrane-related components (28 proteins) and the ABC transport complex (2 proteins). In the MF category, the focus shifted towards hydrolase activity (four proteins). Similarly, the analysis of GO enrichment for differential proteins in both the GA and CK groups (Fig. 5c and 5d) revealed that the differentially expressed proteins in the GA group were primarily concentrated in the BP (22 localized proteins) and CC (30 membrane-related proteins) categories, whereas in the CK group, the emphasis was mainly on the CC (28 membrane-related proteins) and MF (38 proteins associated with hydrolase activities) categories.

Table 2 illustrates that after 6 h of fermentation, the protein expression within the central metabolic pathways of the GA group exhibited slight changes compared to the CK group. For example, in the EMP pathway, with the exception of GlpX (fructose 1,6-diphosphatase), which showed a modest 0.31-fold up-regulation, other proteins displayed down-regulated expression. Notably, Pgi, Fbp, and Eno had log2FC values of -0.58, -0.93, and − 0.82, respectively. In the TCA cycle, only PdhB (β subunit of pyruvate dehydrogenase E1 component) and PDHC (α subunit of DNA polymerase III) exhibited slight increases in protein expression, with log₂FC values of 0.47 and 0.59, respectively. Conversely, the expression of other proteins, including SdhA (protein subunit of succinate dehydrogenase), notably decreased, with a log₂FC value of -1.25. Changes in protein expression within the PPP pathway were relatively minimal. After 20-hour fermentation, the log2FC values for certain proteins in these metabolic pathways underwent substantial changes, with most proteins in the EMP pathway showing increased expression. In the TCA cycle, aside from PdhABC, which was up-regulated by 0.71, 1.38, and 1.16, the expression of other proteins aligned with the transcriptome results, indicating down-regulation. Similarly, the expression of PPP pathway proteins was consistent with the results of the transcriptome analysis. Furthermore, this study analyzed protein expression at different stages of fermentation (20 and 6 h) for different treatments. The results revealed that in the GA group, protein expression in the EMP pathway was predominantly up-regulated, whereas the CK group exhibited the opposite trend. However, enzyme expression in the TCA cycle and PPP pathway remained largely consistent between the two groups, with PPP pathway enzymes being down-regulated.

After a 6-hour fermentation period, a comparison between the GA and CK groups revealed notable changes in the expression of proteins involved in the glutamate synthesis pathway (Table 2). Specifically, the expressions of RacE and RocG in the glutamate synthesis pathway were up-regulated in the GA group, with log₂FC values of 1.78 and 0.15, respectively. In contrast, the expression of other proteins within this pathway decreased, with GltB (glutamate synthase domain 3) showing the most significant down-regulation, marked by a log₂FC value of -3.28. However, proteins and regulatory factors related to γ-PGA synthesis exhibited only slight changes, such as the log₂FC values of -0.39 for DegQ, -0.78, DegU, and − 0.19 for PgsA. However, after a 20-hour fermentation period, substantial changes were observed in the expression of histones in both the GA and CK groups. In the glutamic acid synthesis pathway, the log₂FC value of PurQ increased from − 0.29 to 1.89, while RacE exhibited an increase in log₂FC from 1.76 to 4.12. Concurrently, the expression of other proteins in this pathway was also down-regulated. Within the γ-PGA synthesis system, both PgsA and PgsB were up-regulated, with log₂FC values of 1.68 and 3.05, respectively. Notably, the protein expression of DegQ and DegS in the DegS-DegU two-component system diverged from the transcriptome analysis. Specifically, the log₂FC values for regulatory factors DegQ and DegS were 1.9 and − 0.44, respectively. However, the protein expression of the ComP-ComA system aligned with aforementioned transcriptional analysis, with the ComA regulatory factor exhibiting down-regulation. Additionally, there was a general increase in enzymatic activity within the glutamate synthesis pathway. For example, the activity of glutamate racemase (RacE), which was responsible for the transformation of D-glutamic acid and L-glutamic acid, increased by 4.12 times, while the expression of γ-PGA hydrolase (Pgds) decreased by 2.24 times.

Table 2 presents a significant increase in γ-PGA polymerase activity within the GA group after 20 h compared to the 6-hour mark. Notably, the log₂FC values for PgsA and PgsB were 2.04 and 3.83, respectively. Furthermore, it was observed that γ-PGA synthetase activity in the CK group after 20 h of fermentation was lower than that at the 6-hour mark. γ-PGA synthetase activity notably increased by a factor of 2.05. This observation suggested that the expression of the γ-PGA synthetase gene exogenous glutamic acid. However, the translation of this mRNA into protein may have been influenced by other genes or regulatory factors, resulting in the reduced activity of γ-PGA synthetase.

Optimization of fermentation conditions

Preliminary optimizations were conducted for carbon and nitrogen sources in an effort to enhance γ-PGA yield during fermentation. The results of carbon source optimization are presented in Fig. 6a. Among the tested options, glucose as the carbon source yielded the highest γ-PGA production at 38.35 g/L, followed by sucrose (36.5 g/L), starch (33.9 g/L), and xylose (32.6 g/L). Conversely, when glycerol was employed as the carbon source, the γ-PGA yield was the lowest at 22.8 g/L. Figure 6b illustrates that yeast powder was a superior nitrogen source, yielding a γ-PGA production of 40.12 g/L. Other nitrogen sources, including pancreatic peptone, soybean peptone, and bean cake powder, yielded approximately 37.5 g/L γ-PGA. In contrast, beef paste and corn steep liquor, when applied as nitrogen sources, resulted in lower yields of 14.32 g/L and 16.2 g/L, respectively. Consequently, the optimal carbon source was glucose, and the preferred nitrogen source was yeast powder.

The results revealed that during the synthesis of γ-PGA, the transcriptional activity of related genes, including those associated with glycolysis, tricarboxylic acid cycle, and PPP pathway, decreased. This decrease could be related to the increased fermentation broth viscosity and the impact of dissolved oxygen. To optimize fermentation, four organic carriers (w/v: 0.5%) were selected. The organic carriers included n-hexane, n-heptane, n-dodecane, and n-hexadecane. Figure 7a illustrates that when n-hexadecane was introduced at the onset of fermentation, the highest γ-PGA yield was achieved, reaching 42.9 g/L. This was followed by n-hexane and n-heptane, with γ-PGA yields of 38.2 g/L and 36.2 g/L, respectively. In contrast, n-dodecane afforded the lowest γ-PGA yield, only reaching 9.24 g/L. Considering the potential impact of organic matter added at the early stages of fermentation on bacterial growth, the organic carrier was introduced into the fermentation for 24 h. Figure 7b shows that, with the addition of the organic carrier after 24-hour fermentation, the γ-PGA production increased by 9.4 g/L in the n-heptane group and by 17.06 g/L in the n-dodecane group. Among these, the n-heptane group achieved the highest γ-PGA output of 45.6 g/L. Notably, there was no significant difference observed between the n-hexadecane group and the n-hexane group, with respective outputs of 43.3 g/L and 35.9 g/L. Therefore, n-heptane was selected for liquid fermentation for 24 h.

Scale-up experiment of batch feeding in 5L fermentor

In the context of shaking flask conditions, the fermentation duration was insufficient, resulting in a low dissolved oxygen concentration and inadequate carbon source concentration in the later stages. These limitations restricted the successful synthesis of γ-PGA. Therefore, fermentation processes were optimized using a 5 L fermentor. The fermentation parameters were set to 500r/min for agitation, 2 L/min for aeration, and a pH of 7. Glucose was added when the total sugar content decreased below 50 g/L. Figure 8 illustrates the dynamic changes in various fermentation parameters. After 24 h of fermentation, the cell growth (OD₆₀₀) reached 3.195, the total sugar residue was 24.09 g/L, and the γ-PGA yield reached 36.29 g/L. However, glutamate content in the fermentation broth remained relatively stable. At this point, an additional 100 g of glucose was introduced, increasing the total sugar content to 55.82 g/L. By the 32-hour point, the glutamate in the fermentation broth decreased to 33.72 g/L, indicating a consumption of 16.27 g/L. Bacterial cell growth increased to 7.68, prompting the second addition of 100 g glucose. At the 48-hour point, bacterial growth (OD₆₀₀) peaked at 10.82, while the total sugar residue reached 43.67 g/L. To sustain fermentation, 50 g of glucose was added. During the fermentation period from 54 to 96 h, as bacterial cells entered the decay phase and nutrient consumption decreased, the last feeding occurred at 54 h, with 50 g of glucose added. At 82 h of fermentation, the total glucose consumption reached 84.18 g/L, and γ-PGA production peaked at 92.5 g/L, subsequently stabilizing. Towards the end of fermentation, bacterial growth (OD₆₀₀) decreased to approximately 8.0, with total sugar and glutamate levels in the tank measuring 18.3 g/L and 31.6 g/L, respectively.

Currently, industrial production of γ-PGA primarily relies on glutamate-dependent strains, necessitating the addition of exogenous sodium glutamate for γ-PGA synthesis. Therefore, thorough investigation of the mechanisms underlying glutamate dependence is crucial. However, research in this area has been limited. This study compared and analyzed differences in SCP017-03 gene expression at both the transcriptional and protein levels by employing transcriptomics and protein genomics. Different from the findings of Sha et al., where nearly all genes in the three central metabolic pathways were significantly up-regulated, the transcriptome results showed a significant down-regulation in the expression of coding genes in the PPP and TCA cycle pathways. Conversely, most of the coding genes in the EMP pathway were up-regulated. This disparity was attributed to the decreased dissolved oxygen in the culture medium caused by the highly viscous γ-PGA product. This decreased the expression of genes encoding the TCA and PPP pathways. However, most coding genes in the glycolytic pathway were up-regulated, promoting pyruvate accumulation and directing metabolism towards the fermentation product, lactic acid. The significant up-regulation of dehydrogenase-encoding genes upon glutamic acid addition further validated this hypothesis. Building upon this analysis, subsequent experiments in this study enhanced γ-PGA production by improving the dissolved oxygen levels in the culture medium. Interestingly, the pgsBCA gene cluster responsible for γ-PGA synthesis was present in the genome of the control group without exogenous glutamic acid, with FPKM values reaching 859, 744, and 1243, respectively, although γ-PGA synthesis did not occur. This suggested that the lack of sufficient glutamic acid as a precursor within the cells and protein-level regulation may be contributing factors. In contrast, the experimental group supplemented with exogenous glutamate exhibited significant up-regulation in the expression of the pgsBCA gene cluster, with log₂FC values of 2.63, 2.83, and 2.78, respectively, which was consistent with the findings of Sha et al. (Sha et al. 2019a). In summary, the response mechanism to glutamate addition varied among different γ-PGA-producing strains, highlighting the complexity of this phenomenon.

The gene encoding the γ-PGA degrading enzyme pgdS was located downstream of the pgsBACE operon. The PgdS was secreted into the extracellular space, which facilitated γ-PGA degradation. Additionally, previous studies have shown γ-glutamyltranspeptidase (GGT) and DL-endopeptidase (CwlO) (Ashiuchi et al. 2006; Morelli et al. 2014; Mitsui et al. 2011). In this study, observations were conducted in both the control group (CK), lacking glutamate, and experimental group (GA), with glutamate supplementation. The pgdS exhibited minimal expression in both groups, whereas the expression of cwlO remained low, with FPKM values of 59 and 282, respectively. However, the expression level of ggt decreased, with its FPKM dropping from 1231 to 266. Feng et al. conducted individual knockout experiments of the pgdS, ggt, and cwlO genes in glutamate-independent strains. The γ-PGA yield improved only when the cwlO gene was singly knocked out, and further enhancements were observed through simultaneous knockout of the pgdS and ggt genes (Feng et al. 2014). Similarly, Scoffone et al. reported a doubling of γ-PGA yield after double knockout of pgdS and ggt in glutamate-dependent strains (Scoffone et al. 2013). Furthermore, overexpression of pgdS significantly increased γ-PGA production (Sha et al. 2019b).

The proteomic analysis in this study revealed that genes in the energy consumption stage of the glycolytic pathway exhibited a slight down-regulation, whereas genes associated with the energy production stage were up-regulated. Furthermore, the PPP and TCA cycle pathways displayed noticeable down-regulation, aligning perfectly with the metabolic pathway responses observed in transcriptome sequencing following glutamate supplementation. Additionally, dynamic changes in the expression of key genes involved in γ-PGA synthesis were analyzed by qPCR at various fermentation time points. The results indicated that during the initial stage of fermentation (10 h), the genes from all central metabolic and γ-PGA synthesis pathways were up-regulated. As fermentation progressed (20 h and 48 h), the genes associated with γ-PGA synthesis continued to display up-regulation, while those related to central metabolic pathways exhibited a declining trend. In summary, for glutamic acid-dependent bacteria, the addition of glutamate had a dual purpose: activating γ-PGA synthetase expression and providing the ample supply of glutamate necessary for γ-PGA synthesis.

The mechanism underlying the high yield of γ-PGA is complicated and encompasses aspects such as glutamate supply, γ-PGA synthesis, transport, secretion, energy metabolism, and glutamate metabolism. In this study, the qPCR analysis revealed that in the experimental group supplemented with glutamate, the expression of the pgsBCA gene cluster exhibited significant up-regulation throughout the 10th to 48th hours of fermentation, compared to the control group. Moreover, the genes degS and degU displayed substantial up-regulation at the 10th and 20th hours of fermentation, with a slight increase at the 48th hour, while degQ showed down-regulation. Transcriptome analysis indicated that the coding genes comX, comA, and swrA were expressed at low levels during the 20th hour of fermentation and were slightly down-regulated after glutamate addition. In parallel, proteomic analysis demonstrated a significant up-regulation of comP expression at the sixth hour of fermentation. Moreover, the quorum sensing system (QS system), which was known to influence γ-PGA synthesis by primarily affecting comA expression and indirectly regulating γ-PGA synthesis, has been considered (Omer et al. 2015; Hu et al. 2022). The study findings indicated significant down-regulation of the coding genes for Spo0K permease (oppABCDF) and phosphatase RapC (rapHCFK), while the expression of comA was slightly down-regulated. This suggested that comA may be regulated by other regulatory factors or genes.

Based on the comprehensive analysis of strain SCP017-03 at the physiological, protein, and transcription levels, along with the optimization of fermentation medium composition and enhanced dissolved oxygen conditions, several key findings emerged. Glucose and yeast extract were identified as the optimal carbon and nitrogen sources, respectively, increasing the yield of γ-PGA from 36.5 g/L to 40.12 g/L. However, the accumulation of the high-viscosity substance γ-PGA within the fermentation broth led to reduced aeration and inadequate dissolved oxygen levels, negatively affecting both cell growth and γ-PGA production. Researchers have proposed various strategies to address this challenge. For instance, Su et al. integrated the vgb gene (encoding hemoglobin) into the genome of a γ-PGA-producing strain (Su et al. 2010) to augment dissolved oxygen levels. Xu et al. successfully elevated γ-PGA yield to 39.4 g/L by introducing four different organic oxygen carriers into the fermentation medium (Zhang et al. 2012). Feng et al. proposed an aerobic plant cellulose experimental bed to enhance γ-PGA production (Feng et al. 2016). In this study, we focused on promptly mitigating dissolved oxygen deficiency in fermentation cultures and enhancing γ-PGA yield through the optimization of oxygen carriers. The results demonstrated a significant increase in γ-PGA production by introducing n-heptane during a 24-hour fermentation period, elevating the yield from 40.12 g/L to 44.3 g/L. Furthermore, in a 5 L fermentor, the yield reached an impressive 95.2 g/L after batch feeding with sucrose.

In summary, this study presented a pioneering integration of transcriptome and proteome analyses, revealing the glutamate-dependent mechanism underlying γ-PGA synthesis in B. subtilis. Through a comparative analysis of strains cultured with and without glutamate, key genes involved in glycolysis, PPP, TCA cycle, and γ-PGA synthesis pathway were identified. This exploration revealed potential factors and metabolic pathways crucial to γ-PGA production, thereby providing an initial framework for understanding the production mechanism of γ-PGA. Subsequent research focused on fermentation optimization, resulting in a remarkable output of 95.2 g/L with the introduction of an oxygen carrier, demonstrating promising prospects for industrial-scale production. The findings of this study offer a valuable metabolic target for further enhancement of γ-PGA yield, with substantial significance for the large-scale production of targeted metabolites.

Author contributions

Caiyun Wu, Fanglan Ge and Wei Li conceived and designed research. Caiyun Wu conducted experiments. Jiao Li, Yao Ren, Yutao Gou and Shuai Jing checked the data. Caiyun Wu wrote the manuscript. All authors read and approved the manuscript.

Funding

This study was funded by Sichuan Province Science and Technology Support Program (2022NSFSC1720), and Chengdu Science and Technology Program (2022-YF05-00631-SN).

Availability of data and materials

The data generated or analyzed during this study are included in this published article and its Additional file.

Declarations of Competing interests

The authors declare no competing interests.

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Analysis of glutamate-dependent mechanism and optimization of fermentation conditions for polyglutamic acid production by Bacillus subtilis SCP017-03

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Abstract

Figures

Introduction

Materials and Methods

Strain and media

Determination of related indexes in fermentation process

Transcriptome analysis

Proteomic analysis

Results

Effects of glutamate addition on cell growth and γ-PGA synthesis

Transcriptome analysis of strain scp017-03 in response to glutamate addition

qPCR analysis of genes related to γ-PGA biosynthesis

Proteomic analysis of strain SCP017-03 in response to glutamic acid addition

Optimization of fermentation conditions

Scale-up experiment of batch feeding in 5L fermentor

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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