Streptococcus bovis S1 used in this study was previously isolated from the rumen fluid of Saanen goats in our laboratory (Chen et al., 2016b). The ccpA mutant of S. bovis S1 was constructed by homologous recombination in this experiment. Before inoculation, both the S. bovis S1 and its ccpA mutant were revived in a modified de Man, Rogosa and Sharpe (MRS) medium (Chen et al., 2016b) in an anaerobic workstation (DG250, Don Whitley Scientific, England) at 37°C. These cultures (at exponential phase) were transferred using 1% (v/v) inoculum into 200 mL anaerobic serum bottles containing 100 mL basal medium, respectively. The basal medium was prepared according to the methods described in Chen et al. (2016b); and it contained: 0.45 g/L KH₂PO₄, 0.9 g/L NaCl, 0.9 g/L (NH₄)₂SO₄, 0.12 g/L CaCl₂⋅2H₂O, 0.19 g/L MgSO₄⋅7H₂O, 1.0 g/L tryptone, 1.0 g/L yeast extract, and 0.6 g/L cysteine hydrochloride. Glucose solutions were filter-sterilized and added to the sterile basal medium at a final concentration of 5 or 50 mM. After inoculation, the culture bottles were sealed and transferred to a thermostat shaker (TS-1102C, Bosheng Scientific Instrument Co., Ltd., Yangzhou, China) and grown at 37°C and 160 rpm. The pH of the medium was constantly maintained at 6.5 by continuous titration with 10% NaOH. Three replicates were set for each treatment.

The ccpA gene was disrupted by homologous recombination as follows. First, DNA fragments corresponding to the upstream (1053 bp fragment; primer pairs ccpA Up F/ccpA Up R, Supplementary Table 2) and downstream (1101 bp fragment; primer pairs ccpA Down F/ccpA Down R, Supplementary Table 2) sequences of ccpA were amplified by PCR using S. bovis S1 genomic DNA as a template. The erythromycin resistance gene erm was amplified with the primers erm F and erm R (Supplementary Table 2). The PCR product was purified using a PCR purification kit (Qiagen, Beijing, China) according to the manufacturer’s instructions. The amplified fragments were, respectively, cloned into EcoRI, BamHI, and SacI restriction sites of pUC19 vector to generate pUC19-ccpA up-erm-ccpA down (pCE). The recombinant vector pCE was electroporated into S. bovis S1 cells using an electroporation system at 2.5 kV, 200 Ω, and 25 μF. Finally, knockout mutants were selected on MRS plates containing 1 μg/mL erythromycin at 37°C for 3–4 days. The results of knockout were validated by qRT-PCR and DNA sequencing as previously described (Chen et al., 2015; Supplementary Figure 1).

Cell growth was monitored by measuring OD values at 600 nm using SpectraMax M5 plate reader (Molecular Devices Corporation, United States) at 1-h intervals, and the maximal growth rate (μ_max) was estimated according to the logistic model (Perni et al., 2005; Dai et al., 2020). The cultures of each sample were harvested by centrifugation (12,000 rpm, 2 min, 4°C) when they reached exponential phase (at OD₆₀₀ of 0.6 for the wild-type and ccpA mutant strains grown in glucose-excess condition, at OD₆₀₀ of 0.2 for both strains grown in glucose-limited condition) and stationary phase (at OD₆₀₀ of 0.9 for both strains grown in glucose-excess condition, at OD₆₀₀ of 0.3 for both strains grown in glucose-limited condition). The cell pellets obtained at exponential phase were quickly frozen in liquid nitrogen for 15 min, and stored at −80°C for further RNA isolation; the supernatants at both growth phases were filtered using a 0.22 μm filter membrane and stored at −80°C for the determination of metabolites.

A high-performance liquid chromatographer (HPLC, Shimadzu, Japan) equipped with an acclaim OA column (Sepax Carbomix H-NP) and a UV detector was used to detect concentrations of organic acids (lactate, formate, and acetate) in the supernatant. The column temperature was maintained at 55°C; the mobile phase was 2.5 mM H₂SO₄, and its flow rate was set at 0.5 mL/min. Organic acids were then measured with a UV detector set at 210 nm. The concentration of FBP was determined using a commercial kit (Comin Biotechnology Co., Ltd., Suzhou, China), according to the manufacturer’s instructions.

Total RNA from S. bovis S1 wild-type and mutant strains, grown in the different conditions were extracted using TRIzol reagent (Invitrogen, Shanghai, China) according to the manufacturer’s instructions. The quality and integrity of total RNA were determined using NanoDrop spectrophotometer (Thermo Scientific, United States) and Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, United States). Total RNA was purified using Zymo-Seq RiboFree Total RNA Library Kit. The first-strand cDNA was synthesized using random oligonucleotides and SuperScript III and the second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends by exonuclease/polymerase activities and the enzymes were removed. After adenylation of the 3′ ends of the DNA fragments, Illumina PE adapter oligonucleotides were ligated for subsequent hybridization. To select cDNA fragments of the preferred length (400–500 bp), the library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, CA, United States). DNA fragments with ligated adaptor molecules on both ends were selectively enriched using Illumina PCR Primer Cocktail in a 15-cycle PCR reaction. Products were purified (AMPure XP system) and quantified using the Agilent high sensitivity DNA assay on a Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, United States). The library obtained was sequenced on NovaSeq 6000 platform (Illumina) by Shanghai Personal Biotechnology Co., Ltd.

The raw data was filtered using Cutadapt (v1.15) software to obtain high-quality data, free of adapter sequences, primers, poly-A tails and unwanted artifacts from the high-throughput sequencing reads. The filtered reads were mapped onto the reference genome of Streptococcus equinus S1, using Bowtie2.¹ The gene read count value was calculated using HTSeq (v0.9.1) as the original expression level of the gene. To normalize the gene expression levels of different genes and different samples, fragments per kilobase of exon per million fragments mapped (FPKM) was used. DESeq (v1.30.0) was used to analyze the differentially expressed mRNA transcripts with | log₂FoldChange | > 1. P-value < 0.05 was considered as a statistically significant differential expression.

Primers for qRT-PCR were designed using Beacon Designer 7.0 software. Total RNA was isolated using the method described above, and reverse-transcribed to cDNA using Quant reverse transcriptase (Tiangen, Biotech Co., Ltd., China), as per the manufacturer’s instructions. The qPCR was performed on the ABI Step-One-Plus RT-PCR system (ABI 7500, Applied Biosystems, Foster City, CA) using the TB Green Premix Ex Taq™ II Kit (TaKaRa Biotechnology Co., Ltd., Dalian, China). A 20 μL reaction was set; reaction solution contained 10 μL 2 × TB Green Premix Ex Taq II, 1.6 μL primer, 1 μL cDNA, 0.4 μL 50 × ROX, and 7.0 μL Rnase-free water. The RT-qPCR conditions were as follows: 95°C for 30 s, followed by 40 cycles of the amplification at 95°C for 5 s and 60°C for 34 s. All samples were evaluated in triplicates. Relative gene expression was normalized with the expression of the 16S rRNA gene and calculated using the 2^–ΔΔCT method.

Results were expressed as “mean ± SD (standard deviation).” All data were analyzed with SPSS 25.0 (IBM, United States) and plotted using GraphPad Prism 8. Statistical significances were evaluated with the Student’s t-test (unpaired Student’s t-test, P < 0.05).

The growth curves for S. bovis S1 wild strain and the ccpA deletion strain under glucose-excess or -limited conditions are shown in Figure 1. In the stationary phase, as expected, the optical density (OD₆₀₀) values of both strains in glucose-excess were significantly higher than those in glucose-limited conditions. Moreover, the growth yield of the two strains in same glucose levels exhibited no differences in the stationary phase. However, the inactivation of ccpA significantly reduced the maximal growth rate (μ_max) of bacteria in the exponential phase (P < 0.05) in both glucose-excess (0.37 ± 0.002 h^–1 versus 0.23 ± 0.007 h^–1, respectively) and glucose-limited conditions (0.29 ± 0.008 h^–1 versus 0.18 ± 0.008 h^–1, respectively); μ_max of both the strains grown in glucose-excess were significantly higher than those in glucose-limited conditions (P < 0.05). These data suggested that the growth of S. bovis S1 was dependent on both CcpA and glucose availability.

Table 1 shows the organic acid production characteristics of S. bovis S1 wild-type and ccpA deletion strains in glucose-excess or -limited conditions. In the exponential growth phase, the lactate yield and percentage of S. bovis S1 wild-type strain were significantly higher than those of ccpA deletion strain in glucose-excess condition (P < 0.05); corresponding values for formate and acetate of wild-type strain were lower than those of the mutant strain (P < 0.05). Similar results were observed for organic acid production in the low-glucose conditions. Indeed, the yields of organic acids for both strains were greater in the high-glucose condition, however, the percentages of formate and acetate were much higher in glucose-limited culture. The observations in the stationary phase were similar to those in the exponential growth phase, except for the difference in acetate yield and percentage between both strains grown in low-glucose condition, which were statistically non-significant. As shown in Figure 2, the FBP concentration in S. bovis S1 wild-type strain was significantly higher than that of ccpA deletion strain in both the glucose-excess and glucose-limited conditions (P < 0.05); the concentrations of FBP for both strains were significantly higher in high-glucose conditions (P < 0.05).

To further investigate the global transcriptional regulation of CcpA in S. bovis S1 at different glucose concentrations, the transcriptome of S. bovis S1 wild-type and ccpA mutant were sequenced in glucose-excess or -limited conditions. RNA-seq was performed in the exponential growth phase of the bacterium. The statistical data for the transcriptome analysis is presented in Supplementary Table 1. A total of 364 million raw reads were generated from all samples. After the removal of low-quality reads, about 334 million clean reads with an average read length of 150 bp were obtained and used for subsequent analysis. Of these, 316 million were mapped onto the annotated S. equinus S1 genome with an average mapping ratio of at least 94.9%; the sequence reads matched with all the 1802 coding genes in the S. equinus S1 genome, which indicated that the sequencing depth was sufficient to cover all the transcripts in the cells (Supplementary Figure 2).

Based on the annotation of the S. equinus S1 genome, the read counts for each gene were calculated using HTSeq 0.6.1p2. The gene expression levels were normalized for different samples using FPKM. The gene expression profile of all samples was evaluated using principal component analysis (PCA). As expected, the three biological replicates cluster together closely for each treatment condition (Supplementary Figure 3). Differentially expressed genes (DEGs) were identified using the DESeq software with an absolute value of log₂FoldChange > 1 and the false discovery rate (FDR) <0.05. DEGs between S. bovis S1 wild-type and its ccpA mutant in glucose-excess or -limited conditions were identified by four pair-wise comparisons of the overall transcriptome profiles as follows: between wild-type strains grown in glucose-excess and -limited conditions; between ccpA mutants grown in glucose-excess and -limited conditions; between wild-type and its ccpA mutant in glucose-excess conditions; between wild-type and its ccpA mutant in glucose-limited conditions (Supplementary Figure 4). Global gene expression patterns were visualized by volcano plots (Supplementary Figure 5). To better understand the molecular mechanisms of CcpA-regulated phenotypic differences of S. bovis S1 at different glucose concentrations, an enrichment analysis for the differential expressed genes was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Figure 3 and Supplementary Table 3).

To validate the RNA-seq results, we randomly selected 10 genes and examined their transcript levels by qPCR (Supplementary Table 2). Although the magnitude of the genetic variation was different between the two analyses, the qPCR results showed similar trends in upregulations or downregulations as in the transcriptomic analysis. This confirmed the reliability of the transcriptome data.

A total of 822 differential genes were expressed in the wild-type and ccpA mutant strains grown in glucose-excess conditions. Among them, 401 genes were downregulated while 421 were upregulated. This comparison showed the largest number of genetic changes among the four comparisons studied. Sixty-one percent of these DEGs (502 genes) corresponded to 98 specific expression pathways, including metabolism pathways for carbohydrates, energy, and amino acids. Significantly altered carbohydrate metabolism pathways in KEGG following ccpA knockout were fructose and mannose metabolism, glycolysis/gluconeogenesis, pyruvate metabolism, and butanoate metabolism. In the fructose and mannose metabolism pathway (ko00051), four genes involved in fructose and mannose transport and gene encoding fructose-1-phosphate kinase were upregulated, while the genes encoding five enzymes involved in the metabolism of fructose and mannose were downregulated. These findings showed that CcpA regulated the transport and metabolism of fructose and mannose (Figure 4). The expression levels of 20 genes in the glycolysis/gluconeogenesis pathway (ko00010) were altered in absence of ccpA (Figure 4). In comparison with the wild-type strain, genes encoding the pyruvate dehydrogenase (PDH) complex, galactose mutarotase, phosphoenolpyruvate carboxykinase, and alcohol-acetaldehyde dehydrogenase were significantly upregulated in the ccpA mutant. The genes encoding L-LDH, phosphopyruvate hydratase, phosphoglycerate kinase, 6-phosphofructokinase, pyruvate kinase, two glyceraldehyde-3-phosphate dehydrogenases, 6-phospho-beta-glucosidases, triose-phosphate isomerase, phosphoglycerate mutase, and fructose-1,6-bisphosphate aldolase were significantly downregulated. We also identified DEGs involved in the pyruvate metabolism pathway (ko00620) (Figure 4). Among these DEGs, all the upregulated genes were also involved in the glycolysis/gluconeogenesis pathway except for those encoding for NAD-dependent malic enzyme, D-3-phosphoglycerate dehydrogenase, and formate acetyltransferase; genes encoding acetyl-CoA carboxylase biotin and acyl phosphatase were downregulated in the ccpA mutant. In addition, the inactivation of ccpA also led to the downregulation of the gene encoding α-amylase. Taken together, these results showed that deletion of ccpA affected the pathways in glycolysis and pyruvate metabolism of S. bovis S1. The downregulation of ldh and upregulation of pfl led to higher formate and lower lactate levels, which suggested that the absence of ccpA could switch the fermentation pattern from homolactic to mixed fermentation.

We compared the transcriptomic profiles of wild-type and ccpA mutant strains in glucose-limited conditions. In this comparison, only 210 genes were significantly differentially expressed; 129 genes were upregulated while 81 genes were downregulated in the ccpA mutant. Compared with the transcriptomic comparison in glucose-excess condition, the DEGs reduced by approximately three quarters in glucose-limited condition. This indicated that the regulation of some genes by CcpA depended on glucose availability. Among these 210 DEGs, 118 could be grouped into different metabolic pathways in the KEGG database. In this comparison, only 4 significantly altered pathways were identified (P < 0.05), which included purine metabolism (ko00230), glyoxylate and dicarboxylate metabolism (ko00630), one carbon pool by folate (ko00670), and riboflavin metabolism (ko00740); only glyoxylate and dicarboxylate metabolism pathway was related to carbohydrate metabolism. Interestingly, there were almost no DEGs associated with pyruvate metabolism. Despite this, there were a significant number of DEGs involved in carbohydrate metabolism. The genes encoding 1-phosphofructokinase, glycosyl hydrolase family 32, PTS fructose transporter subunit IIC, galactose mutarotase, and alcohol-acetaldehyde dehydrogenase were upregulated in the absence of ccpA, while the genes encoding PTS cellobiose transporter subunit IIB, two alpha-amylases, glucose-6-phosphate isomerase, starch phosphorylase, phosphoglycerate kinase, and glucose-6-phosphate isomerase were significantly downregulated. These DEGs were mainly associated with fructose and mannose metabolism (ko00051), starch and sucrose metabolism (ko00500), and glycolysis/gluconeogenesis (ko00010), which was consistent with the results of comparison between the wild-type strain and ccpA mutant grown in glucose-excess condition. Notably, the expression of ldh and pfl was not significantly different in this comparison, which implied that the inactivation of ccpA might have little effect on the fermentation products in glucose-limited conditions.

To examine the effects of glucose concentration at the transcriptional level in the S. bovis S1, the transcriptomes of wild-type strains grown in glucose-excess and -limited conditions were compared. A total of 680 genes were differentially expressed (P < 0.05): 316 genes were upregulated while 364 were downregulated in the wild-type strain grown in glucose-limited conditions. To assess their biological functions, 418 of the 680 DEGs were clustered in 92 special pathways in KEGG; 12 significant pathways were primarily involved in the metabolism of amino acids, energy, carbohydrates, and cofactors and vitamins. Here, we focused on the KEGG pathways associated with carbohydrate metabolism. Compared to the wild-type strain grown at high-glucose concentrations, the DEGs involved in the citrate cycle including genes encoding phosphoenolpyruvate carboxykinase and the PDH complex were significantly upregulated in the low-glucose condition, while the gene encoding citrate synthase was downregulated. The glycolysis/gluconeogenesis pathway in the S. bovis S1 wild-type strain was dependent on glucose concentrations in the culture. In this pathway, the upregulated genes were consistent with those in the citrate cycle pathway except for the gene encoding galactose mutarotase. The genes encoding enzymes including L-LDH, type I glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, 6-phosphofructokinase, phosphopyruvate hydratase, triose-phosphate isomerase, phosphoglycerate mutase, and fructose-1,6-bisphosphate aldolase were downregulated in low-glucose conditions. Besides, the low-glucose condition also upregulated the expression of genes encoding alpha-amylase, pyruvate formate lyase-activating protein, as well as CcpA. All of these results indicated that, although to a lesser extent, glucose availability could affect the fermentation pattern by transcriptional regulation in S. bovis S1 wild-type strain. This was also confirmed by the fermentation profiles.

Finally, the transcriptome profiles of ccpA mutant strains grown in glucose-excess and -limited conditions were compared. Here, a total of 340 genes were differentially expressed; 144 genes were upregulated while 196 were downregulated; 210 genes were annotated to different KEGG pathways. Unlike in the wild-type strain, no gene in the citrate cycle pathway was significantly upregulated; the gene encoding phosphoenolpyruvate carboxykinase and a cluster of three genes that encoded aconitate hydratase, citrate synthase and NADP-dependent isocitrate dehydrogenase were significantly downregulated in this comparison. Besides, the number of DEGs in pyruvate metabolism, starch and sucrose metabolism, fructose and mannose metabolism, and glycolysis was considerably lesser than the comparison of wild-type strains grown in glucose-excess and -limited conditions. These findings suggested that CcpA mediated the differential expression of these genes. The gene encoding L-LDH was downregulated in this comparison, which was consistent with the results of the comparison in wild-type strain. This result indicated that in low-glucose conditions, the yield of lactate of S. bovis S1 could reduce even in the absence of ccpA.