HrcA-mediated transcriptional regulation affects the growth and survival of Streptococcus suis under low-glucose conditions

Abstract

Streptococcus suis (S. suis) is a major zoonotic pathogen whose nasopharyngeal colonization relies on adaptive regulation in response to the host’s low-glucose microenvironment. However, the molecular mechanisms underlying this adaptation remain largely unexplored. In this study, RNA-seq analysis of S. suis cultured under low-glucose (0.2%) conditions revealed 86 DEGs, predominantly associated with the phosphotransferase system, alternative carbon metabolism, and energy homeostasis pathways. A phenotypic screening of eight transcription factor (TF) mutants revealed that deletion of HrcA significantly impaired bacterial growth and survival under low-glucose conditions. ChIP-seq analysis revealed the HrcA-binding motif (GTGCTAATT) and mapped 391 potential target genes, 18 of which were differentially expressed under low-glucose conditions. Further qPCR and electrophoretic mobility shift assays (EMSAs) validated the direct regulation of 10 target genes by HrcA. Specifically, HrcA represses energy-intensive genes (B9H01_00980 and B9H01_04980) to conserve energy while activating B9H01_00995 and B9H01_01125 to promote alternative carbon metabolism and pyruvate fermentation. Additionally, HrcA modulates the expression of the AraC family TF1 and the DeoR family TF4, establishing a hierarchical regulatory network. Notably, HrcA downregulates its own expression under low-glucose conditions to fine-tune carbon metabolism gene regulation and maintain S. suis homeostasis, providing new insights into its adaptive strategies.

Introduction

Streptococcus suis (S. suis) is a significant zoonotic pathogen (Lun et al. 2007; Okwumabua et al. 2017) that commonly exists as a commensal bacterium in swine populations (Obradovic et al. 2021). However, it can also cause severe invasive diseases, including bacterial meningitis, pneumonia, septicaemia, and respiratory infections (Dutkiewicz et al., 2018; Vötsch et al. 2018). Asymptomatic colonization is a prerequisite for S. suis invasion and transmission (Ferrando et al. 2014). The upper respiratory tract, particularly the nasopharynx, serves as the primary niche for S. suis colonization and a key site for S. suis replication (Ferrando et al. 2014; Weiser et al. 2018). Despite the low glucose availability in the nasopharyngeal environment (Ferrando et al. 2014; Liu et al. 2023), S. suis can survive and colonize this nutrient-deficient niche, suggesting the presence of regulatory mechanisms that facilitate adaptation to low-glucose conditions. However, the molecular mechanisms governing S. suis gene regulation under such conditions remain poorly understood.

Transcription factors (TFs) play crucial roles in bacterial adaptation to environmental changes by sensing external signals and modulating the expression of downstream target genes, allowing bacteria to swiftly respond to environmental fluctuations (Fulton et al. 2009; Vaquerizas et al. 2009). For example, in Streptococcus pneumoniae, the transcription factor MgaSpn detects changes in glucose concentration and regulates the synthesis of phosphatidylcholine (ChoP), which is involved in carbohydrate transport, metabolism, and ribosome synthesis. This regulation enhances environmental adaptability and optimizes bacterial survival (Paixão et al. 2015). A similar mechanism may contribute to the colonization and pathogenesis of S. suis.

The role of TFs in regulating S. suis gene expression under low-glucose conditions remains largely unexplored. In this study, RNA sequencing (RNA-seq) was employed to analyze the global gene expression profile of S. suis in a low-glucose environment, identifying HrcA as a crucial TF for bacterial growth and survival under these conditions. Chromatin immunoprecipitation sequencing (ChIP-seq) identified the HrcA-binding motif and mapped 391 potential target genes, 18 of which presented differential expression in response to low glucose. Further validation via quantitative PCR (qPCR) and electrophoretic mobility shift assays (EMSAs) confirmed the direct regulation of 10 target genes by HrcA. These findings establish HrcA as a key regulator of S. suis homeostasis in low-glucose environments by modulating genes associated with carbon metabolism. This study advances our understanding of the molecular mechanisms that enable S. suis to adapt to nutrient-limited conditions.

Results

The transcriptional landscape of the S. suis low-glucose response

To investigate the adaptive regulatory mechanisms of S. suis under glucose-limited conditions, this study employed a chemically defined medium (CDM) devoid of glucose supplemented with either 0.2% (low-glucose) or 1% (normal-glucose) glucose. RNA-seq analysis was conducted to compare transcriptomic differences between these conditions (Fig. 1A). A total of 86 differentially expressed genes (DEGs) were identified, including 28 downregulated and 58 upregulated genes (Fig. 1A, Table S1). Gene Ontology (GO) enrichment analysis demonstrated that these DEGs were significantly associated with pathways related to carbohydrate transport, phosphotransferase system (PTS) activity, and transcriptional regulation (Fig. 1B). Furthermore, KEGG pathway analysis revealed that these DEGs are involved primarily in metabolic networks governing PTS metabolism, alternative carbon source utilization (e.g., fructose/mannose), and fatty acid oxidation for energy homeostasis (Fig. 1C).

Fig. 1

The transcriptional landscape of S. suis in response to low-glucose conditions. A Volcano plot illustrating gene expression differences between 0.2% glucose CDM and 1% glucose CDM based on RNA-seq analysis. Genes without significant differential expression are shown in gray. Significantly upregulated genes are highlighted in red, whereas moderately upregulated genes are highlighted in light red. Similarly, significantly downregulated genes are indicated in blue, whereas moderately downregulated genes are shown in light blue. B GO enrichment analysis of DEGs. C KEGG pathway enrichment analysis. A smaller enrichment p value indicates greater statistical significance. D Comparison of RNA-seq and qPCR expression profiles for selected genes in the same samples

Notably, the coordinated upregulation of key PTS components, including B9H01_01250, B9H01_04040, and the ABC oligosaccharide transporter B9H01_00980, suggests that S. suis enhances glucose uptake efficiency and expands its carbon source utilization. This adaptation likely involves the hydrolysis of α-galactosides to compensate for glucose scarcity. In the energy metabolism and alternative carbon source pathways, the dihydroxyacetone kinase complex genes (B9H01_09410–B9H01_09415) were upregulated by 5.8- and 5.6-fold (Table S1), catalyzing the conversion of glycerol into dihydroxyacetone phosphate. This finding indicates a metabolic shift toward lipid utilization to counteract energy deficits. Additionally, the coordinated upregulation of arginine deiminase pathway genes (B9H01_03125–B9H01_03140) facilitates ATP and ammonia production via arginine catabolism, thereby contributing to carbon‒nitrogen balance maintenance.

Interestingly, genes associated with energy conservation were broadly downregulated, including those involved in folate biosynthesis (B9H01_05540–B9H01_05555), with log2-fold changes of −1.53, −1.41, and −1.44, respectively, and specific sugar ABC transporters (B9H01_06090), with a log2-fold change of −1.08 (Table S1). This observation suggests that S. suis downregulates energy-intensive biosynthetic pathways to optimize resource allocation under glucose-limited conditions (Papp-Wallace and Maguire, 2006; Görke and Stülke, 2008). To validate the accuracy of our RNA-seq data, 30 randomly selected DEGs were analyzed via qPCR. The expression patterns observed via qPCR closely mirrored the RNA-seq results (Fig. 1D), further confirming the reliability of our transcriptomic analysis.

HrcA modulates the growth and survival of S. suis under low-glucose conditions

Transcriptomic analysis of S. suis under low-glucose conditions revealed eight differentially expressed TFs (Table 1), suggesting their potential roles in regulating the bacterial response to glucose limitation. To elucidate their functions, we successfully generated eight TF mutants via a resistance marker replacement strategy (Fig. 2A-B, Fig. S1) and systematically evaluated their growth and survival under low-glucose conditions.

Table 1 Related information of 8 differentially expressed TFs

Fig. 2

TF mutants were constructed and identified via PCR (A-B). Lane M represents the DL5000 DNA marker. Each set of four lanes corresponds to a specific mutant group (e.g., 1–4, 5–8). The results first display external primer amplification for both the wild-type (WT) and mutant (ΔTF) strains, followed by internal primer amplification for the WT and ΔTF strains

To evaluate the roles of transcription factors in S. suis adaptation to glucose limitation, we selected two media for phenotypic assays: (1) CDM for growth curve analysis, as preliminary data indicate that it supports glucose-dependent proliferation (Fig. S2), and (2) DMEM for survival assessment, where 0.2% glucose supplementation fails to sustain S. suis growth, reflecting stringent nutritional stress. This dual approach ensures that both growth dynamics and survival capacity are evaluated under distinct metabolic conditions.

Growth curve analysis in CDM revealed that the TF2 mutant presented a significantly lower growth rate than did the WT strain under normal glucose conditions. Under low-glucose conditions, the TF2 mutant presented a pronounced growth defect compared with the WT at 4 h postinoculation, although this difference gradually diminished over time (Fig. 3B). The TF6 mutant displayed a growth rate comparable to that of the WT under normal glucose conditions but exhibited a significant reduction in growth under low-glucose conditions (Fig. 3F). Similarly, the TF8 mutant presented a growth phenotype akin to that of TF2 (Fig. 3H). In contrast, the growth curves of the remaining TF mutants were not significantly different from those of the WT (Fig. 3A, C–E, G).

Fig. 3

HrcA influences the growth and survival of S. suis under low-glucose conditions A–H Growth curves of WT and ΔTF under normal glucose conditions (1% glucose) and low-glucose conditions (0.2% glucose). All glucose concentrations were based on chemically defined medium (CDM) without glucose supplemented with either 1% or 0.2% glucose as indicated. I Survival rates of WT and ΔTF under low-glucose conditions (DMEM-0.2% glucose). J Growth curves of WT, ΔHrcA, and CΔHrcA under normal-glucose conditions (1% glucose) and low-glucose conditions (0.2% glucose). All glucose concentrations were based on CDM without glucose supplemented with either 1% or 0.2% glucose as indicated. K Survival rates of WT, ΔHrcA, and CΔHrcA under low-glucose conditions (DMEM-0.2% glucose). The error bars indicate standard deviations. L Growth curves of the WT, ΔHrcA, and CΔHrcA strains in nutrient-rich medium (TSB). All the data are presented as the means with standard deviations from three biological replicates. All the data are expressed as the mean values accompanied by standard deviations derived from three biological replicates. The error bars represent the standard deviations. Statistical significance is indicated as follows: ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05

To further assess the survival capacity of the mutants under glucose-limited conditions, we performed survival rate assays for both the WT and TF mutant strains in DMEM supplemented with 0.2% glucose. The results demonstrated that the TF 2 mutant presented the most severe survival deficit under low-glucose conditions, while the TF 3, TF 5, and TF 7 mutants also presented varying degrees of survival impairment (Fig. 3I).

On the basis of these findings, we focused on TF2, which had the most significant regulatory effect under low-glucose conditions. This factor was identified as HrcA, a heat-inducible transcriptional repressor and a key regulator of the bacterial heat shock response (Duru et al. 2021; Versace et al., 2021). However, its role in adaptation to low-glucose environments remains largely uncharacterized.

To confirm the specific role of HrcA under low-glucose conditions, we constructed a complemented strain (CΔHrcA) (Fig. S3). The results demonstrated that the growth and survival rates of CΔHrcA under low-glucose conditions were fully restored to WT levels (Fig. 3J-K). Notably, in nutrient-rich medium (TSB), the WT, ΔHrcA, and CΔHrcA strains presented no significant differences in growth rates. (Fig. 3L). This finding suggests that, in addition to its well-characterized role in heat shock regulation, HrcA plays a specific role in the adaptive response to glucose limitation.

Identification of the HrcA-binding motif in the S. suis genome

To identify potential target genes regulated by HrcA, we performed ChIP-seq analysis on the complemented strain CΔHrcA carrying a 3 × Flag tag (Fig. S4). The genome-wide distribution of HrcA binding sites in S. suis is illustrated in Fig. 4A. On the basis of the ChIP-seq results, the HrcA binding motif was identified via MEME software (Fig. 4B).

Fig. 4

Identification of HrcA-binding motifs. A IGV visualization of the HrcA ChIP-seq binding profile."HrcA-IP"represents DNA enriched by 3 × Flag beads, whereas"HrcA-Input"represents total DNA without 3 × Flag bead capture. The y-axis indicates sequencing depth, and the x-axis represents genomic positions. B MEME analysis of HrcA binding motifs. Potential binding motifs were identified by analyzing nucleotide sequences via MEME. C EMSA was used to determine the specific binding of HrcA to its motif. HrcA (0 mM, 1 mM, 2.5 mM, or 5 mM) was incubated with either the motif-containing DNA sequence (MS, 50 nM) or a mutated motif sequence (MMS, 50 nM) prior to performing EMSA. D GO functional enrichment analysis of potential target genes. E KEGG pathway enrichment analysis. A smaller enrichment p value indicates greater statistical significance

To validate the accuracy of the identified HrcA binding motif, the HrcA protein was purified from low-yield expression cultures via nickel-affinity chromatography. The purified protein was then utilized in an EMSA (Fig. S5). The EMSA results demonstrated that HrcA binds to motif-containing DNA fragments in a concentration-dependent manner. Notably, mutation of the motif sequence completely abolished binding (Fig. 4C), confirming both the specificity of HrcA binding and the authenticity of the identified motif.

Using Find Individual Motif Occurrences (FIMO) analysis, we predicted potential HrcA targets within the S. suis SC19 genome and identified 391 genes containing the HrcA binding motif (Table S2), suggesting that these genes are putative HrcA targets. GO functional annotation and KEGG pathway enrichment analysis of these target genes (Fig. 4D–E) revealed significant enrichment in nucleotide catabolism, metabolic processes, ATP binding, and metal ion binding, indicating that HrcA may regulate energy metabolism, metal ion homeostasis, and nucleotide metabolism in response to environmental stress. Additionally, the significantly enriched pathways included fatty acid synthesis, biotin metabolism, secondary metabolite biosynthesis, and energy metabolism. The enrichment of these pathways further underscores the critical role of HrcA in metabolic regulation, particularly in energy acquisition and biosynthesis. These findings suggest that HrcA may serve as a central regulator of multiple metabolic pathways and molecular functions, playing crucial roles in cellular stress responses, energy homeostasis, and metabolic adaptation.

HrcA regulates carbon metabolism-related genes under low-glucose conditions

To investigate the regulatory role of HrcA under glucose-limited conditions, we performed an intersection analysis between the 391 predicted HrcA target genes and the 86 DEGs identified in the RNA-seq data under low-glucose conditions. This analysis revealed that 18 potential target genes were differentially expressed in response to glucose limitation (Fig. 5A).

Fig. 5

Verification of functional targets of HrcA via EMSA and qPCR. A Venn diagram showing the overlap between DEGs identified from S. suis RNA-seq under low-glucose conditions and potential target genes identified by HrcA ChIP-seq. B Regulation of differentially expressed target genes by HrcA under normal-glucose conditions. The blue color represents the WT under normal-glucose conditions (1% glucose), whereas the yellow color represents ΔHrcA under the same conditions. C Regulation of differentially expressed target genes by HrcA under low-glucose conditions. Blue represents the WT under low-glucose conditions (0.2% glucose), whereas yellow represents ΔHrcA under the same conditions. D–M qPCR analysis of significantly differentially expressed target genes regulated by HrcA under low-glucose conditions combined with EMSA to assess HrcA binding to its target genes. These results demonstrate the direct regulation of target genes by HrcA under low-glucose conditions. The qPCR results correspond to those in panel C. For EMSA analysis, purified HrcA (0 mM, 2.5 mM, or 5 mM) was incubated with motif-containing DNA fragments (50 nM) derived from the promoter or CDS regions of differentially expressed target genes. All the experiments were performed at least three times. Mean differences between data groups were evaluated via two-tailed Student’s t tests. The error bars represent the standard deviations. Statistical significance is indicated as follows: ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05

To further investigate the regulatory effects of HrcA on these genes under varying glucose concentrations, we performed qPCR analysis. Under normal glucose conditions, HrcA negatively regulates several key genes, including the sugar ABC transporter (B9H01_00980), glucose-1-phosphate adenylyltransferase (B9H01_04980), and isoamylase (B9H01_09920), which are involved in sugar transport, glycogen biosynthesis, and exopolysaccharide metabolism, respectively. This repressive effect of HrcA may help bacteria minimize energy consumption and maintain metabolic homeostasis when glucose is abundant (Fig. 5B).

Under low-glucose conditions, certain genes, such as B9H01_00980, B9H01_09920, and B9H01_10010, remained repressed by HrcA, suggesting that HrcA strictly regulates their expression to prevent unnecessary high-energy-consuming processes when glucose is scarce. Notably, the regulatory function of HrcA significantly shifted under glucose limitation, with some genes switching from repression to activation. For example, B9H01_00995 (α-galactosidase), B9H01_01125 (formyl-CoA transferase), and B9H01_04980 (glucose-1-phosphate adenylyltransferase) were upregulated by HrcA in response to low-glucose conditions. These genes participate in the metabolism of alternative carbon sources, including galactose, glycerol, and glycogen, enabling bacterial survival under energy-deprived conditions (Fig. 5C, Görke and Stülke, 2008; Gao et al. 2024; Liang et al. 2025).

qPCR results further revealed that HrcA consistently activated TF1 and TF4 under both normal and low-glucose conditions (Fig. 5B-C). However, RNA-seq data from S. suis grown in a low-glucose environment indicated that while HrcA expression was downregulated, the expression of TF 1 and TF 4 was upregulated (Table 1), suggesting a complex and dynamic regulatory interplay between HrcA, TF1, and TF4.

To further validate the regulatory relationship between HrcA and these differentially expressed genes, we selected 10 significantly differentially expressed target genes under low-glucose conditions for EMSA. The results demonstrated that HrcA binds to these target genes in a concentration-dependent manner, confirming its direct regulatory role under glucose-limited conditions (Fig. 5 D-M).

Discussion

Glucose is the primary carbon source for S. suis and is essential for its survival (Willenborg et al. 2015). However, the regulatory mechanisms that enable S. suis to adapt and persist in host niches with limited glucose availability, such as the nasopharynx (Paixão et al. 2015), remain poorly understood. In this study, RNA-seq was utilized to examine genome-wide transcriptional alterations in S. suis under low-glucose conditions, revealing its adaptive responses to glucose scarcity.

The DEGs were enriched primarily in carbohydrate metabolism pathways, including fructose/mannose metabolism and starch/sucrose metabolism, as well as the PTS and energy metabolism pathways, such as the tricarboxylic acid cycle and glycolysis/gluconeogenesis (Fig. 1C). These findings align with the core survival strategy of pathogenic bacteria, which prioritizes nutrient acquisition over host damage (Sundar et al. 2018; Zeng et al. 2023).

The significant enrichment of PTS, a bacterial-specific system for carbohydrate transport and phosphorylation (Görke and Stülke, 2008), under low-glucose conditions suggests that S. suis enhances PTS activation to efficiently capture trace amounts of glucose and alternative carbon sources such as fructose and mannose (Görke and Stülke, 2008; Durica-Mitic et al., 2018). This metabolic strategy is similar to that of Escherichia coli (E. coli), which optimizes energy acquisition by increasing substrate affinity under carbon-limited conditions (Durica-Mitic et al., 2018).

The significant differential expression of eight TFs in the transcriptomic data suggested that S. suis rapidly adjusts its transcriptional regulatory network to dynamically modulate gene expression in response to environmental stress. These DEGs are likely direct or indirect regulatory targets of these TFs (Sabath et al. 2024). Among them, HrcA has the most significant effect on the growth and survival of S. suis under low-glucose conditions. Other TFs also influence the growth and survival of S. suis to varying degrees under glucose-limited conditions, and further studies can be conducted to explore their roles in greater depth.

HrcA is widely recognized as a transcriptional repressor of heat shock genes in various bacterial species (Roncarati and Scarlato, 2017). However, its role in regulating gene expression under low-glucose conditions remains largely unexplored. By integrating transcriptomics, ChIP-seq, and EMSA, this study elucidated the molecular mechanism by which HrcA regulates carbon metabolism-related genes to facilitate S. suis adaptation to glucose limitation (Fig. 6). Under low-glucose conditions, HrcA transcription was significantly downregulated, and the HrcA deletion mutant presented early-stage growth retardation and markedly reduced survival rates (Fig. 3B, I), indicating that HrcA plays a critical role in glucose adaptation through a complex transcriptional regulatory mechanism.

Fig. 6

HrcA-mediated regulation of target genes under normal and low-glucose conditions. The blue “T” lines indicate inhibition, whereas the red arrows represent activation. The gray solid lines denote qPCR results with no statistically significant differences. Yellow boxes categorize target genes on the basis of their functions

ChIP-seq analysis revealed a specific HrcA binding motif (GTGCTAATT) in the S. suis genome (Fig. 4B). This motif exhibits a high degree of sequence similarity to the core region of the CIRCE element (TTAGCACTC-N9-GAGTGCTAA), which has been reported as the binding site for HrcA in other bacterial strains (Zuber and Schumann, 1994; Hakiem et al. 2020), suggesting that HrcA may regulate target gene expression through a conserved DNA recognition mechanism. This finding not only confirms the cross-species conservation of the HrcA binding element but also reveals unique evolutionary characteristics of the HrcA binding motif in S. suis. Although highly similar to the CIRCE element, minor nucleotide variations in this motif may influence HrcA binding affinity to its target genes, suggesting that the HrcA regulatory network extends beyond the heat stress response to dynamic metabolic adaptation under glucose limitation. This regulatory shift underscores the functional flexibility of HrcA under different stress conditions.

By analyzing potential target genes, we identified up to 391 genes in S. suis. that may be regulated by HrcA (Table S2). Integrated RNA-seq and ChIP-seq analyses revealed that 18 HrcA target genes were differentially expressed under low-glucose conditions (Fig. 5A). Among them, the sugar ABC transporter (B9H01_00980) and glucose-1-phosphate adenylyltransferase (B9H01_04980) are repressed by HrcA under normal glucose conditions, and this repression persists under glucose-limited conditions and is likely to prevent unnecessary energy expenditure.

Under low-glucose conditions, HrcA transitioned from a repressive role to an activating role for α-galactosidase (B9H01_00995) (Fig. 5B-C), indicating that HrcA helps S. suis adapt to glucose scarcity by activating the utilization of alternative carbon sources. This regulatory mechanism facilitates bacterial survival and colonization in the nasopharyngeal niche, which has low glucose but is rich in galactose (Trappetti et al., 2017; Gao et al. 2024). The regulation of formyl-CoA transferase (B9H01_01125) also shifted from repression to activation (Fig. 5B-C), suggesting that HrcA may facilitate S. suis adaptation to low-glucose conditions by modulating pyruvate metabolism (Fan et al. 2025). However, while our findings provide strong evidence of transcriptional changes, we acknowledge the limitations of transcriptomics in fully capturing metabolic flux. Transcriptional changes do not always correlate directly with metabolic outcomes because of factors such as posttranslational regulation and enzyme activity (Tripodi et al. 2015). To address this limitation, future studies will incorporate metabolomic analyses to confirm whether the observed transcriptional shifts lead to significant metabolic reprogramming under low-glucose conditions.

Furthermore, although 373 potential target genes did not exhibit differential expression under low-glucose conditions, they were significantly enriched in key pathways associated with energy metabolism, metal ion homeostasis, and secondary metabolite biosynthesis (Fig. 4D-E). These findings indicate that HrcA not only regulates core carbon metabolism genes but also interacts with other functional genes to maintain cellular homeostasis in glucose-limited environments.

In this study, we observed a phenomenon that warrants further investigation. RNA-seq analysis revealed that under low-glucose conditions, HrcA transcription was downregulated, whereas TF1 and TF4 transcription was upregulated. However, qPCR analysis revealed that, in the ΔHrcA mutant, TF1 and TF4 expression was significantly reduced regardless of the glucose concentration. This apparent contradiction may be attributed to three possible explanations: 1) Compensatory regulatory network: In a low-glucose environment, the downregulation of HrcA may activate other regulatory factors that compensate for the increase in TF1 and TF4 transcription. The regulatory effect of these factors might override HrcA's direct influence on TF1 and TF4. A similar phenomenon has been reported in Lactobacillus plantarum, where the expression levels of HrcA target genes (grpE, dnaK, and dnaJ2) remained unchanged despite reduced HrcA expression, suggesting a compensatory regulatory mechanism (Van Bokhorst-van de Veen et al., 2013). 2) Disruption of a hierarchical regulatory network: HrcA may function within a complex hierarchical regulatory network, and its deletion could impair TF1 and TF4 activation, leading to transcriptional downregulation independent of glucose concentration. A comparable regulatory hierarchy has been identified in Staphylococcus aureus, where CtsR directly regulates HrcA, which, in turn, controls genes such as groESL, forming a cascade regulatory system (Chastanet et al. 2003; Roncarati and Scarlato, 2017). The absence of HrcA disrupts this network, thereby altering groESL expression. Similarly, in this study, HrcA may regulate TF1 and TF4 through an unidentified hierarchical mechanism, and its deletion likely disrupts this system, resulting in the overall downregulation of TF1 and TF4 expression. 3) Spatiotemporal variability in experimental conditions: Differences in the temporal and spatial aspects of experimental conditions may contribute to discrepancies between RNA-seq and qPCR data. Although we endeavored to maintain consistent experimental conditions across both RNA-seq and qPCR analyses, variations in data acquisition methods may still account for the observed differences.

This study presents several unresolved questions that require further investigation to fully elucidate the function and molecular mechanism of HrcA in S. suis adaptation to low-glucose conditions. These questions include identifying the transcription factors (TFs) that mediate compensatory regulation following HrcA downregulation, determining the specific roles of TF 1 and TF 4 within the hierarchical regulatory network, and elucidating their target genes. Additionally, while transcriptional shifts have provided valuable insights, future research will also incorporate metabolomic analyses to confirm whether the observed transcriptional changes lead to significant metabolic reprogramming. By systematically analyzing these aspects, we aimed to elucidate the central role of HrcA in the regulatory network that governs S. suis adaptation to low-glucose environments. These efforts will deepen our understanding of the molecular mechanisms underlying bacterial responses to glucose limitation in the host and provide new targets for the development of antibacterial strategies.

Conclusion

This study elucidates the global transcriptional changes in S. suis under low-glucose conditions and demonstrates that HrcA facilitates S. suis homeostasis and adaptation by downregulating its own expression, thereby regulating carbon metabolism-related genes and coordinating the expression of other functional genes. These findings provide new insights into the regulatory mechanisms that enable bacteria to adapt to nutrient deprivation.

Methods

Bacterial strains, plasmids, and growth conditions

The bacterial strains and plasmids used in this study are listed in Table S3. S. suis strains and their isogenic mutants were cultured in tryptic soy broth (TSB; BD) or on tryptic soy agar (TSA; BD) supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco) at 37°C. E. coli DH5α, which was used as the cloning host, was grown in lysogeny broth (LB; Gibco) or plated onto LB agar at 37°C. When needed, antibiotics were added at the following concentrations: spectinomycin (SPC, 50 μg/mL for E. coli, 100 μg/mL for S. suis), kanamycin (Kana, 50 μg/mL), and erythromycin (Erm, 500 μg/mL for E. coli, 4 μg/mL for S. suis).

RNA extraction and RNA-seq analysis

To form the control group, S. suis strain SC19 was cultivated in chemically defined medium (CDM) supplemented with 1% glucose, which was prepared in-house according to the formulation described by van de Rijn and Kessler (van de Rijn and Kessler, 1980), until it reached the midexponential phase. until it reached the midexponential phase. For the low-glucose treatment group, SC19 cells were cultured in CDM supplemented with 0.2% glucose until they reached the same growth phase. The bacterial cultures were centrifuged at 4500×rpm for 15 min at 4°C, after which the bacterial pellets were harvested. Total RNA was extracted via the Bacteria RNA Extraction Kit (Vazyme). The purified RNA samples were sent to OEBiotech (Shanghai, China) for RNA-seq analysis, with each group containing three biological replicates. Sequencing reads were aligned via Rockhopper2 (Tjaden, 2015). Differential expression analysis was performed via DESeq2 (Love et al. 2014), with a significance threshold of an absolute fold change ≥ 1 and q < 0.05, to identify DEGs.

Construction of deletion mutants and complemented strains

TF mutant strains were generated via resistance replacement and signal peptide transformation methods following established protocols. The primers used are listed in Table S4. Upstream and downstream fragments of the TF genes, along with fusion linkers and a spectinomycin resistance (SPC) fragment, were amplified separately via the SC19 genome and the pSET2 vector as templates. The L-arm–SPC–R-arm fragments were assembled via overlap PCR, purified, and transformed into SC19, as described previously, to generate the mutant strains (Gao et al. 2022). For hrcA complementation (CΔHrcA), the hrcA gene, including a C-terminal 3×Flag tag and a constitutive enolase promoter, was cloned and inserted into the pSET2 (Erm) plasmid and introduced into the ΔHrcA strain.

Assessment of S. suis growth in different low-glucose basal media

WT strains were cultured in TSB until an OD600 nm of 0.6 was reached, as measured via a NanoOne ultramicro spectrophotometer (Yooning). The bacterial cells were then harvested, washed three times with PBS, and adjusted to an OD600 nm of 0.2. The cultures were subsequently inoculated at a 1:50 (v/v) ratio into CDM or Dulbecco’s modified Eagle’s medium (DMEM, Solarbio) supplemented with 0.2% glucose. Bacterial growth was monitored at 37°C via the Bioscreen C system (Oy Growth Curves Ab Ltd.), with each strain analyzed in triplicate.

Growth of S. suis in CDM with different glucose concentrations

The WT and ΔTF strains were cultured in TSB until they reached an OD600 nm of 0.6. The bacterial cells were harvested, washed three times with PBS, and adjusted to an OD600 nm of 0.2. The cultures were then inoculated at a 1:50 (v/v) ratio into CDM containing either 1% or 0.2% glucose or into TSB. Bacterial growth was assessed at 37°C via the Bioscreen C system, with each strain analyzed in triplicate.

Survival of S. suis in DMEM with low-glucose concentrations

The WT and ΔTF strains were grown in TSB until an OD600 nm of 0.6 was attained. The bacterial cells were then collected, rinsed three times with PBS, and subsequently resuspended to an OD600 nm of 0.6. The cultures were subsequently inoculated into DMEM supplemented with 0.2% glucose at a 1:50 (v/v) ratio and incubated under static conditions at 37°C. Viable bacterial counts were assessed at 0 h and 6 h, with three biological replicates conducted for each strain.

Chromatin immunoprecipitation sequencing (ChIP-seq)

ChIP assays were conducted following previously established protocols (Minch et al. 2015). The CΔHrcA strain expressing 3 × Flag-tagged HrcA was cultured in 40 mL of TSB until the logarithmic growth phase was reached. The cells were fixed with 1% formaldehyde for 10 min at room temperature, and the cross-linking reaction was quenched by the addition of 125 mM glycine. The cells were lysed, and chromosomal DNA was fragmented to 0.2–1.0 kb via sonication. The lysate was centrifuged at 4°C to remove insoluble debris, and the supernatant was used as the input sample for immunoprecipitation. The samples were incubated with 50 μL of agarose-conjugated anti-FLAG antibodies (Sigma‒Aldrich) in immunoprecipitation buffer. After sequential washing, crosslink reversal, and DNA purification (Niu et al. 2023), DNA fragments ranging from 150 to 250 bp were isolated for library preparation via the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs). The resulting libraries were subsequently sequenced on an Illumina HiSeq 2000 system. Two biological replicates were analyzed. The ChIP-seq reads were mapped to the SC19 genome (NZ_CP020863.1) via Bowtie2 (V. 2.3.4.1)(Langmead and Salzberg, 2012), and uniquely mapped reads were retained. Binding peaks (q < 0.01) were identified via MACS2 (V. 2.1.1, Zhang et al. 2008). BEDtools (V. 2.26.0) (Quinlan and Hall, 2010) was used to merge and intersect peak intervals. HrcA-binding motifs were identified via MEME software, and their genome-wide occurrence was determined via the FIMO algorithm (Bailey et al. 2015).

Quantitative real-time PCR (qPCR) assays

qPCR analysis was carried out with RNA extracted from the samples. cDNA synthesis was achieved via the HiScript II Q RT SuperMix Kit (Vazyme) according to the manufacturer’s protocol. Amplification was then performed with Taq Pro Universal SYBR qPCR Master Mix (Vazyme) on a QuantStudio 6 Flex system (Thermo Fisher). Gene expression levels were normalized to gapdh (Brassard et al. 2004), and relative changes were determined via the 2^^−ΔΔCT method. Each group included three biological replicates, and the primers used are detailed in Table S4.

HrcA protein purification and electrophoretic mobility shift assays (EMSAs)

The HrcA gene was PCR-amplified from the SC19 genome and inserted into the pET-28a vector, which carries a 6×His tag. The recombinant plasmid was sequence-verified and introduced into E. coli BL21 (DE3). The expression of the target protein was induced with 0.8 mM IPTG at 16°C for 20 h. Bacterial cells were then harvested, rinsed with PBS, and disrupted via a low-temperature ultrahigh-pressure homogenizer (JNBIO, China). The soluble HrcA protein was purified via nickel‒nitrilotriacetic acid spin columns (GE Healthcare) and subsequently eluted with 500 mM imidazole. The eluate was desalted, concentrated to approximately 3 mL in Tris buffer (25 mM Tris, 150 mM NaCl, pH 7.4), aliquoted, and stored at − 80°C. For the EMSAs, self-annealed complementary oligonucleotide probes (Table S4) were incubated with purified HrcA at 0 mM, 2.5 mM, or 5 mM in a 20 μL reaction mixture containing EMSA buffer, 10% glycerol, BSA, and ddH₂O. Following 20 min of incubation at 30 °C, the samples were subjected to electrophoresis on a native polyacrylamide gel at 100 V for 60 min, stained with GelRed (Biosharp), and visualized via a Bio-Rad ChemiDoc XRS + system.

Statistical analysis

Unless otherwise specified, statistical analyses were carried out via two-tailed, unpaired t tests. Each experiment was conducted in triplicate, and the findings are expressed as the means ± standard deviations. Data analysis was performed via GraphPad Prism 9.0.