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Multi-omics analysis reveals genes and metabolites involved in Streptococcus suis biofilm formation

BMC Microbiology volume 24, Article number: 297 (2024) Cite this article

Abstract

Background

Streptococcus suis is an important zoonotic pathogen. Biofilm formation largely explains the difficulty in preventing and controlling S. suis. However, little is known about the molecular mechanism of S. suis biofilm formation.

Results

In this study, transcriptomic and metabolomic analyses of S. suis in biofilm and planktonic states were performed to identify key genes and metabolites involved in biofilm formation. A total of 789 differential genes and 365 differential metabolites were identified. By integrating transcriptomics and metabolomics, five main metabolic pathways were identified, including amino acid pathway, nucleotide metabolism pathway, carbon metabolism pathway, vitamin and cofactor metabolism pathway, and aminoacyl-tRNA biosynthesis metabolic pathway.

Conclusions

These results provide new insights for exploring the molecular mechanism of S. suis biofilm formation.

Peer Review reports

Introduction

Streptococcus suis is an important zoonotic pathogen, and its infection can cause meningitis and sepsis in pigs [1]. In severe cases, it can lead to pig death and cause huge economic losses in the global pig farming industry [2]. S. suis is also transmitted to humans through contaminated environments or diets, where it threatens human health [3]. It is worth noting that thousands of cases of human infection with S. suis have been caused in China and Vietnam [4, 5]. According to research reports, S. suis has become one of the most common pathogens of bacterial meningitis in adult humans [6]. Bacteria exist in nature in two main forms, namely planktonic state and biofilm state [7]. Biofilm is a bacterial community composed of extracellular DNA (eDNA), proteins and polysaccharides [8]. Bacteria are endowed with a number of survival advantages when they switch from a planktonic state phenotype to biofilm state. Biofilm formation can make bacteria resistant to the antibiotics and the host immune system [9]. Biofilm have been explored for decades, and evidence has pointed to the role of biofilm formation in the pathogenic process of pathogenic bacteria, especially in relation to persistent bacterial infections. Based on conservative estimates, 65%~80% of bacterial diseases are related to biofilm [10]. The formation of biofilms is often accompanied by changes in bacterial gene expression and remodeling of metabolic processes. Analysis of biofilm formation in Bifidobacterium pseudocatenulatum using transcriptomics and metabolomics showed that stress response, quorum sensing and exopolysaccharides (EPS) production are crucial in the process of B. pseudocatenulatum biofilm formation [11]. Analysis of Salmonella typhimurium M3 biofilm formation by transcriptomics and metabolomics revealed that metabolites of amino acid metabolism (o-aminobenzoic acid, indole, and putrescine) may act as signaling molecules to promote S. typhimurium M3 biofilm formation [12].

Along with the rapid development of transcriptomics, proteomics and metabolomics, it has become a trend to systematically analyze the changes in the process of bacterial biofilm formation with the help of histological tools [13]. Rui Jiang et al. analyzed the transcriptome of Haemophilus parasuis biofilm formation by RNA-Seq, in which the genes artM, artQ, ssrS, pflA and hutX may be involved biofilm formation [14]. The analysis of Abigail Leggett on the metabolomics of Pseudomonas aeruginosa in both planktonic and biofilm states reveal significant differences in creatine, glutathione, alanine, choline metabolism and tricarboxylic acid cycle [15].

Biofilm formation is a complex dynamic process, and the information generated through a single omics method in analyzing the background of the entire biofilm system is very limited. Integrating multi-omics can help us decipher the crucial metabolic pathways for biofilm formation. However, few studies have used multi-omics strategy to identify the core expression pathways required for the biofilm of S. suis. This study used transcriptomics and untargeted metabolomics methods to determine differentially expressed genes and metabolites in biofilm state. This will provide new research directions for the mechanism of S. suis biofilm formation and the treatment of persistent infections caused by S. suis biofilm.

Materials and methods

Bacterial strains, growth conditions, and reagents

The S. suis serotype 2 strain used in this experiment was ZY05719 which was isolated from dead pigs in 2005 (Sichuan, China). S. suis was grown in tryptic soy broth (TSB) (Sigma-Aldrich, St. Louis, MO).

RNA isolation and RNA-Seq data analysis

Overnight cultured S. suis was inoculated into cell culture plates at a ratio of 1:100 and cultured at 37℃ for 48 h. For the planktonic group, the TSB suspension in the cell culture dish containing S. suis growing in its planktonic state was gently collected, and the medium was centrifuged for planktonic bacteria cells. It is important to remember not to touch the bottom of the cell culture dish gently here. The biofilm group is completely different from the planktonic group. Specifically, after the planktonic bacteria are gently removed, an appropriate amount of PBS is added, and the remaining planktonic bacteria are gently washed away with PBS. Then, the cell scraper is used to scrape the biofilm formed bacteria from the cell culture dish bottom. Finally, the bacteria are resuspended in PBS, centrifuged, and the biofilm state bacteria are collected. Two replicates were performed in each group. Total RNA was extracted from planktonic S. suis and biofilm S. suis collected using TRIzol™Plus RNA purification kit (Thermo Fisher Scientific, Shanghai, China). Send the total RNA of biofilm S. suis and planktonic S. suis to Jingnuojin Co., Ltd. for Illumina Hiseq platform sequencing. Subsequently, clean the sequencing results according to S. Suis 05ZYH33 genome (CP000407.1), functionally annotated through Cluster of Orthologous Group (COG) database.”

Experimental validation of DEGs by RT-qPCR

To validate the gene expression profiles obtained by transcriptome sequencing analysis, we used RT-qPCR to confirm the expression levels of eight selected genes with S. suis 16 S rRNA serving as the reference gene. According to the instructions of the reagents, the RNA of the planktonic group and the biofilm group is reverse transcribed into cDNA by PrimeScript™RT reagent Kit. RT-qPCR was performed using AceQ Universal SYBR qPCR Master Mix (Q511-02, Vazyme, Nanjing, China) and Bio-Rad CFX96 Touch real-time fluorescent quantitative PCR instrument (Bio-Rad, USA). The specific primers are shown in Table 1. The thermal cycle condition as follows: 95℃ for 3 min, 40 cycles of 95℃ for 30s, 60℃ for 30s. The fold change was calculated using the formula 2−ΔΔCT [16].

Metabolite extraction

Overnight cultured S. suis was inoculated into cell culture plates at a ratio of 1:100 and cultured at 37℃ for 48 h. Samples of biofilm S. suis and planktonic S. suis were centrifuged at 12,000 rpm for 10 min. The samples were then rapidly frozen in liquid nitrogen, followed by ultrasonic crushing with 800µL of cold extract solution (methanol: acetonitrile: H2O = 2:2:1). The sonicated samples were centrifuged at 12,000 rpm at 4 °C for 10 min, and the resulting supernatant was stored at -80 °C until further LC-MS analysis.

LC–MS analysis of metabolites

The metabolites were analyzed by Liu’s method [17]. Metabolomics samples of planktonic and biofilm S. suis were analyzed using a Thermofisher UPLC system (Thermofisher, SanJose, CA, USA) combined with an LTQ XL mass spectrometer. Columns and conditions are as follows: Chromatographic column: 250 × 4.6 mm 5 μm BETASIL C18 70,105–154,630. The column was maintained at a temperature of 40℃ and operated at a flow rate of 0.5mL/min. In the positive ion mode, mobile phase A consists of 0.1% formic acid, while mobile phase B is composed of an acetonitrile solution containing 0.1% formic acid. In the anionic mode, mobile phase A comprises a 1mM ammonium formate solution (containing 0.1% formic acid), and mobile phase B remains as acetonitrile. Sample volume was 2 µL. The optimized gradient profile was determined as follows: 0 min (0% B), 8 min (35% B), 18 min (35% B), 22 min (90% B), 28 min (90% B), 30 min (0% B). ESI ion source spray voltage is 3800 V (positive ion mode), 3100 V (negative ion mode). The capillary temperature is 320℃. The sheath gas flow rate is 45Arb. The auxiliary gas flow rate is 15Arb. Scanning range 70–1000 m/z. Raw data handling was done using Compound Discoverer software. MetaboAnalyst 5.0 (https://www.metaboanalyst.ca) was used for metabolic pathway and function analysis.

Statistical analysis

All data from Transcriptomics and Metabolomics were statistically analyzed using R software. The significance of differences was determined by t-test. P < 0.05 was considered to be significant.

Result

Sequencing of the Streptococcus suis transcriptome

Biofilm formation is a complex dynamic process. In order to identify the key genes in the process of biofilm formation in S. suis, we performed transcriptomic analysis of biofilm and planktonic state of S. suis. A total of 24,109,505 (planktonic phenotype) and 29,340,019 (biofilm phenotype) sequenced raw reads were obtained for the cDNA libraries. By normalizing the original data, only genes with Log2FoldChange > 1 or Log2FoldChange < -1 and p < 0.05 were analyzed downstream. Our data showed that a total of 789 (36.74%) differentially expressed genes were identified, of which 434 (20.21%) of the differentially expressed genes were up-regulated, whereas 355 (16.53%) of the differentially expressed genes were down-regulated (Fig. 1A, B). After Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the 789 differential genes, the differential genes were enriched into 62 KEGG pathways, and the differential genes were mainly involved in the pathways of Bitoin metabolism, DNA replication, Starch and sucrose metabolism, Glycerophospholipid metabolism, Glycerophospholipid, and Glycolysis/Gluconeogenesis pathways (Fig. 1C). Differential genes were subjected to GO for annotation, and differentially expressed genes were classified into two categories: molecular functions and biological processes. The main functions are categorized as transferase activity, small molecule metabolic processes, carbohydrate metabolic processes, small molecule metabolic processes containing nucleobases, and lipid metabolic processes (Fig. 1D). To validate these RNA-seq results, qRT-PCR was performed. we used qRT-PCR to differentially express five genes randomly selected among the up-regulated and down-regulated genes between biofilm and planktonic cells, respectively. The results showed the same trend in qRT-PCR results as RNA-seq biofilm-specific expression of genes (Fig. 1E).

Fig. 1
figure 1

RNA-Seq Analysis of S. suis in Planktonic and Biofilm States. A: Fan diagram of differentially expressed genes in biofilm state S. suis; B: Volcano map of differentially expressed genes in biofilm state S. suis; C: Analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of differentially expressed genes in biofilm state of S. suis; D: Analysis of the Gene Ontology (GO) of differentially expressed genes in biofilm state S. suis. E: RT-qPCR verification of differential gene expression

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Metabolomics analysis

To investigate the changes in bacterial metabolism in S. suis before and after biofilm formation, we performed metabolomic analysis using LC-MS to decipher the unique metabolism that is critical for biofilm formation. Metabolomics analysis revealed significant metabolic alterations during biofilm being formed, which was significantly different from the planktonic S. suis population (Fig. 2). From the scoring plot derived from the partial least-squares discrimination analysis (PLS-DA) analysis of the metabolomics data collected by the LC/MS system, we can see that principal component analysis 1(PC1) accounted for 40.5% of the variance in the cationic mode and principal component analysis 2 (PC2) accounted for 10% of the variance (Fig. 2A).Stratification cluster analysis of metabolites was performed, as shown in Fig. 2B. Different color regions in the figure represented the relative quantification between different groups. It was found from the figure that many different metabolites were highly expressed in the biofilm S. suis, while their expressions were relatively low in the planktonic S. suis (Fig. 2B). A total of 1135 metabolites were detected by metabolomics, of which 202 metabolites were up-regulated and 163 down-regulated metabolites (Fig. 2C). Analysis of differential metabolite enrichment showed that the top 10 enriched metabolite pathways were Glutamate Metabolism, Arginine and Proline Metabolism, Purine Metabolism, Glutathione Metabolism, Cysteine Metabolism, Glycine and Serine Metabolism, Urea Cycle, Ammonia Recycling, Malate-Aspartate Shuttle and Gluconeogenesis (Fig. 2D). Further detailed pathway analysis of the differential metabolites revealed the involvement of Glutamate Metabolism, Aminoacyl-tRNA biosynthesis, Methane metabolism, Glyoxylate and dicarboxylate metabolism, Glycine, serine and threonine metabolism, Pyrimidine metabolism, Nicotinate and nicotinamide metabolism, Citrate cycle (TCA cycle), Purine metabolism, Sulfur metabolism, Alanine, aspartate and glutamate metabolism, Cysteine and methionine metabolism, Cyanoamino acid metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis, Pantothenate and acetyl coenzyme A (CoA) biosynthesis, Pyruvate metabolism, Glycolysis / Gluconeogenesis, Phenylalanine metabolism (Fig. 2E).

Fig. 2
figure 2

Untargeted Metabolomics Analysis of S.suis in Planktonic and Biofilm States. A: principal component analysis (PCA) score plot of metabolites in S.suis planktonic cells and biofilms; B: Cluster analysis plot of metabolites of S.suis in biofilm and planktonic states; C: Volcan plot of differential metabolites of S. suis in biofilm and planktonic state; D: Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differential metabolites of S. suis in biofilm and planktonic states; E: Detailed pathways enriched of differentially detected metabolites

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3.3 Combined transcriptome and metabolomics analysis

In order to obtain a more comprehensive understanding of the gene transcription and metabolic regulation networks underlying S. suis biofilm formation, we employed KEGG to integrate transcriptomics and metabolomics data. Upon analysis, we identified 15 metabolic pathways, including Glutamate Metabolism, Aminoacyl-tRNA Biosynthesis, Methane Metabolism, Glycine, Serine and Glycine Metabolism, Cyanoamino acid metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis, Pantothenate and CoA biosynthesis, Pyruvate metabolism, Glycolysis/Gluconeogenesis and Phenylalanine metabolism. The 15 pathways are summarized as amino acid metabolism, carbon metabolism, nucleotide metabolism, aminoacyl-tRNA biosynthesis, vitamin and cofactor metabolism, methane metabolism, and thiamine metabolism. Based on the different degree of enrichment of each pathway, we focus on the analysis of amino acid metabolism, carbon metabolism, nucleotide metabolism, aminoacyl-tRNA biosynthesis, and vitamin and cofactor metabolism, with corresponding schematic diagrams shown in Fig. 3. In the amino acid metabolic pathway, the concentrations of metabolites Glycine and Phenylalanine decreased, while the concentrations of L-Cysteine and Oxaloacetate increased. Correspondingly, At the gene transcription level, 8 genes were up-regulated, and 3 genes were down-regulate (Tables 2 and 3). In the nucleotide metabolic pathway, the content of metabolite Adenine increases, while the content of Inosine, CDP, and CMP decreases. At the level of gene transcription, 22 genes are downregulated, 12 genes were up-regulated (Tables 2 and 3). In the carbon metabolism pathway, the content of metabolite Oxaloacetate increased. At the gene transcription level, 5 genes were up-regulated, and 14 genes were down-regulate (Tables 2 and 3). In the vitamin and cofactor metabolism pathway, the concentrations of metabolites L-Cysteine and NAD + significantly increased. The expression levels of SSU05_0689 and nadE genes were upregulated, while the expression level of SSU05_1088 was downregulated (Tables 2 and 3). In the aminoacyl-tRNA biosynthesis pathway, the metabolites L-Phenylalanine and Glycine showed a decrease in content, while the metabolite L-Cysteine showed an increase in content. The expression levels of pheT, pheS, glyS and glyQ genes were downregulated, while the expression level of cysS gene was upregulated (Tables 2 and 3). Overall, the integrated analysis successfully identified the pathway that may be associated with the formation of S. suis biofilm.

Table 1 Primers used in this study
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Table 2 Genes downregulated in biofilm state in S. suis
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Fig. 3
figure 3

Metabolic pathways affected during biofilm formation in S. suis: amino acid metabolism, carbon metabolism, nucleotide metabolism, vitamin and cofactor metabolism, and aminoacyl-tRNA biosynthesis pathway. Blue color represents down-regulated metabolites and genes, and red color represents up-regulated metabolites and genes

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Table 3 Genes downregulated in biofilm state S. suis
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Discussion

Biofilm formation creates a safe haven for the bacteria, with the help of which some of them survive the challenges of harsh conditions. Biofilm formation poses a great challenge to its therapeutic aspects [18]. In this study, 789 differential genes and 365 differential metabolites were identified by transcriptomics and metabolomics, respectively. In integrating transcriptomics and metabolomics data, a total of 165 metabolic pathways were identified including: Aminoacyl-tRNA biosynthesis, Glutathione metabolism, Methane metabolism, Pyrimidine metabolism, Nicotinate and nicotinamide metabolism, Purine metabolism, Cyanoamino acid metabolism, Alanine, aspartate and glutamate metabolism, Thiamine metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis, Pantothenate and CoA biosynthesis, Pyruvate metabolism, Glycolysis / Gluconeogenesis, Glycine, serine and threonine metabolism, Cysteine and methionine metabolism (Figs. 1 and 2). These results suggest that amino acid metabolism, carbon metabolism, nucleotide metabolism, Aminoacyl-tRNA biosynthesis and vitamin and cofactor metabolism, methane metabolism, and thiamin metabolism play key roles in the formation of the biofilm of S. suis (Fig. 3). Compared to planktonic state bacteria, biofilm state bacteria exhibit a lower level of metabolic activity [19, 20]. Our metabolomics data strongly supports the notion that numerous crucial metabolic pathways are downregulated in biofilm state bacteria. Consistent with this observation, the transcription levels of enzymes involved in catalyzing metabolic reactions within these pathways also demonstrate a declining trend.

Studies have shown that amino acid metabolism plays an important role in biofilm formation in several bacteria [21, 22]. Deficiency of glycine biosynthesis induces cellular autolysis in Staphylococcus epidermidis small colony variants cells (SCV), and SCV cellular autolysis induces intracellular DNA leakage and promotes S. epidermidis biofilm formation [23].

There is ample evidence to suggest that D-amino acids are involved in bacterial growth, peptidoglycan metabolism, and the formation and dissemination of biofilm [21, 24, 25]. Research has shown that the accumulation of D-amino acids can effectively inhibit the formation of biofilms, and can also disperse pre-existing biofilms [21]. For example, the addition of D-phenylalanine can inhibit the formation of Staphylococcus aureus biofilms [21]. Based on our metabolomics results, we did not detect the presence of D-amino acids, instead, significant changes in L-amino acids were observed. This suggests that L-amino acids play a role in regulating the formation of S. suis biofilms to some extent.

The dynamic changes in carbohydrate metabolism are directly linked to the bacterial energy supply, and it is closely related to the development process of bacterial biofilms [26]. In S. suis, the glycolytic enzyme GAPDH is responsible for converting 3-phosphoglycerate to 1,3-bisphosphoglycerate [27]. This enzyme is highly expressed in biofilm state S. suis, and similar situations also occur in S. pneumoniae, Streptococcus equi subsp. equi and S. aureus [28,29,30]. The deletion of this gene results in a decrease in the ability of S. suis to form biofilm [31]. SSU05_0544 encodes pyruvate kinase, which catalyzes the conversion of phosphoenolpyruvate to pyruvate [32]. The regulation of this enzyme’s activity is directly related to the downstream metabolite pyruvate content, which in turn affects downstream pyruvate metabolism [32]. It is worth mentioning that the enzyme responsible for pyruvate metabolism, the pyruvate dehydrogenase complex, is encoded by genes such as SSU05_1840 and SSU05_1841, which have been validated by reverse genetics methods to have a relationship with biofilm formation [16]. The inactivation of this gene, impairs the S. suis biofilm formation. SSU05_0398 and SSU05_1401 both encode the glucose/maltose/N-acetylglucosamine-specific phosphotransferase system IIC component. The regulatory role of the phosphotransferase system (PTS) in the Streptococcus pyogenes biofilm formation has been demonstrated, and ptxA and ptxB, which encode PTS-related components, knockout of these genes impaired polysaccharide production in the extracellular matrix of biofilms and then impaired the biofilm formation [33]. SSU05_1638 and SSU05_0297 encode phosphoglycerate mutase, which has shown a downregulated transcription in our transcriptomics data, and previous studies have suggested that this is related to oxygen availability, given the lower oxygen utilization within the biofilm, this downregulation is also observed in S. aureus [34]. Oxaloacetate, α-ketoglutarate and succinyl-CoA are the key intermediates in the TCA cycle. Acetyl-CoA can be condensed with oxaloacetate to form citrate [35]. In contrast, citrate promoted the biofilm formation of S. aureus by inhibiting the production of polysaccharide adhesin and stimulating the interaction between bacteria and object surfaces [36]. SU05_2154 encodes succinate dehydrogenase/fumarate reductase in S. suis. In S. aureus, the sdhCAB gene encoding such enzyme shows a significant increase in transcription level when biofilm formation occurs [37]. The deletion of sdhCAB in S. aureus can cause the disruption of TCA cycle and lead to less NADH and FADH production under aerobic conditions. The concentration of NADH is directly related to the formation of biofilm, and the increase of NADH concentration is conducive to the transition of bacteria to biofilm state [37].

Through a combined analysis of transcriptomics and metabolomics, we found that there were significant differences in the nucleotide metabolic pathways between planktonic and biofilm state S. suis. Nucleotide metabolism plays a critical role in biofilm formation [38]. In the case of S. sanguinis, the deletion of the purB, which encodes the key enzyme for purine synthesis, adenylosuccinate lyase, greatly impaired biofilm formation and the virulence of S. sanguinis to cause endocarditis [39]. Cytidine 5’-diphosphate can be used to produce cytidine triphosphate, which is used in the synthesis of DNA and RNA [40]. The reduction of these nucleotides indirectly impacts the extracellular DNA (eDNA) content in biofilms, thereby compromising the biofilm formation. It has been reported that in S. aureus, the pgl gene mutation impaired the pathways related to pyrimidine metabolism, and related metabolites such as ribose-5p, UMP and GMP were reduced [41]. In S. aureus, S. epidermidis and S. pneumoniae, eDNA has been shown to play a role in maintaining the stability of the biofilm structure [42,43,44,45]. Additionally, inosine, another metabolic byproduct, exerts an influence on purine metabolism. Research findings indicate that inosine impedes the elevation of xanthine and guanosine levels, thereby promoting an increase in C-di-GMP concentration [46]. Consequently, this upsurge in C-di-GMP triggers the formation of biofilms.

The aminoacyl-tRNA biosynthesis pathway acts as a transport amino acid to promote protein synthesis, ensuring that gene sequences can be accurately transcribed and translated into proteins to maintain intracellular homeostasis [47]. Proteins play an important role in maintaining biofilm stability [48]. And the pathway L-Phenylalanine, Glycine differential metabolites were up-regulated, L-Cysteine differential metabolites were down-regulated and pheT, pheS, glyS, glyQ differential gene expression levels were down-regulated and cysS differential gene expression levels were up-regulated. The aminoacyl-tRNA biosynthesis pathway plays an important role in the formation of S. suis biofilm, but how it plays a role needs to be further investigated.

The vitamin and cofactor metabolisms involved in this study, as seen by co-analysis, were nicotinate and nicotinamide metabolism (NAD+), pantothenate and CoA biosynthesis Pantothenate and CoA biosynthesis (L-Cysteine). A significant up-regulation of metabolic NAD+ and L-Cysteine content was found. NAD+ is a vital coenzyme in cellular metabolism, particularly in oxidation-reduction reactions, playing a pivotal role [49]. The redox state of bacteria significantly influences the formation of diverse bacterial biofilms [50, 51]. In S. mutans, rex encodes glutamine transferase that regulates intracellular NADH/NAD+ levels [52]. As previously mentioned, the intracellular NADH/NAD+ ratio impacts bacterial biofilm formation [51]. The mleS in S. aureus encodes an NAD+ dependent malate dehydrogenase, which facilitates the synthesis of lactic acid and ATP under microaerobic conditions, the absence of mleS significantly hampers the S. aureus biofilm formation [53].

Multi-omics data analysis and cross-validation among multiple omics reduce the false positive results of a single omics, thereby helping to provide a more comprehensive understanding of the regulatory networks involved in the process of biofilm formation. While omics as a powerful tool provides researchers with new research directions and key clues at the macro level, the results presented by these omics still require researchers to conduct further work to verify them at the actual research level. The results obtained from this omics study also suggest that we should pay attention to the regulatory role of nucleic acid metabolism in the S. suis biofilm formation, which will be our focus for future follow-up studies. This study found that the formation of S. suis biofilm was mainly associated with amino acid metabolism, carbon metabolism, nucleotide metabolism, aminoacyl-tRNA biosynthesis, and vitamin and cofactor metabolism.

Conclusion

In summary, significant changes in transcription and metabolism of S. suis in the biofilm and planktonic states were observed. Combined with transcriptomic and metabolomic analyses, this study found that the formation of S. suis biofilm was mainly associated with amino acid metabolism, carbon metabolism, nucleotide metabolism, aminoacyl-tRNA biosynthesis, and vitamin and cofactor metabolism. However, further studies are needed to determine this and to elaborate on the exact underlying mechanisms of S. suis biofilm formation.

Data availability

The original contributions presented in the study are included in the Supplementary Information. Further inquiries can be directed to the corresponding authors. The datasets GSE217756 used in the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/gds/?term=GSE217756).

References

  1. Lun ZR, Wang QP, Chen XG, Li AX, Zhu XQ. Streptococcus suis: an emerging zoonotic pathogen. Lancet Infect Dis. 2007;7(3):201–9. https://doi.org/10.1016/S1473-3099(07)70001-4.

    Article  PubMed  Google Scholar 

  2. Wertheim HF, Nghia HD, Taylor W, Schultsz C. Streptococcus suis: an emerging human pathogen. Clin Infect Dis. 2009;48(5):617–25. https://doi.org/10.1086/596763.

    Article  PubMed  Google Scholar 

  3. Dechene-Tempier M, Jouy E, Bayon-Auboyer MH, Bougeard S, Chauvin C, Libante V, et al. Antimicrobial resistance profiles of Streptococcus suis isolated from pigs, wild boars, and humans in France between 1994 and 2020. J Clin Microbiol. 2023;61(9):e0016423. https://doi.org/10.1128/jcm.00164-23.

    Article  PubMed  CAS  Google Scholar 

  4. Liu Y, Kou Z, Wang X, Chen S, Li R, Wang Q. Case report: one human Streptococcus suis infection in Shandong Province, China. Med (Baltim). 2023;102(14):e33491. https://doi.org/10.1097/MD.0000000000033491.

    Article  Google Scholar 

  5. Wertheim HF, Nguyen HN, Taylor W, Lien TT, Ngo HT, Nguyen TQ, et al. Streptococcus suis, an important cause of adult bacterial meningitis in northern Vietnam. PLoS ONE. 2009;4(6):e5973. https://doi.org/10.1371/journal.pone.0005973.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. van Samkar A, Brouwer MC, Schultsz C, van der Ende A, van de Beek D. Streptococcus suis Meningitis: a systematic review and Meta-analysis. PLoS Negl Trop Dis. 2015;9(10):e0004191. https://doi.org/10.1371/journal.pntd.0004191.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Diaz-Munoz SL, Boddy AM, Dantas G, Waters CM, Bronstein JL. Contextual organismality: beyond pattern to process in the emergence of organisms. Evolution. 2016;70(12):2669–77. https://doi.org/10.1111/evo.13078.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Costerton JW, Lewandowski Z, Caldwell DE, Korber DR, Lappin-Scott HM. Microbial biofilms. Annu Rev Microbiol. 1995;49:711–45. https://doi.org/10.1146/annurev.mi.49.100195.003431.

    Article  PubMed  CAS  Google Scholar 

  9. Meng X, Chen Y, Wang P, He M, Shi Y, Lai Y, et al. RyhB in avian pathogenic Escherichia coli regulates the expression of virulence-related genes and contributes to Meningitis Development in a mouse model. Int J Mol Sci. 2022;23(24). https://doi.org/10.3390/ijms232415532.

  10. Novotny LA, Goodman SD, Bakaletz LO. Redirecting the immune response towards immunoprotective domains of a DNABII protein resolves experimental otitis media. NPJ Vaccines. 2019;4:43. https://doi.org/10.1038/s41541-019-0137-1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Zhang T, Liu Z, Wang H, Zhang H, Li H, Lu W, et al. Multi-omics analysis reveals genes and metabolites involved in Bifidobacterium pseudocatenulatum biofilm formation. Front Microbiol. 2023;14:1287680. https://doi.org/10.3389/fmicb.2023.1287680.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yuan L, Fan L, Dai H, He G, Zheng X, Rao S, et al. Multi-omics reveals the increased biofilm formation of Salmonella Typhimurium M3 by the induction of tetracycline at sub-inhibitory concentrations. Sci Total Environ. 2023;899:165695. https://doi.org/10.1016/j.scitotenv.2023.165695.

    Article  PubMed  CAS  Google Scholar 

  13. Huang W, Wang Y, Tian W, Cui X, Tu P, Li J, et al. Biosynthesis investigations of Terpenoid, Alkaloid, and Flavonoid Antimicrobial agents Derived from Medicinal plants. Antibiot (Basel). 2022;11(10). https://doi.org/10.3390/antibiotics11101380.

  14. Jiang R, Xiang M, Chen W, Zhang P, Wu X, Zhu G, et al. Biofilm characteristics and transcriptomic analysis of Haemophilus parasuis. Vet Microbiol. 2021;258:109073. https://doi.org/10.1016/j.vetmic.2021.109073.

    Article  PubMed  CAS  Google Scholar 

  15. Leggett A, Li DW, Bruschweiler-Li L, Sullivan A, Stoodley P, Bruschweiler R. Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics. Sci Rep. 2022;12(1):17317. https://doi.org/10.1038/s41598-022-22127-x.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Wang Y, Wang Y, Liu B, Wang S, Li J, Gong S, et al. Pdh modulate virulence through reducing stress tolerance and biofilm formation of Streptococcus suis serotype 2. Virulence. 2019;10(1):588–99. https://doi.org/10.1080/21505594.2019.1631661.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Liu Z, Li L, Fang Z, Lee Y, Zhao J, Zhang H, et al. Integration of Transcriptome and Metabolome reveals the genes and metabolites involved in Bifidobacterium bifidum Biofilm formation. Int J Mol Sci. 2021;22(14). https://doi.org/10.3390/ijms22147596.

  18. Karygianni L, Ren Z, Koo H, Thurnheer T. Biofilm Matrixome: Extracellular Components in Structured Microbial communities. Trends Microbiol. 2020;28(8):668–81. https://doi.org/10.1016/j.tim.2020.03.016.

    Article  PubMed  CAS  Google Scholar 

  19. Fux CA, Costerton JW, Stewart PS, Stoodley P. Survival strategies of infectious biofilms. Trends Microbiol. 2005;13(1):34–40. https://doi.org/10.1016/j.tim.2004.11.010.

    Article  PubMed  CAS  Google Scholar 

  20. Sadiq FA, Yan BW, Zhao JX, Zhang H, Chen W. Untargeted metabolomics reveals metabolic state of in the biofilm and planktonic states. Lwt-Food Sci Technol. 2020;118; doi: ARTN 10877210.1016/j.lwt.2019.108772.

  21. Kolodkin-Gal I, Romero D, Cao S, Clardy J, Kolter R, Losick R. D-amino acids trigger biofilm disassembly. Science. 2010;328(5978):627–9. https://doi.org/10.1126/science.1188628.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Malviya J, Alameri AA, Al-Janabi SS, Fawzi OF, Azzawi AL, Obaid RF, et al. Metabolomic profiling of bacterial biofilm: trends, challenges, and an emerging antibiofilm target. World J Microbiol Biotechnol. 2023;39(8):212. https://doi.org/10.1007/s11274-023-03651-y.

    Article  PubMed  CAS  Google Scholar 

  23. Liu J, Shen Z, Tang J, Huang Q, Jian Y, Liu Y, et al. Extracellular DNA released by glycine-auxotrophic Staphylococcus epidermidis small colony variant facilitates catheter-related infections. Commun Biol. 2021;4(1):904. https://doi.org/10.1038/s42003-021-02423-4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Alvarez L, Aliashkevich A, de Pedro MA, Cava F. Bacterial secretion of D-arginine controls environmental microbial biodiversity. ISME J. 2018;12(2):438–50. https://doi.org/10.1038/ismej.2017.176.

    Article  PubMed  CAS  Google Scholar 

  25. Leiman SA, May JM, Lebar MD, Kahne D, Kolter R, Losick R. D-amino acids indirectly inhibit biofilm formation in Bacillus subtilis by interfering with protein synthesis. J Bacteriol. 2013;195(23):5391–5. https://doi.org/10.1128/JB.00975-13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. McBain AJ, O’Neill CA, Amezquita A, Price LJ, Faust K, Tett A, et al. Consumer Safety considerations of skin and oral Microbiome Perturbation. Clin Microbiol Rev. 2019;32(4). https://doi.org/10.1128/CMR.00051-19.

  27. Wang K, Lu C. Adhesion activity of glyceraldehyde-3-phosphate dehydrogenase in a Chinese Streptococcus suis type 2 strain. Berl Munch Tierarztl Wochenschr. 2007;120(5–6):207–9.

    PubMed  CAS  Google Scholar 

  28. Shin JH, Lee HW, Kim SM, Kim J. Proteomic analysis of Acinetobacter baumannii in biofilm and planktonic growth mode. J Microbiol. 2009;47(6):728–35. https://doi.org/10.1007/s12275-009-0158-y.

    Article  PubMed  CAS  Google Scholar 

  29. Jomaa M, Kyd JM, Cripps AW. Mucosal immunisation with novel Streptococcus pneumoniae protein antigens enhances bacterial clearance in an acute mouse lung infection model. FEMS Immunol Med Microbiol. 2005;44(1):59–67. https://doi.org/10.1016/j.femsim.2004.12.001.

    Article  PubMed  CAS  Google Scholar 

  30. Yi L, Wang Y, Ma Z, Zhang H, Li Y, Zheng JX, et al. Biofilm formation of Streptococcus equi ssp. zooepidemicus and comparative proteomic analysis of biofilm and planktonic cells. Curr Microbiol. 2014;69(3):227–33. https://doi.org/10.1007/s00284-014-0574-z.

    Article  PubMed  CAS  Google Scholar 

  31. Wang Y, Yi L, Wu Z, Shao J, Liu G, Fan H, et al. Comparative proteomic analysis of Streptococcus suis biofilms and planktonic cells that identified biofilm infection-related immunogenic proteins. PLoS ONE. 2012;7(4):e33371. https://doi.org/10.1371/journal.pone.0033371.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Wang Y, Wang YX, Li JP, Gong SL, Sun LY, Grenier D et al. Is involved in the cell division and normal septation of. Microbiol Res. 2019;228; doi: ARTN 126304 https://doi.org/10.1016/j.micres.2019.126304

  33. Wu X, Hou J, Chen X, Chen X, Zhao W. Identification and functional analysis of the L-ascorbate-specific enzyme II complex of the phosphotransferase system in Streptococcus mutans. BMC Microbiol. 2016;16:51. https://doi.org/10.1186/s12866-016-0668-9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Becker P, Hufnagle W, Peters G, Herrmann M. Detection of differential gene expression in biofilm-forming versus planktonic populations of Staphylococcus aureus using micro-representational-difference analysis. Appl Environ Microbiol. 2001;67(7):2958–65. https://doi.org/10.1128/AEM.67.7.2958-2965.2001.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Willenborg J, Huber C, Koczula A, Lange B, Eisenreich W, Valentin-Weigand P, et al. Characterization of the pivotal carbon metabolism of Streptococcus suis serotype 2 under ex vivo and chemically defined in vitro conditions by isotopologue profiling. J Biol Chem. 2015;290(9):5840–54. https://doi.org/10.1074/jbc.M114.619163.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Dobinsky S, Kiel K, Rohde H, Bartscht K, Knobloch JK, Horstkotte MA, et al. Glucose-related dissociation between icaADBC transcription and biofilm expression by Staphylococcus epidermidis: evidence for an additional factor required for polysaccharide intercellular adhesin synthesis. J Bacteriol. 2003;185(9):2879–86. https://doi.org/10.1128/JB.185.9.2879-2886.2003.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Gaupp R, Schlag S, Liebeke M, Lalk M, Gotz F. Advantage of upregulation of succinate dehydrogenase in Staphylococcus aureus biofilms. J Bacteriol. 2010;192(9):2385–94. https://doi.org/10.1128/JB.01472-09.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Yee R, Cui P, Shi W, Feng J, Zhang Y. Genetic screen reveals the role of Purine Metabolism in Staphylococcus aureus Persistence to Rifampicin. Antibiot (Basel). 2015;4(4):627–42. https://doi.org/10.3390/antibiotics4040627.

    Article  Google Scholar 

  39. Ge X, Kitten T, Chen Z, Lee SP, Munro CL, Xu P. Identification of Streptococcus sanguinis genes required for biofilm formation and examination of their role in endocarditis virulence. Infect Immun. 2008;76(6):2551–9. https://doi.org/10.1128/IAI.00338-08.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Danchin A, Ouzounis C, Tokuyasu T, Zucker JD. No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects. Microb Biotechnol. 2018;11(4):588–605. https://doi.org/10.1111/1751-7915.13284.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kim J, Kim GL, Norambuena J, Boyd JM, Parker D. Impact of the pentose phosphate pathway on metabolism and pathogenesis of Staphylococcus aureus. PLoS Pathog. 2023;19(7):e1011531. https://doi.org/10.1371/journal.ppat.1011531.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. DeFrancesco AS, Masloboeva N, Syed AK, DeLoughery A, Bradshaw N, Li GW, et al. Genome-wide screen for genes involved in eDNA release during biofilm formation by Staphylococcus aureus. Proc Natl Acad Sci U S A. 2017;114(29):E5969–78. https://doi.org/10.1073/pnas.1704544114.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Schilcher K, Horswill AR. Staphylococcal Biofilm Development: structure, regulation, and treatment strategies. Microbiol Mol Biol Rev. 2020;84(3). https://doi.org/10.1128/MMBR.00026-19.

  44. Wang B, Zhan Q, Xiao Y, Xu Y, Zhao H, Rao L, et al. Mupirocin enhances the biofilm formation of Staphylococcus epidermidis in an atle-dependent manner. Int J Antimicrob Agents. 2023;62(4):106904. https://doi.org/10.1016/j.ijantimicag.2023.106904.

    Article  PubMed  CAS  Google Scholar 

  45. Domenech M, Ruiz S, Moscoso M, Garcia E. In vitro biofilm development of Streptococcus pneumoniae and formation of choline-binding protein-DNA complexes. Environ Microbiol Rep. 2015;7(5):715–27. https://doi.org/10.1111/1758-2229.12295.

    Article  PubMed  CAS  Google Scholar 

  46. Kennelly C, Tran P, Prindle A. Environmental purines decrease Pseudomonas aeruginosa biofilm formation by disrupting c-di-GMP metabolism. Cell Rep. 2024;43(5):114154. https://doi.org/10.1016/j.celrep.2024.114154.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Francklyn CS, Mullen P. Progress and challenges in aminoacyl-tRNA synthetase-based therapeutics. J Biol Chem. 2019;294(14):5365–85. https://doi.org/10.1074/jbc.REV118.002956.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Mortensen NP, Mercier KA, McRitchie S, Cavallo TB, Pathmasiri W, Stewart D, et al. Microfluidics meets metabolomics to reveal the impact of Campylobacter jejuni infection on biochemical pathways. Biomed Microdevices. 2016;18(3):51. https://doi.org/10.1007/s10544-016-0076-9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Covarrubias AJ, Perrone R, Grozio A, Verdin E. NAD(+) metabolism and its roles in cellular processes during ageing. Nat Rev Mol Cell Biol. 2021;22(2):119–41. https://doi.org/10.1038/s41580-020-00313-x.

    Article  PubMed  CAS  Google Scholar 

  50. Gomez-Carretero S, Libberton B, Rhen M, Richter-Dahlfors A. Redox-active conducting polymers modulate Salmonella biofilm formation by controlling availability of electron acceptors. NPJ Biofilms Microbiomes. 2017;3:19. https://doi.org/10.1038/s41522-017-0027-0.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Prasad UV, Vasu D, Gowtham RR, Pradeep CK, Swarupa V, Yeswanth S, et al. Cloning, expression and characterization of NAD Kinase from Staphylococcus aureus involved in the formation of NADP (H): a key molecule in the maintaining of Redox Status and Biofilm formation. Adv Biomed Res. 2017;6:97. https://doi.org/10.4103/2277-9175.211833.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Bitoun JP, Liao S, Yao X, Xie GG, Wen ZT. The redox-sensing regulator Rex modulates central carbon metabolism, stress tolerance response and biofilm formation by Streptococcus mutans. PLoS ONE. 2012;7(9):e44766. https://doi.org/10.1371/journal.pone.0044766.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Chen F, Yin Y, Chen H, Li S, Yin G, Wang H. mleS in Staphylococcus aureus contributes to Microaerobic metabolic activity, abscess formation, and Survival in macrophages. Microbiol Spectr. 2023;11(3):e0090923. https://doi.org/10.1128/spectrum.00909-23.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China (32172852), Excellent Youth Foundation of Henan Scientific Committee (222300420005), Henan Provincial Science and Technology Research Project (232102110095), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (24IRTSTHN033) and Key Scientific Research Projects of Universities in Henan Province (24A230013).

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  1. Haikun Wang, Qingying Fan these authors contributed equally to this study.

Authors and Affiliations

  1. College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, 471000, China

    Haikun Wang, Qingying Fan, Yuxin Wang & Yang Wang

  2. Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China

    Haikun Wang, Qingying Fan, Yuxin Wang, Li Yi & Yang Wang

  3. College of Life Science, Luoyang Normal University, Luoyang, 471934, China

    Li Yi

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Yang Wang and Li Yi designed the concept of the research article. Haikun Wang, Qingying Fan and Yuxin Wang contributed to writing the manuscript. Yang Wang and Li Yi critically read and corrected the manuscript. All authors reviewed the manuscript.

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Wang, H., Fan, Q., Wang, Y. et al. Multi-omics analysis reveals genes and metabolites involved in Streptococcus suis biofilm formation. BMC Microbiol 24, 297 (2024). https://doi.org/10.1186/s12866-024-03448-5

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BMC Microbiology

ISSN: 1471-2180

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