PJZ_57_6_2707-2714

Introduction

A Streptococcus bovis/Streptococcus equinus complex (SBSEC) of domesticated animals, particularly cows and horses, is a non-enterococcal group D Streptococcus spp. complex. It is composed of 7 (sub) species. For example, S. bovis can cause acute ruminal acidosis and bloat in cows and is often associated with mastitis. (Pompilio and Di Bonaventura, 2019). In spite of the lack of precise numbers, it is generally recognized that SBSEC infection of cattle results in significant losses, nearly a billion dollars annually (Herrera et al., 2009). As an emerging pathogen that causes infectious endocarditis in humans and is strongly associated with colorectal cancer, SBSEC is receiving increasing attention from scientists (Kaiki et al., 2021; Öberg and Nilson, 2022).

S. mutans has a quorum sensing (QS) system that consists primarily of a signal peptide and the ComDE two-component regulatory system. Its QS signal is a 21-amino-acid peptide pheromone called the competence stimulating peptide (CSP) (Asanuma et al. 2010). In the extracellular environment, this peptide accumulates from CSP precursors (encoded by comC). CSP stimulates the sensor histidine kinase (ComD) and the response regulator (ComE) when its concentration reaches a critical threshold (Suntharalingam and Cvitkovitch, 2005). It has been found that the CSP-dependent QS system regulates a variety of physiological activities in S mutans, including bacteriocin productions, competence development, formation of biofilms, and stress response (OmerOglou et al., 2022).

Yang and Tal-Gan (2019) revealed developing novel streptococcus QS modulators with higher potency and improved pharmacological properties has been possible by identifying structural features that are optimal to achieve receptor ComD activation and understanding the CSP:ComD interaction. New generations of antibacterial agents may be able to treat Streptococcus diseases by modulating Streptococcus QS through intercepting CSP: ComD interactions. Based on this advancement, it is reported that 7S globulin 3 derived from the adzuki bean (Senpuku et al., 2019), sodium new houttuyfonate (Shui et al., 2019) and fructanase (Suzuki et al., 2017) had inhibiting effects on competence-stimulating peptide-dependent QS system in S. mutans. The deletion of entire comC and two-thirds of comD reduced growth rate, which may be related to changes in protein expression (Asanuma et al., 2004). However, the effect of comC deletion on the metabolism of the cells has not been elucidated till now.

Therefore, the objectives of the present study were to investigate the metabolic changes operating in comC knockout Sreptococcus bovis JB1 strain by employing metabolomics. The results of this study will contribute to a comprehensive and in-depth understanding of QS regulatory mechanism, and possibly provide preventative methods for tackling S. bovis biofilms.

Materials and methods

Sources of Streptococcus bovis and culture conditions

The S. bovis JB1 was screened from a native beef cattle (Xuanhan Yellow Cattle) and identified previously by our team. The diet given in Supplementary Table S1 was presented to the cattle. S. bovis was chronically grown in our laboratory. Briefly, the medium contained (g/L): casein peptone, 10.0; beef extract 10.0; yeast extract, 5.0; glucose, 5.0; sodium acetate, 2.0; Tween 80, 1.0; K2HPO4, 2.0; MgSO47H2O, 0.2; MgSO4H2O, 0.05; CaCO3, 20.0; agar, 15.0. The pH of culture incubations was maintained between 6.8~7.0.

Construction of a comC-disrupted mutant of S. bovis

A comC-disrupted mutant (ΔcomC) was constructed as described previously BY Asanuma et al. (2004). The upper and lower regions of comC were amplified by PCR and blunt ended by T4 DNA polymerase (TaKaRa, Dalian, China). With the help of DNA ligase T4 (TaKaRa, Dalian, China), ermB, the erythromycin resistance gene, was inserted between the upper and down regions of comC. The plasmid pUC18 (TaKaRa, Dalian, China) was used as the carrier of ligated product. Using an electroporated plasmid, the recombinant plasmid was transferred to S. bovis strain JB1. Transformants were finally selected with erythromycin (10 μg/mL). All restriction enzymes and T4 DNA ligase were obtained from Takara Biotechnology (Dalian, China).

Quantification of metabolome changes using GC-MS

Harvested cells were at the mid-exponential phase (OD600=0.7). The pre-treatment method of microbiology was defined by (Smart et al., 2010). A gas chromatography-mass spectrometry (GC–MS) apparatus was used with 1 µL samples (with Inert MSD: 7890A, 5975C, 7693 autosamplers) with a phenylmethylsilicone 5% capillary column, 30m×0.250mm×0.25μm. With the injector set to 280°C, splitless injection was performed. The column oven temperature was programmed at 70°C for 2 min, then 30°C at 10°C/min for 5 min. Five ions were monitored for each analyte by electronic energy 70 ev identification: 73, 101, 148, 203, 204 for levamisole, 56, 91, 118, 145, and 162 for aminorex, 73, 91, 162, 291, and 306 for bis-trimethylsilyl aminorex, and 72, 148, and 91 for mephentermine. Analyses were also performed in scan mode under the same chromatographic conditions.

Statistical analysis

An analysis of multivariate data was performed in accordance with Qiu et al. (2016). Simca-P1 v 12.0 (Umetrics, Sweden) was used to validate the models using a seven-fold cross-validation method using the partial least square discriminant (PLS-DA) with Unit Variance scaling. Using CV-ANOVA, the significance of the PLS-DA model was verified. We assessed the importance of each metabolite in the PLS-DA based on variable importance in projections (VIP).

Results

Growth curves of Streptococcus bovis JB1 and ΔcomC mutant

The growth of S. bovis was monitored via measuring the optical density at 600 nm (OD600). S. bovis was harvested till the late exponential growth phase. The OD value of ΔcomC mutant (KO) at growth cessation was lower than that of the wild type (WT). The ΔcomC mutant reach the growth plateau was about 30 min later than the wild type (Fig. 1). Subsequently, the bacterial cells were harvested and metabolomic tool was applied, and LC/MS (Agilent 7890A) platform were used in determining the intracellular metabolites.

Classification of annotated metabolites

Through the detection, the total ion chromatogram (TIC) of each group of typical samples is shown in Supplementary Fig. S1. From the total ion chromatogram, the samples of the two groups were different. A total of 140 peaks were detected, however according to existing databases and standards, of which only a total of 78 substances were annotated, the details can be seen in supplementary material “Metabolome.xlsx”. These substances were mainly primary metabolites, the specific classification as shown in Supplementary Figure S2.

Principal component analysis (PCA)

The PCA scores plot of the two groups was presented in Figure 2A. All of the sample plots were in Hotelling T2 ellipse (95%). The KO group could be clearly distinguished from WT group. Two principal components were obtained, and the parameters of the model for the two groups was R2X=0.756, Q2=0.546. In addition, supervised clustering method PLS-DA was also used to classify the two groups. Model was established for the first and second principal components after unit variable scaling was applied. We can see that the clustering result was similar with PCA scores plot (Fig. 2B). The KO group can be clearly differentiated with WT group. The model was tested by leave-one-out cross validation, and the model cumulative explanation rate parameters was R2Y=0.997, Q2=0.973. The R2Y=0.997 meant the fit goodness of the model was high, and the Q2=0.973 meant the predictive ability of the model was strong. Permutation test result indicated that the intercept of R2 on the Y axis was 0.613 and the intercept of Q2 on the Y axis was -0.0424. This meant that the model did not have excessive fitting (Fig. 2C).

Variable importance in projections (VIP)

Figure 3 shows a VIP plot of the PLS-DA of the KO and WT groups, in which the metabolites were ranked according to their importance in discriminating KO and WT. Using VIP plots, the top 15 important metabolites were shown. A higher VIP value indicates a greater contributionto the difference between WT and KO. The VIP plots indicated that trehalose, proline, 2-hydroxyglutaric acid, valine, leucine, isoleucine, tyramine, 1, 3-Di-tert-butylbenzene, putrescine, oxalic acid, glycine, heptanoic acid, pipecolic acid, aspartic acid and ornithine were the strongest discriminating metabolites for separating KO and WT group. The heatmap on the right side of the VIP plots indicated that 8 (i.e., trehalose, 2-hydroxyglutaric acid, tyramine, 1,3-Di-tert-butylbenzene, putrescine, oxalic acid, glycine and aspartic acid) out of 15 metabolites were increased while 7 metabolites (proline, valine, leucine, isoleucine, heptanoic acid, pipecolic acid and ornithine) were decreased.

Pathways

The top 6 affected pathways of KO group compared with wild type were glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, pyruvate metabolism, cysteine and methionine metabolism, glutathione metabolism, arginine and proline metabolism (Table II, Fig. 4). The alteration of pathways was caused by the change of metabolites reflected in Table I.

Table I. Different expressed metabolites of intracellular content of Sreptococcus bovis JB1 wild type (WT) and ΔcomC mutant (KO).

No.	Compounds	KO vs. WT
No.	Compounds	FC	P-value
1	Trehalose	96.313	<0.001
2	Mannitol	4.927	<0.001
3	Glucose	4.163	<0.001
4	Monomethylphosphate	4.072	<0.001
5	Sucrose	3.941	0.003
6	Glycolic acid	3.922	0.004
7	Glycine	3.686	<0.001
8	Hydroxylamine	2.829	<0.001
9	Nonadecanoic acid	2.729	<0.001
10	Putrescine	2.591	<0.001
11	2-Hydroxyglutaric acid	2.586	<0.001
12	Oxalic acid	2.581	<0.001
13	Urea	2.502	0.028
14	1,3-Di-tert-butylbenzene	2.485	<0.001
15	2,4,6-Tri-tert.-butylbenzenethiol	2.459	<0.001
16	Serine	2.435	<0.001
17	Citric acid	2.416	<0.001
18	Heptanoic acid	2.409	<0.001
19	Pyruvic acid	2.391	<0.001
20	Dodecanoic acid	2.349	<0.001
21	Asparagine	2.349	<0.001
22	Hexadecanol	2.325	<0.001
23	Benzoic acid	2.206	<0.001
24	Ribose	2.079	0.003
25	Octadecanol	2.070	<0.001
26	Nonanoic acid	2.034	<0.001
27	Pipecolic acid	0.499	<0.001
28	Valine	0.403	<0.001
29	Isoleucine	0.344	<0.001
30	9-Z-Octadecenoic acid	0.326	0.020
31	Leucine	0.308	<0.001
32	9-Z-Hexadecenoic acid	0.296	0.018
33	Cysteine	0.243	<0.001
34	Glutamine	0.235	<0.001
35	Ornithine	0.234	<0.001
36	Tyramine	0.199	<0.001
37	Proline	0.181	<0.001

All different metabolites listed here are those VIP>1, fold change >2 or <0.5 and P value<0.05.

Table II. Significant different metabolites that enriched in the pathways of intracellular content obtained from Sreptococcus bovis JB1 wild type and ΔcomC mutant.

Pathway	Total Cmpd	Hits	Raw p	-log (p)	Holm adjust	FDR p	Impact
Glyoxylate and dicarboxylate metabolism	15	3	1.52E-04	8.79E+00	2.13E-03	2.10E-04	0.50
Alanine, aspartate and glutamate metabolism	18	7	3.26E-10	2.18E+01	1.43E-08	3.82E-09	0.45
Pyruvate metabolism	20	3	3.32E-04	8.01E+00	4.32E-03	4.46E-04	0.34
Cysteine and methionine metabolism	25	6	5.77E-10	2.13E+01	2.48E-08	5.42E-09	0.20
Glutathione metabolism	13	3	2.67E-10	2.20E+01	1.22E-08	3.82E-09	0.17
Arginine and proline metabolism	30	8	9.02E-09	1.85E+01	3.43E-07	4.24E-08	0.17

Discussion

The glycosidic bond between two molecules of glucose makes trehalose a non-reducing disaccharide. As a typical stress metabolite, trehalose can form a protective film on cell surfaces when exposed to harsh environmental conditions, including high temperature, high cold, high osmotic pressure, drying, and water loss. In this way, the biomolecular structure is effectively protected from destruction, and the living body’s life process and biological characteristics are preserved (Câmara et al., 2019; Izanloo et al., 2021; Kokina et al., 2022; Wei et al., 2022). Câmara et al. (2019) reported that cells naturally enriched in trehalose or glutathione acquired resistance to dehydration, preventing the oxidation of glutathione. In this study, the concentration of trehalose in the ΔcomC mutant was increased by 96-fold, which was the most surprising finding of this study. This verified that trehalose was indeed a stress metabolite. Our KEGG result showed that the glutathione pathway was also significantly enriched. The reduced glutathione form is metabolized in multiple ways, leading to glutamate, cysteine, and glycine biosynthesis (Koga et al., 2011), therefore the concentration of glycine and cysteine, and the glutamate metabolism pathway was changed in the ΔcomC mutant when compared with the wild type. Bacteria alter growth and biofilm formation in the face of stress, and biofilm is the way of bacteria to resist stress (Peterson et al., 2015). Polysaccharides, lipids, adhesive proteins, and secreted extracellular DNA make up the biofilm matrix (Hobley et al., 2015). In this experiment, we observed a 4-fold increase in mannitol concentration under of comC knockout stress conditions. The mechanism of mannitol production has been elaborated in previous studies (Hu et al., 2018). Taken together, under the condition of comC knockout stress, the synthesis and secretion of trehalose and mannitol were increased.

A 4-fold increase in glucose concentration was observed in our study, and previous studies have shown that E. coli could enhance its tolerance by using exogenous glucose under conditions of ofloxacin stress. When glucose was consumed, its growth rate decreased (Amato et al., 2013). In addition, the glyoxylate and dicarboxylate metabolism plays a very important role in the energy metabolism in many fungi as a bypass for the tricarboxylic acid cycle (Padilla-Guerrero et al., 2011). The enriched glyoxylate and dicarboxylate metabolism pathway may help producing more glucose. Therefore, high glucose production and utilization may be one of the mechanisms by which S. bovis resist various stress.

Usually, amino acids are decomposed by decarboxylation groups to form various amines. For example, glycine, ornithine, arginine, histidine and tyrosine are degraded to form methylamine, putrescine, histamine, tyramine and other putrefactive amines, which are toxic to the biological environment (de las Rivas et al., 2006; Henao-Escobar et al., 2015). In this experiment, the concentration of hydroxylamine, putrescine, tyramine was changed when the comC was knockout. The putrescine produced by bacteria are mainly via ornithine decarboxylase or agmatine deiminase pathway (Ahmad et al., 2020), which was attributed to the decrease of ornithine. It is reported that 2-hydroxyglutaric acid bind and inhibit ATP synthase and mTOR signaling and showed a growth-suppressive function (Fu et al., 2015). The 2-fold increase of 2-hydroxyglutaric acid accounted for the decreased growth of ΔcomC mutant observed in this study. Pyruvate was reported to enable the proliferation of RC-deficient cells and increased the content of aspartate (Chen et al., 2016), which was in agreement with our study. The enriched pyruvate metabolism pathway in our study may be a compensatory mechanism for inhibited cell growth.

The branched amino acids were all decreased when the comC was knockout. The amount of amino acids (valine, leucine, isoleucine, threonine, arginine, glutamate, phenylalanine) decreased significantly when sea cucumber, Apostichopus japonicus facing high temperature stress (above 25 oC) (Shao et al., 2015), and the metabolomic profiles of budding yeast cells were consistent with these observations: pyruvic acid accumulation, TCA cycle intermediate accumulation, and branched chain amino acid depletion (Kamei et al., 2014). The massive oxidation of these branched amino acids may also be intended to maintain normal metabolism of cells under stressful conditions.

Oxidative stress is particularly damaging to the sulfur-containing amino acids cysteine and methionine (Ezraty et al., 2017), and sulfur-containing amino acids are used as substrates for oxidative stress, thereby the concentration of cysteine was declined, and the glutathione metabolism pathway was enriched. Mitochondria use proline and arginine as metabolic fuels (Misener et al., 2001). In Bactrocera dorsalis, gut microbiota contribute significantly to its resistance to low-temperature stress by stimulating arginine and proline metabolism (Raza et al., 2020). Therefore, the lowered proline concentration was oxidized to ward off comC knockout stress.

Conclusions

After the knockout of comC, the S. bovis intracellular metabolites undergo dramatic changes, among which the trehalose increased by 96 times, glucose and mannitol, etc. increased by 4 times, and putrescine and 2-hydroxyglutaric acid increased by 2 times, whereas valine, leucine, isoleucine, proline and cysteine etc. were dramatically reduced. Based on the KEGG results, the metabolisms of glyoxylates and dicarboxylates, aspartates, glutamates, pyruvates, cysteines, and methionines, glutathione, and arginine were enriched. S. bovis promotes energy metabolism and secrets huge amounts of trehalose and mannitol to form biofilm to resists oxidative stress after comC knockout. The synthetic pathway of trehalose can be used as a drug target for the prevention or treatment of S. bovis disease.

dECLARATIONS

Acknowledgement

Thanks to the Associate Professor Zhang Xiangfei from the Sichuan Grassland Science Research Institute for the guidance and assistance during the comC gene knockout process.

Funding

The financial support was provided by the National Natural Science Foundation of China (31402104, 31802086) and Sichuan Provincial Natural Science Foundation (2022NSFSC0064).

IRB approval

This study was approved by Animal Nutrition Institute Review Board 2023.

Supplementary material

There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20231209141813

Statement of conflict of interest

The authors have declared no conflict of interest.

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