Adaptive metabolism of Staphylococcus aureus revealed by untargeted metabolomics

Abstract

Staphylococcus aureus (SA) is an opportunistic pathogen that can cause a wide spectrum of infections, from superficial skin inflammation to severe, and potentially fatal, invasive diseases. Due to the many pottential routes of infection, host derived environmental signals (oxygen availability, nutrients, etc) are vital for host colinzation and thus contribute to SA’s pathogenesis. To uncover the direct effects of environmental factors on SA metabolism, we performed a series of experiments in diverse culture environments and correlated our findings of SA’s metabolic adaptation to some of the pathogen’s known virulence factors. Untargeted metabolomics was conducted on a Thermo Q-Exactive^™ high-resolution mass spectrometer (HRMS). We detected 260 intracellular polar metabolites from our bacteria cultured in both aerobic and anaerobic conditions, as well as glucose and dextrin supplemented cultures. These metabolites were mapped to relevant metabolic pathways to elucidate the adaptive metabolic processes of both methicillin sensitive SA (MSSA) and methicillin resistant SA (MRSA). We also detected increased expression of virulence genes agr-I and sea of MRSA supplemented with both glucose and dextrin by qPCR. With the metabolic data collected that may be associated with the adaptive growth and virulence of SA, our study could set up the foundations for future work to identify metabolic inhibitors/modulators to mitigate SA infections in different growth environments.

Graphical Abstract

1. Introduction:

Staphylococcus aureus (SA) is a Gram-positive commensal microbe that asymptomatically colonizes about 30% of the human population¹. It is widely regarded as an opportunistic pathogen that can cause a wide spectrum of infections, from superficial skin infections to severe, and potentially fatal, invasive diseases². Host derived environmental signals are vital for SA colinzation and thus contribute to SA’s transition from commensal strain to pathogen³. SA quickly acquired resistance to antibiotics after treatments were provided in clinical practice⁴, and subsequently dubbed as methicillin resistant staphylococcus aureus (MRSA). Once colonized, SA demonstrates its adaptive metabolic processes by rewiring central carbon metabolism to consume available host-dervied food products and utilize them as their own energy substrates⁵.

In addition, manifestaion of SA disease is advanced by expression of virulence factors⁶, which is directly influenced by the metabolic state of the strain⁷. SA’s metabolic flexibility is unique as it can utilize both glycolysis and pentose phosphate pathways for carbohydrate metabolism⁸, as well as synthesize a majority of amino acids which can be used to drive cellular processes⁹. Metabolic regulators of central carbon metabolism¹⁰, as well amino acid metabolism¹¹, have been shown to be upregulated in the presence of glucose, altering carbon flux while simultaneously enhancing virulence¹². Recently, stress-induced impairment to purine biosynthesis has also been shown to manifest as a hypervirulence in the USA300 strain of SA¹³. The fact that transcription of virulence factors such as RNAIII, a primary output of the agr quorum sensing two-component system, responds to the available concetration of glucose¹⁴, highlights that SA must maintain adequate metabolite pools to persist in survival and promote virulence¹⁵.

While extensive research efforts have attempted to compile a wealth of data in regards to the regulation, function, and structure of various virulence factors and of proteins involved in antibiotic resistance^{16, 17}, only a few studies have focused on the flexible adaptive metabolism of this pathogen which is utilized to not only to maintain cellular functions and survival but enhance pathogenicity as well¹⁸. Our recent data has demonstrated SA’s adaptive metabolism when subjected to treatment by methicillin in the presence of glucose¹⁹, and subsequently using various antibiotic classes²⁰. The objective of this study was to uncover the intracellular metabolic changes exhibited by both pathogenic MRSA and methicillin sensitive Staphylococcus aereus (MSSA) when cultured in various environmental conditions (with the focus on the availability of oxygen and carbon sources). We first aimed to replicate oxygen deprived conditions synonymous with human large intestinal environment; then investigated whether different energy substrates that simulate the major carbonhydrates from host diets can alter SA metabolism. These metatbolic responses to environmental changes were also analyzed for correlations between metabolites and virulence factors. Collectively, we believe this study will provide extensive metabolic data that support the metabolic adaptations of SA in various growth conditions, and pave the way for future in vivo studies that further examinate SA pathogenesis.

2. Materials and methods:

2.1. Bacterial strains and growth conditions

Isogenic strains of MSSA RN450 and MRSA 450M were used in this study. These isogenic strains only differ in the expression of the antibiotic-resistant genes (Staphylococcal cassette chromosome mecA)²⁰. Both strains were first cultured overnight in modified Gifu anaerobic medium (mGAM) (HiMedia, West Chester, PA, USA), which was selected due to its reduced glucose concentration compared to customarily used Gifu anaerobic medium (GAM) (0.5g compared to 3g, respectively). Full compositions of both culture media are compared in supplemental table 1. To investigate the impact of oxygen levels on SA metabolism, the bacteria cultures were maintained for 6 hours at 37°C aerobically in a Thermo Fisher incubator (Thermo Fisher Scientific, Pittsburgh, PA, USA); or anaerobically in a Coy Laboraties anaerobic chamber (Coy Lab, Flint, Michigan, USA). Oxygen levels throughout the anaerobic culture period ranged between 10–20 ppm. For the comparison of two types of carbon sources on S. aureus metabolism, bacteria was inoculated into baseline mGAM culture media supplemented with either 1% w/v of glucose or dextrin (Thermo Fisher Scientific, Pittsburgh, PA, USA).

Final optical density (OD600) of bacterial cultures was measured using an ELx808 absorbance plate reader (BioTek, Winooski, VT, USA) to estimate growth prior to metabolite extraction. Four biological replicates for each group were prepared in the study for a total of fourty-eight analyzed samples.

2.2. Metabolite Extraction and LC-MS/MS Analysis

Polar metabolite extraction used in this study has been previously described²¹. Briefly, culture samples were centrifuged at 18,000 xG for 10 minutes and washed three times to remove growth medium. Two hundred and fifty microliters of cold methanol was added to the bacteria pellet and the samples were agitated on a vortex machine, following addition of 50 μL isotope-labelled amino acid as internal standards (Cambridge Isotope Laboratories, Tewksbury, MA, USA). The mixture was stored at −20°C for 20 min then centrifuged for 10 minutes to pellet bacteria cells. One hundred and fifty microliters of supernatant was transferred to an LC-MS vial for untargeted metabolomics analysis. To ensure instrument reproducibility throughout the analysis, a pooled quality control (pQC) was prepared by mixing 5 μL aliquots of every biological sample analyzed in an LC-MS vial and homogenized for 2 minutes. The LC-MS/MS analyses were performed on a Vanquish ultra-high-performance liquid chromatography (UHPLC) system (Thermo Scientific, Waltham MA, USA) coupled to a Q-Exactive^™ Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Scientific, Waltham MA, USA). A sample volume of 5 μL was injected onto an XBridge BEH Amide XP Column, 130Å (150 mm × 2.1 mm ID, particle size 2.5 μm) (Waters Corporation, Milford, MA, USA) which was consistently housed at 40°C. Mobile phases were prepared as the following: (A) 5 mM NH4Ac in ACN/H2O (10:90, v/v) with 0.1% acetic acid; (B) 5mM NH₄Ac in ACN/H₂O (90:10, v/v) with 0.1% acetic acid. The mobile phases were delivered at a flow rate of 0.3 mL/min for a 12 min run with the following stepwise gradient for solvent B: firstly 70%; 0–5 min 30%; 5–9 min 30%; 9–12 min 70%. The electrospray ionization source (ESI) on the Q-Exactive^™ was operated in both positive and negative ion mode. The ion spray voltage was set at 4 kV with a capillary tube temperature of 320°C. The sheath gas rate was set to 10 arbitrary units. Full MS scans of 1 ms were performed at 35,000 units resolution. A pQC sample followed by a blank (mobile phase solution) injection were performed among every ten injections of biological samples to monitor instrument performance. The pQC sample were also utilized for the top 10 MS/MS analyses with dynamic exclusion during the analysis for compound identification. All raw data for the MS has been deposited to MassIVE under accession number MSV000088123.

2.3. Quantitative Polymerase Chain Reaction (qPCR):

The expression of virulence genes in these strains and the effect of substrate on their expression was measured by qPCR. In brief, total genomic DNA was extracted and purified using Promega wizard(R) denomic DNA purification kit (Promega, Madison, WI, USA). Integrity of harvested DNA was verified by NanoDrop ND-1000 spectrophotometer, and qPCR was performed using a CFX96 real-time PCR system (Bio-Rad Laboratories, Hercules, CA, USA) with a SYBR Green PCR kit (Bio-Rad Laboratories, Hercules, CA, USA). Primers were purchased from Integrated DNA Technologies (Coralville, IA, USA), and are detailed in supplemental table 2. Target gene expression was normalized to 16S sequence using the 2^−ΔΔCT method²².

2.4. Data Processing and Statistical Analysis

Quan browser module of Xcalibur version 4.0 (Thermo Fisher Scientific, Waltham, MA, USA) was used for the initial screening of the spectral peaks prior to importing data to into the automatic feature annotation and interpretation software Compound Discoverer 3.1 (Thermo Scientific, Waltham MA, USA) for metabolite identification. All UHPLC−MS data were searched against our in-house database containing experimentally obtained MS/MS spectra of 171 authentic analytical standards²³ and several online databases, including the Kyoto encyclopedia of genes and genomes (KEGG)²⁴, the human metabolome database (HMDB)²⁵, and PubChem²⁶ for metabolite identification. The collected data were then normalized to optical density per replicate, and mannually filtered to remove redundancy and ensure instrument reproducibility. Intracellular metabolites were collected for analysis, and grouping details of the individual analyses for each section can be found in supplemental figure 1. Any metabolite with a coefficient of variance > 30% was excluded from subsequent analyses. Statistical analyses, including univariate (t-test), multivariate (partial least squares-discriminant analysis) and pathway analysis, were conducted using the online resource MetaboAnalyst 5.0²⁷. The global test package was used to determine the association of metabolites within respective pathways with an experimental variable. Top pathways were selected based on pathway impact >0.2 and −log₁₀(p) > 2 to increase number of selected metabolic pathways. Metabolites from top altered pathways were selected for further statistical analyses. PLS-DA was used for the analysis of the metabolic differences between the various culture conditions of the tested strains. VIP plots were generated to report top metabolites individual contribution to our models’ predictive and explanatory capacity, with respect to all other metabolites included in the model.

3. Results and Discussion:

3.1. Influence of various conditions on Staphylococcus aureus growth

Various environmental factors such as pH, nutrient availability, and reactive oxygen species (ROS) can influence growth and virulence in SA²⁸. One primary environmental factor varying thoughout the course of SA colonization and infection is the oxygen availability. Free oxygen considerabily diminishes within the intestinal track of host when SA is ingested as a potential foodborn pathogen, and the oxygen levels are nearly consistent with hypoxic conditions (predicted around 3–5 mm Hg)²⁹. To observe the effect of oxygen availability on SA growth, we cultured both methicillin sensitive SA (MSSA) RN450 and methicillin resistant SA (MRSA) 450M in both aerobic and anaerobic conditions. After a 6-hour incubation in baseline mGAM, we observed no significant effect of oxygen on the growth of RN450 (Figure 1A). In comparison, our representative MRSA strain had significantly reduced culture density in anaerobic conditions as observed by OD600 readings (p-value= 3.40E-03) (Figure 1B). SA is able to persistently grow in anaerobic environments by switching its phenotype to small colony variants (SCV)³⁰. Numerous SCVs demonstrate slower growth, reduced membrane potential, and deregulated expression of virulence factors. In addition, free oxygen availability has been credited as one of the primary antagonistic conditions for TCA cycle activity in SA³¹. Specifically, upon switching from aerobic to anaerobic conditions, SA decreases the expression levels of the TCA cycle genes and up-regulates genes involved in glucose metabolism³². In an environment lacking added carbon sources such as the mGAM cultures, reduced TCA activity manifests as slower cell proliferation. Thus, it is of great importance to understand the combinatory effect of oxygen and nutrient availability to better elucidate the wholistic metabolic modifications employed by SA to survive harsh environments.

Figure 1:

Influence of different environmental conditions on Staph aureus growth. Densities of anaerobic and anaerobic cultures as measured by OD600 for A) RN450 and B) 450M; Densities of baseline mGAM and energy substrate supplemented cultures as measured by OD600 for C) RN450 and D) 450M. * denotes p-value < 0.05 as determined by pairwise t-test

As the diet of host will generally provide nutrients for commensals to utilize as energy substrates, we sought to observe the effects of afforded energy substrates on strain growth under anaerobic conditions by obtaining OD600 readings from 6 hour cultures supplemented with 1% w/v of glucose or dextrin. Here, we utilized glucose as a carbohydrate model where dextrin represented dietary fiber³³. When compared to baseline mGAM, RN450 demonstrated significantly increased growth as a result of both glucose (p-values= 3.90E-04) and dextrin supplementation (p-values= 1.30E-04) (Figure 1C). OD600 readings for dextrin supplemented cultures were also significantly higher that those with added glucose (p-value= 1.30E-03). Similarly, 450M exhibited increase in its growth with both added glucose (p-values= 4.60E-04) and dextrin (p-value= 5.40E-03) (Figure 1D). No apparent change was evident between glucose and dextrin supplemented cultures (p-value= 1.80E-01). This comes as no surprise, as these added carbon sources afforded more energy substrates, and SA would benefit from increased nutrient availability to fuel growth and subsequent pathogenesis. However, it is unclear how SA metabolism was modulated from the transition of aerobic to anaerobic growth, and if SA metabolism is modulated the same way by the additions different of carbon sources. Hence, we performed the following metabolomics studies to answer these research questions.

3.2. The impact of oxygen levels to Staphylococcus aureus metabolism

In our previous study, we have demonstrated the ability of SA to adapt its metabolic processes to overcome environmental changes when treated with various classes of antibiotics²⁰. We have also highlighted significant changes to SA central carbon metabolism as it acquires antibiotic tolerance¹⁹. In order to better understand the metabolic mechanisms fueling persistence of this pathogen under different growth environments, we sought to elucidate the effects of an oxygen deprived environment on SA metabolism. A high resolution untargeted metabolomics platform was used to detect metabolic deregulation after a six hour anaerobic incubation of SA to determine how its metabolism is rewired to combat anaerobic conditions compared to its aerobic growth/metabolism. As SA can migrate from the nasal cavity and transition to internal host niches, we hypothesized that it survives the anaerobic conditions by rewiring its central metabolic processes to recycle available nutrients and combat adverse environments. From our untargeted LC-MS metabolomics, we detected 260 polar metabolites from more than 15 metabolic pathways. Two component partial least squares-discriminant analysis (PLS-DA) model in figure 2A compared the aerobic vs. anaerobic metabolic profiles of our representative MSSA strain, and revealed independent clustering between aerobic and anaerobic groups. Data suggested that oxygen availability does indeed influence overall metabolism, where component one found on the X-axis summarizes 32.9% of the metabolic variances due to the different oxygen conditions. The Y-axis highlighting component two summarizes 20.6% of variation between the two different oxygen conditions. This PLS-DA model exhibited an R² value of 0.99 and a Q² value of 0.77, suggesting good model fit and predictive ability, respectively³⁴. Additional validation was conducted using permutation tests, which confirmed robustness of the model at 20 permutations (p-values= 3.20E-02) (supplemental figure 2A). For our representative MRSA stain, oxygen dependent metabolic differences were also observed, and the component one of PLS-DA model summarized 40.9% of variations when compared the two oxygen conditions, while 24% variances can be summarized by component 2 (R²= 0.99 and Q²= 0.79) (Figure 2B). Permutation tests for this model demonstrated a p-value of 1.81E-02 (supplemental figure 2B). Collectively, our data indicates that the presence or absence of oxygen does influence the overall metabolic profile of SA, suggesting unique rewiring of metabolic processes to maintain growth. Deregulated metabolites were subsequently selected using a volcano analysis with a significance cut-off of log₂(FC) greater than 1 and p-value less than 0.05 (supplemental figure 3A). For MSSA, 16 metabolites were found to be deregulated as a results of anaerobic growth. Those signifnicantly deregulated metabolites and their respective fold changes are detailed in supplemental table 3. Among these metabolites, we noted uridine, uracil and deoxyuridine monophosphate which partake in pyrimidine metabolism. Additionally, two prominent purine metabolism intermediates, inosinic acid and guanosine, were also noted. NADPH was pinpointed as the most increased intermediate in RN450. Reports have demonstrated elevated pentose phosphate pathway activity and accumulated NADPH in SA subjected to oxidative stress³⁵. This cofactors is relied upon for amino acid biosynthesis in SA³⁶, and its increased levels can be attributed to robust carbon recycling in anaerobic SA. Alternatively for 450M, 24 significantly altered metabolites were determined by volcano analysis (supplemental table 4). Glycerol 3-phosphate was evidently the most elevated metabolite when comparing anaerobically cultured 450M to those cultured in the presence of oxygen. Its increase is synonymous with excessive reliance on glycolsis, which is reported to be the primary energy pathway for SA in anaerobic conditions³⁷.

Figure 2:

Influence of oxygen on Staph aureus metabolism. PLS-DA plot depicting the different metabolic profiles of aerobic and anaerobic cultures for A) RN450 (MSSA) and B) 450M (MRSA); Heatmap highlighting the relative abundance of the significantly deregulated metabolites from volcano plot for C) RN450 (MSSA) and D) 450M (MRSA) in aerobic and anaerobic cultures; Significantly deregulated metabolic pathways implicated upon culturing strain in anaerobic conditions in both our E) RN450 (MSSA) and F) 450M (MRSA). Significance cut-off based on a −log₁₀(p) > 1.30

Comparative analysis of these deregulated metabolites between different oxygen conditions are presented in the heatmaps in figure 2C–D to highlight the differences in their respective relative abundances. Among the deregulated metabolites, it was noted that inosinic acid and NADPH were decreased in both strains when cultured in anaerobic conditions. It also became evident that lack of oxygen increased intracellular uracil in both SA strains as well. Interestingly, an inverse trend for 2’-deoxyadenosine 5’-monophosphate (dAMP) was observed as it was found to be increased in our anaerobically cultured RN450 but relatively decreased in anaerobic 450M. Decreased dAMP levels in our MRSA strain hint at impaired DNA synthesis as a result of oxygen deficiency³⁸. As these metabolites partake in nucleotide metabolism in SA, their collective deregulation suggests anaerobic conditions impairs growth of SA, irrespective of its methicillin resistance. The significant deregulation of purine and pyrimidine pathway metabolites could also suggest consumption of their pathway intermediates to synthesize proteins assisting in combating environmental stress resulting from lack of oxygen³⁹.

Genomic analyses of oxygen deprived SA suggested their reliance on mixed-acid fermentation to cope with anaerobic stress³². To maintain consistent redox potential, bacteria produce a combination of succinate, formate, acetate, lactate, and alcohol in fermentative conditions⁴⁰. Our metabolomics analysis detected unaltered levels of lactic acids in our anaerobically cultured strains, which suggests that there may be other unknown metabolic modifications in SA to sustain growth. To better elucidate the overall influence of anerobic conditions and uncover the metabolic adaptivity of SA, pathway analysis was conducted to elucidate the deregulated metabolic pathways influenced by anaerobic SA growth, and many of the detected pathways revolved around central carbon and amino acid metabolism (Table 1). While no change was observed to overall culture density, anaerobic culturing directly influenced five metabolic pathways in MSSA, namely amino sugar and nucleotide metabolism, arginine biosynthesis, alanine aspartate and glutamate metabolism, as well as purine and pyrimidine metabolism (Figure 2E). Decreased overall growth was evident in 450M, and our representative MRSA strain demonstrated a more flexible metabolism with ten deregulated metabolic pathways (Table 2), which included cysteine and methionine metabolism, starch and sucrose metabolism, aminoacyl-tRNA biosynthesis, arginine and proline metabolism, pantothenate and CoA biosynthesis, glycine serine and threonine metabolism in addition to the amino acid and nucleotide metabolic pathways reported above (Figure 2F). Previous reports have demonstrated considerable alterations in amino acid composition upon inducing osmotic stress on SA cultures³⁹, which could explain the deregulation of nucleotide bases, such as guanine and thymine, in our cultures as de-novo pathway enzymes produce purine and pyrimidine nucleotides from simple molecules such as glutamine⁴¹. This data suggests that SA can alter central carbon metabolism and recycle amino acids to sustain growth when deprived of oxygen.

Table 1:

Significantly deregulated metabolic pathways in RN450 upon culture in anaerobic conditions

Pathway Name	−log10(p)	FDR	Impact
Purine metabolism	3.57	1.47E-02	0.33
Pyrimidine metabolism	3.26	1.53E-02	0.54
Alanine, aspartate and glutamate metabolism	2.97	1.97E-02	0.84
Arginine biosynthesis	2.48	3.06E-02	0.75
Amino sugar and nucleotide sugar metabolism	2.19	4.39E-02	0.29

Table 2:

Significantly deregulated metabolic pathways in 450M upon culture in anaerobic conditions

Pathway Name	−log10(p)	FDR	Impact
Arginine biosynthesis	5.12	4.15E-04	0.75
Pyrimidine metabolism	4.30	9.24E-04	0.56
Purine metabolism	3.54	2.27E-03	0.33
Glycine, serine and threonine metabolism	2.87	5.42E-03	0.49
Alanine, aspartate and glutamate metabolism	2.86	5.42E-03	0.84
Pantothenate and CoA biosynthesis	2.58	8.98E-03	0.21
Arginine and proline metabolism	2.12	1.83E-02	0.50
Aminoacyl-tRNA biosynthesis	2.11	1.83E-02	0.25
Starch and sucrose metabolism	2.08	1.83E-02	0.37
Cysteine and methionine metabolism	2.06	1.83E-02	0.32

3.3. The impact of different energy substrates on Staphylococcus aureus metabolism

As SA can be ingested and thus considered a foodborne pathogen, it will be afforded various nutritional substrates as consumed by the host. Our data has demonstrated that diverse carbon sources will significantly increase the growth of SA (Figure 1C–D). Thus, we sought to uncover the divergent metabolic mechanisms by which these strains can fuel growth. To test the hypothesis that SA exhibits an adaptive metabolism with respect to the energy substrates available, we cultured our MSSA and MRSA strains in either baseline mGAM or media supplemented with 1% w/v of glucose or dextrin in anaerobic condition. While we observed significant increase in culture density upon supplementation with different carbon sources, the direct metabolic modifications are yet to be explored. Using a one-way analysis of variance (ANOVA), the availability of unique carbon sources was directly involved in the deregulation of 231 metabolites in RN450 compared to 215 in 450M (p-value < 0.05). PLS-DA were used to summarize the overall differences in metabolic profiles of baseline mGAM and glucose/dextrin supplemented cultures. The model revealed a good separation of our three distinct culture conditions for RN450, suggesting that each of glucose and dextrin uniquely impacts the metabolic processes of SA. As evident by PLS-DA (Figure 3A), component 1 (x-axis) and component 2 (y-axis) collectively summarized 78% of variation between substrate groups for MSSA (R²= 0.97 and Q²= 0.95). Permutation tests for this model demonstrated a p-value of 1.38E-06 (supplemental figure 2C). In this model, both dextrin and glucose groups appeared to cluster away from mGAM. As dextrin constitutes of mixture of polymers of glucose units linked together by glycosidic bonds, it is expected to impact SA metabolism in a manner similar to glucose. However, overall unique clustering suggests that the substrates uniquely impacted the SA metabolism. The same effect was evident in our MRSA strain, as 450M cultured in glucose and dextrin appeared to cluster relatively closer to each other than their distance to baseline mGAM samples (Figure 3B). Collectively, the PLS-DA model summarized 69.6% of variation between cultures (R²= 0.95 and Q²= 0.88). Permutation tests for this model demonstrated a p-value of 2.01E-07 (supplemental figure 2D). To better understand the specific effects of substrates on individual metabolite, variance importance in projection (VIP) plots (Figure 3C–D) highlight the top 15 metabolites driving separation of our 2-component statistical models. A greater VIP values indicates greater contribution of this endogenous compound in driving the separation of our analyzed SA strains. The heatmap adjacent to the plots highlights the relative abundance of these metabolites among our culture groups. The growth of MSSA in glucose and dextrin resulted in a decrease in majority of the VIP metabolites (Figure 3C). Dextrin supplemented cultures exhibited the lowest abundance of the VIP metabolites, with the highest intracellular levels detected in baseline mGAM cultures. Relative abundance of VIP metabolites in the 450M cultures followed a similar pattern, except for 16-hydroxyhexadecanoate which was found to be relatively more abundant in our MRSA cultures supplemented with dextrin (Figure 3D). These findings were contrary to our hypothesis that additional energy substrates will lead to accumulation of intracellular metabolite abundance in SA. However, it was evident that many of the noted VIP metabolites are glycolysis intermediates (e.g., phosphoenolpyruvic acid) or amino acid and their derivatives (e.g., serine, N-Acetyl-L-glutamic acid). This finding leads us to believe that production of virulence factors draws on metabolism, where metabolite pools are drained to maintain cellular fitness upon transitioning to virulence⁴². In this setting, endogenous metabolites do not feed back directly into metabolic pathways rather are produced by dedicated synthases to sustain virulence factor production⁴³. Relative comparison of the top 50 most deregulated metabolites resulting from supplementing mGAM, as determined by ANOVA, are visualized in the heatmaps in supplemental figure 4A–B for MSSA and MRSA, respectively. Multivariate analysis of variance (MANOVA) was also employed to explore the systemic alterations of both subtrates when compared to baseline mGAM. After post-hoc adjustment using a Tukey test, it was apparent that 231 metabolites were collevtively deregulated in the RN450 strain cultured in three substrate conditions after false discorvery rate (FDR) adjusted p-value of 0.05 (supplemental figure 5A). Among these metabolites, it was evident that D-mannitol, D-mannitol-1-phosphate and fucose were the top altered metabolites (p-value < 0.05). In comparison, MANOVA uncovered 215 significantly altered metabolites in the 450M strain (supplemental figure 5B), of which 4-hydroxybenzoic acid, eleostearic acid and D-mannitol were the most significantly deregulated between MSSA culture conditions. These findings corroborate unique metabolic processes of SA when presented with various energy substrates regardless of acquired methicillin resistance.

Figure 3:

Influence of different energy substrates on Staph aureus metabolism. PLS-DA plot depicting the overall metabolic profiles of baseline mGAM cultures compared to glucose and dextrin in A) RN450 (MSSA) and B) 450M (MRSA); Top 15 metabolites driving separation of our PLS-DA model in A) RN450 (MSSA) and B) 450M (MRSA); Significantly deregulated metabolic pathways observed when comparing glucose to dextrin supplemented cultures of E) RN450 (MSSA) and F) 450M (MRSA). Significance cut-off based on a −log₁₀(p) > 1.30

To better understand the overall influence of unique carbon sources and unravel the metabolic adaptivity of SA, pathway analysis was conducted to elucidate the deregulated metabolic pathways between glucose and dextrin groups. Baseline mGAM cultures were excluded in efforts to highlight the substrate dependent effects on bacterial metabolism. Pathway analysis for MSSA revealed 9 signficantly deregulated metabolic processes (Table 3), encompassing arginine and proline metabolism, inositol phosphate metabolism, amino sugar and nucleotide sugar metabolism, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism, arginine biosynthesis, cysteine and methionine metabolism, in addition to purine and pyrimidine metabolism (Figure 3E). Evidently, MRSA demonstrated its robust metabolic adaptivity again as fourteen deregulated metabolic pathways were detected when comparing glucose and dextrin supplemented cultures (Table 4). In addition to the afforemention nine pathways altered in MSSA, these altered pathways also included pantothenate and CoA biosynthesis, aminoacyl-tRNA biosynthesis, glutathione metabolism, glycine, serine and threonine metabolism and sulfur metabolism (Figure 3F). Direct comparison of the metabolic deregulations between RN450 and 450M cultures supplemented with the same energy substrate further corroborated SA’s metabolic flexibility. When supplemented with glucose as an energy substrate, it was evident that seven metabolic pathways were deregulated between our MRSA and MSSA strains (supplemental figure 6A). These metabolic processes include arginine and proline metabolism, pyrimidine metabolism, arginine biosynthesis, starch and sucrose metabolism, alanine aspartate and glutamate metabolism, amino sugar and nucleotide sugar metabolism and purine metabolism. Conversely, ten metabolic pathways appeared to be deregulated when culture media was supplemented with dextrin (supplemental figure 6B). Apart from starch and sucrose metabolism, dextrin supplementation induced deregulations to the same metabolic pathways as glucose, in addition to glycine serine and threonine metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism and sulfur metabolism. Thus, while different energy substrates will induce novel metabolic phenotypes within our SA cultures, acquired methicillin resistance will also uniquely influence SA metabolism. These findings suggest that methicillin resistance is energy taxing on SA and is supported by robust metabolic alterations.

Table 3:

Significantly deregulated metabolic pathways in RN450 comparing glucose to dextrin supplemented cultures

Pathway Name	−log10(p)	FDR	Impact
Arginine and proline metabolism	2.81	3.74E-03	0.50
Inositol phosphate metabolism	2.82	3.74E-03	0.63
Amino sugar and nucleotide sugar metabolism	2.88	3.45E-03	0.29
Alanine, aspartate and glutamate metabolism	3.29	1.55E-03	0.84
Starch and sucrose metabolism	3.33	1.51E-03	0.37
Arginine biosynthesis	3.44	1.34E-03	0.75
Cysteine and methionine metabolism	4.45	2.36E-04	0.32
Pyrimidine metabolism	4.85	1.57E-04	0.54
Purine metabolism	4.91	1.57E-04	0.33

Table 4:

Significantly deregulated metabolic pathways in 450M comparing glucose to dextrin supplemented cultures

Pathway Name	−log10(p)	FDR	Impact
Sulfur metabolism	2.83	2.13E-03	0.20
Pantothenate and CoA biosynthesis	3.72	3.89E-04	0.21
Aminoacyl-tRNA biosynthesis	4.77	7.72E-05	0.25
Amino sugar and nucleotide sugar metabolism	4.30	1.39E-04	0.29
Cysteine and methionine metabolism	4.02	2.21E-04	0.32
Purine metabolism	6.56	1.51E-05	0.33
Starch and sucrose metabolism	4.57	9.73E-05	0.37
Glycine, serine and threonine metabolism	4.56	9.73E-05	0.49
Arginine and proline metabolism	3.32	7.46E-04	0.50
Glutathione metabolism	3.48	5.65E-04	0.52
Pyrimidine metabolism	5.21	4.21E-05	0.54
Inositol phosphate metabolism	3.36	7.15E-04	0.63
Arginine biosynthesis	4.55	9.73E-05	0.75
Alanine, aspartate and glutamate metabolism	5.61	3.43E-05	0.84

It is known that Staphylococcus cannot metabolize sulfate or sulfonates as main sulfur contributors in cysteine biosynthesis⁴⁴. Instead, SA metabolism can compensate by the production of thiosulfate or glutathione⁴⁴. Thus, a feedback loop should exist for repressing cysteine catabolism if appropriate sulfur sources are lacking for cysteine production. Avaibility of cysteine in SA is essential to maintain an intracellular reducing environment and mitigate oxidative stress⁴⁵. Our metabolomics analyses revealed significantly reduced cysteine levels in both glucose and dextrin supplemented MRSA cultures (p-value= 3.20E-02 and 9.50E-03, respectively). Additionally, glutathione levels were diminished in dextrin cultured 450M (p-value: 1.30E-02), suggesting that oxidative stress is more prominent when dextrin is used as a carbon source. Another main intermediate in carbon metabolism is glutamate, as it serves as the primary amino group donor for most anabolic enzymatic reactions⁴⁶. Metabolism of glutamate, and main carbon skeletons of glutamine, serve as a central carbon source to facilitate growth in media lacking glucose⁴⁷. This finding is corroborated by our data, as glutamine levels were significanly increased in glucose supplemeted 450M (p-value= 2.30E-02) but not when dextrin was added (p-value= 0.74). Various pathways contributing to glutamate catabolism, from proline, arginine, and histidine precursors, are active solely upon depletion of preferred carbon sources such as glucose. Significant decreased of intracellular proline (p-value= 4.30E-02) and arginine (p-value= 1.30E-03) in dextrin supplemented cultures suggest that this interplay serves as a rapid mechanism to recycle amino acids as carbon sources when glucose is not immediately available. Histdine levels demonstrated a similar trend between glucose and dextrin supplemented MRSA cultures, yet did not exhibit significane (p-value= 5.10E-02), indicating that proline and argine are favored carbon skeletons to replenish glutamine levels. Apparent deregulation to these amino acid metabolic pathways could suggest SA’s conservation of carbon skeletons in the presence of glucose to mediate other cellular processes⁴⁷.

3.4. The Impact of Different Substrates on MRSA Virulence and Its Relationship to Altered Metabolites

To survive the diverse and often hostile environments within the host, SA must be able to sense various environmental signal and modulate specific virulence factors to sustain pathogensis^{48, 49}. These signals are vital for host colinzation and thus contribute to SA’s transition from commensal strain to pathogen. In vitro studies have been widely employed to explore the regulations of virulence gene in SA^{50, 51}. It is established that SA bacteria exhibiting defective aerobic respiration genes are less effective at establishing virulence^{50, 51}. Meanwhile, reports have validated increased virulence in response to the availability of small molecule metabolites (e.g., pyruvate)⁵². In efforts to determine the relationship between growth conditions and differential regulation of SA virulence, we sought to investigate the relative changes in virulence factor expression in our MRSA strain and its relationship to the metabolic adaptation of MRSA. Relative expression of four prominent genes, accessory gene regulator (agr), Staphylococcal enterotoxins A and B (sea, seb), and exfoliative toxin (et) known to be upregulated in pathogenic strains of SA, were quantified in this study. The agr locus is a quorum-sensing, two-component system which was first noted in SA as a regulator controlling the production of secreted virulence factors and surface proteins⁴⁹. We noted significantly increased expression of the conserved component of this gene, agr-I, in both our glucose and dextrin supplemented cultures (p-value= 1.70E-04 and 1.30E-05, respectively) (Figure 4A). Enhanced expression of agr-I resulting from energy substrate supplementation is a vital initial step to delineate the extent of metabolic regulation of SA pathogensis⁷. The availability of pyruvate, the end product of the glucose via glycolysis and prominent nutrient in the host, is sensed by SA prior to initiation of virulence⁵². As anaerobic SA demonstrates a increase of glycolysis genes³², it could be attributed to SA’s attempt to produce more pyruvate to induce pathogensis. In our results, the pyruvate precursor phosoenolpyruvic acid was significantly reduced in both glucose and dextrin supplemented cultures (p-value= 3.60E-02 and 1.00E-03, respectively). As anaerobic conditions reduce TCA activity compromising phosoenolpyruvic acid conversion to oxaloacetate, the decrease of the pyruvate precursor could be a result of elevated pyruvate synthesis to sustain virulence.

Figure 4:

Influence of different energy substrated on Staph aureus virulence. Gene expression levels of virulence factors A) agr-I, B) sea, C) seb, D) eta. E) Correlation heatmap showing the relationship between virulence factors and top VIP metabolites contributing to the unique metabolic profiles of substrate supplemented 450M. Statistical significance determine by pairwise t-test, * denotes p-value < 0.05 whereas † represents p-value < 0.001

SA produces a wide variety of endogenously-derived toxins, such as sea and sea, which manifest in emetic activity⁵³. Almost all enterotoxins are encoded by accessory genetic elements, including plasmids, or by genes located next to the staphylococcal cassette chromosome (SCC) which is compromised in acquired methicillin resistance⁵⁴. These toxins are synthesized by S. aureus upon reaching the logarithmic phase of growth or throughout the transition from the exponential to the stationary phase. In our analysis, we noted that sea was not expressed in our baseline mGAM cultures of MRSA, whereas seb was slightly encoded (Figure 4B–C). sea expression was significantly increased as a result of both glucose and dextrin supplementation of culture media (p-value= 1.20E-05 and 3.00E-04, respectively) (Figure 4B). This effect was mirrored in for seb in similar fashion for both glucose and dextrin supplemented cultures as well (p-value < 0.0001) (Figure 4C). Taken together, this data suggests that elevated levels of nutrient intake can enhance SA pathogenicity and directly contribute to host emetic activity as mediated by staphylococcal enterotoxins.

SA also possess the ability to produce a variety of toxins, such as et. These exotoxins are responsible for dermal damage in humans and they have been associated with clinical symptoms such as fever, skin tenderness, and erythema, followed by large sheets of epidermal separation⁵⁵. Toxin secretion occurs few hours to days after colonization and can cause detrimental effects to host intestinal linings. While eta expression was minimal in baseline mGAM, glucose (p-value = 2.40E-05) and dextrin supplementation (p-value= 8.00E-04) both significantly upregulated exotoxin formation (Figure 4D).

In efforts to better determine specific associations between metabolites and SA virulence, Pearson correlation was calculated between each of the probed genes and the top VIP metabolites driving metabolic heterogeneity between our MRSA cultures when comparing mGAM, glucose and dextrin groups (Figure 4E). The selected VIP score was > 1.55, consistent with other reports⁵⁶. It became apparent that 16-hydroxyhexadecanoate and eleostearic acid were positively correlated with agr-I (correlation coefficient= 0.71 and 0.6, respectively). Conversely, many endogenous microbial metabolites including phosphoenolpyruvic acid, purine, D-glucosamine-6-phosphate and diaminopimelic acid all were negatively associated with the SA virulence regulator agr-I (correlation < −0.9). Overall, it was apparent that almost all selected VIP metabolites were negatively correlated with agr-I, eta and seb expression. Whereas a positive correlation was generally evident for sea. This data suggests that metabolite pools are depleted in pathogenic SA to maintain cellular processes as the strain transitions from commensal to pathogen⁴². It was interesting to note that 16-hydroxyhexadecanoate and eleostearic acid were the only detected VIP metabolites that exhibited an inverse correlation to overall virulence genes compared to all other compounds. These fatty acids were only negatively correlated with sea expression, while our analysis revealed positive correlation with agr-I, seb and eta. 16-hydroxyhexadecanoate is a naturally occurring hydroxy fatty acid⁵⁷. These acids are known to help maintain membrane integrity and could aid in understanding SAs ability to alter membrane fluidity to increase signaling with the environment⁵⁸. Eleostearic acid, on the other hand, is a polyunsaturated fatty acid and a conjugated linoleic acid derivative. Reports have suggested that exposure of SA to linoleic acid can directly upregulate genes associated with stress resistance, cell wall synthesis and virulence⁵⁹. Meanwhile, low concentrations of lineolic acid were shown to increase expression of virulence regulator RNAIII in SA⁵⁹. One limitation of this in vitro study is the lack of host associated factors that will influence virulence. Additionally, SA will generally be in close proximity to other anaerobes which might compete for resources as well⁶⁰. Another limitation arises when considering that the analytical approach favored the detection of relatively polar metabolites compared to other non-polar endogenous compound classes such as lipids. Other than serving as a major energy source and primary component of membranes, lipid derived metabolites have been reported to serve as signaling molecules for other bacteria or host cells⁶¹. SA is one of the bacterial species able of deriving fatty acids from the branched-chain amino acids including leucine, valine or isoleucine⁶². As previous studies have indicated that SA virulence can be mitigated via the deregulation of fatty acid biosynthesis⁶³, future studies on bacterial lipid metabolism are warranted to better enhance our understanding of the extend of SA’s adaptive metabolism to sustain pathogensis.

4. Conclusion:

In this study, we observed significantly decreased growth of our MRSA strain in anaerobic conditions, suggesting these bacteria were subjected to oxidative stress which impaired growth. Additionally, supplementation of culture media with energy substrates significantly increased proliferation of both SA strains. The addition of carbon sources facilitated SA’s ability to overcome environmental stress and persist in growth, indicating intricate metabolic pertubations strategically utilized for energy metabolism. Using our high resolution mass spectrometry metabolomics platform, we noted our MRSA strain demonstrated a more robustly adaptive metabolism, as only 9 metabolic pathways were deregulated in our MSSA strain when subjected to anaerobic conditions, compared to 14 pathways from MRSA. We also noted the changes of growth environments can drive the regulation of virulence in SA, and the associations of changes in SA metabolism to their virulence. Increased expression of agr-I, sea, seb and eta virulence factors was apparent in our supplemented SA cultures. Among the probed metabolites, fatty acids 16-hydroxyhexadecanoate and eleostearic acid were positively correlated with agr-I, suggesting deregulated lipid metabolism in SA virulence. Taken together, our findings supported the use of LC-MS metabolomics for uncovering metabolic adaptation of SA under different oxygen conditions and with different substrate supplementations. Our study could set up the foundations for future studies to identify metabolic inhibitors to mitigate SA infections in different growth environments.

Supplementary Material

supporting information

Figure S1: Grouping of the intracellular metabolite analysis in our SA strains

Figure S2: Permutation tests conducted at 20 permutations to validate PLS-DA model performance for A) anaerobic RN450 B) anaerobic 450M C) RN450 cultured in various energy substrates and D) 450M cultured in various energy substrates

Figure S3: Volacno plots to determine the significantly deregulated metabolites upon culturing our SA strains A) RN450 and B) 450M in anaerobic conditions. Deregulated metabolites were subsequently selected with a significance cut-off of log₂(FC) greater than 1 and p-value less than 0.05

Figure S4: Heatmap highlighting the relative changes of the top 50 deregulated metabolites upon culturing our SA strains A) RN450 and B) 450M in mGAM and energy substrate supplemented media.

Figure S5: Multivariate analysis of variance (MANOVA) revealing the significantly deregulated metabolites of each SA strain among respective culture conditions

Figure S6: Pathway analysis comparing the metabolic deregulations between RN450 and 450M

Table S1: Media compositions of Gifu anaerobic broth (GAM) and modified GAM

Table S2: SA virulence gene primers used in this study

Table S3: Significantly deregulated metabolites between aerobic and anaerobic RN450 as determined by volcano plot

Table S4: Significantly deregulated metabolites between aerobic and anaerobic 450M as determined by volcano plot