ABSTRACT
A disrupted “dysbiotic” gut microbiome engenders susceptibility to the diarrheal pathogen Clostridioides difficile by impacting the metabolic milieu of the gut. Diet, in particular the microbiota accessible carbohydrates (MACs) found in dietary fiber, is one of the most powerful ways to affect the composition and metabolic output of the gut microbiome. As such, diet is a powerful tool for understanding the biology of C. difficile and for developing alternative approaches for coping with this pathogen. One prominent class of metabolites produced by the gut microbiome are short chain fatty acids (SCFAs), the major metabolic end products of MAC metabolism. SCFAs are known decrease the fitness of C. difficile in vitro and that high intestinal SCFA concentrations are associated with reduced fitness of C. difficile in animal models of C. difficile infection (CDI). Here, we use controlled dietary conditions (8 diets that differ only by MAC composition) to show that C. difficile fitness is most consistently impacted by butyrate, rather than the other two prominent SCFAs (acetate and propionate), during murine model CDI. We similarly show that butyrate concentrations are lower in fecal samples from humans with CDI relative to healthy controls. Finally, we demonstrate that butyrate impacts growth in diverse C. difficile isolates. These findings provide a foundation for future work which will dissect how butyrate directly impacts C. difficile fitness and will lead to the development of diverse approaches distinct from antibiotics or fecal transplant, such as dietary interventions, for mitigating CDI in at-risk human populations.
IMPORTANCE Clostridioides difficile is a leading cause of infectious diarrhea in humans and it imposes a tremendous burden on the healthcare system. Current treatments for C. difficile infection (CDI) include antibiotics and fecal microbiota transplant, which contribute to recurrent CDIs and face major regulatory hurdles, respectively. Therefore, there is an ongoing need to develop new ways to cope with CDI. Notably, a disrupted “dysbiotic” gut microbiota is the primary risk factor for CDI but we incompletely understand how a healthy microbiota resists CDI. Here, we show that a specific molecule produced by the gut microbiota, butyrate, is negatively associated with C. difficile burdens in humans and in a mouse model of CDI and that butyrate impedes the growth of diverse C. difficile strains in pure culture. These findings help to build a foundation for designing alternative, possibly diet-based, strategies for mitigating CDI in humans.
INTRODUCTION
Clostridioides difficile is an opportunistic diarrheal pathogen and is an “urgent threat” to global health, as it causes over 220,000 cases and 13,000 deaths per year in the United States alone [1]. A disrupted (dysbiotic) gut microbiome, most commonly resulting from antibiotic use, is the primary risk factor for C. difficile infection (CDI) [2], highlighting the gut microbiome as a key mediator of CDI. Therefore, measures to positively impact the composition and function of the gut microbiome represent potential approaches to understand and mitigate C. difficile pathogenesis.
Diet is one of the most powerful ways to impact the composition and function of the gut microbiome [3,4]. A growing body of literature demonstrates that dietary changes impact C. difficile, the microbiome, and the host during animal models of CDI. For example, low protein diets are protective against CDI and high fat/high protein diets exacerbate CDI [5,6] and availability of the amino acid proline in particular impacts C. difficile fitness in murine models [7]. Diets containing inulin, xanthan gum, and complex mixtures of microbiota accessible carbohydrates (MACs) reduce C. difficile burdens below detection in mice [8,9] and fructooligosaccharides (FOS) increase survival in infected hamsters [10]. Another carbohydrate, trehalose, increases CDI mortality in mice [11] but does not impact C. difficile burdens or virulence in chemostats containing human-derived microbiomes [12], together highlighting the need to understand how diet influences both host- and microbiome-driven factors that impact CDI outcomes. Finally, the abundance of metals such as zinc also correlate with several measures of CDI severity in mice [13]. Together, these studies support that microbiome- and host-dependent metabolite availability in the gut, rather than a specific “susceptible” or “resistant” microbiome configuration, defines colonization resistance against C. difficile [8,14–16]. Furthermore, each of the aforementioned diet-driven impacts on CDI represents an opportunity to understand the diverse metabolic requirements of, and niches occupied by, C. difficile during CDI and are likely to lead to the development of new concepts and approaches for mitigating CDI in at-risk human populations. Notably, this previous work was carried out under controlled experimental conditions which were designed to specifically manipulate conditions of interest using animal models of CDI and a limited number of C. difficile strains. Therefore, though animal models of CDI recapitulate many relevant aspects of human disease, it is unclear the extent to which these findings translate to human populations who are infected by phylogenetically diverse C. difficile strains and who differ in important parameters like immune status and dietary habits.
Of the dietary inputs described above which impact CDI, MACs represent a particularly high-yield avenue for diet-focused work on C. difficile. In particular, the short chain fatty acids (SCFAs), which are the metabolic end products of MAC metabolism by the microbiome [17], impact C. difficile fitness in pure culture and in animal models of infection [8,18,19] and have pleiotropic beneficial effects on the host [20–26]. Three SCFAs (acetate, propionate, and butyrate) are the most abundant metabolites in the gut, together reach concentrations of over 100mM in the gastrointestinal tracts of humans [27], and are influenced by host MAC consumption. The dysbiotic conditions which favor CDI are characterized by low SCFA concentrations in both humans and animal models [8,11,15,28,29].
Despite the emerging understanding of the impact of dietary MACs and their metabolic end-products on CDI and the promise for rapid translation to humans, key questions remain. For example, which MACs are most effective in impacting CDI? What parameters differentiate effective MACs from ineffective MACs? What mechanism(s) underly these differences? Are these conclusions generalizable to all C. difficile strains? To begin to answer these questions, this study leverages a murine model of CDI, human samples, and a collection of C. difficile isolates to demonstrate that elevated concentrations of butyrate are associated with a reduction in C. difficile fitness in pure culture, in mice, and in humans. Together, these findings provide the foundation for future work aimed at understanding the metabolic interactions that dictate C. difficile fitness and pathogenesis and for developing new approaches to mitigate CDI in at-risk human populations.
RESULTS
Inulin and FOS differentially impact C. difficile burdens in mice
In previous work, we demonstrated that inulin, a β-2,1-linked fructan, suppresses C. difficile burdens in a murine model of CDI [8]. To begin to test the generalizability of these findings to other purified MAC sources, we focused on FOS, which is structurally identical to inulin except for its degree of polymerization (DP) (FOS DP = 2-8 and inulin DP = 2-60) [30]. In contrast to mice fed inulin, mice fed FOS retain high burdens of C. difficile 630 during CDI (Figure 1). These results generated two possible hypotheses, that the effect of MAC sources on C. difficile burden is driven by either MAC effects on the microbiota or by direct effects on C. difficile.
To begin to understand the differential impacts of these two MAC types on CDI, we grew C. difficile 630 in minimal medium supplemented with FOS or inulin. C. difficile 630 grows to a higher density in minimal medium supplemented with FOS relative to minimal medium supplemented with inulin (Figure 2A). This and previous work demonstrate that C. difficile cannot use inulin for growth [31]. However, C. difficile encodes an uncharacterized carbohydrate-active enzyme (CAZYme) that belongs to glycoside hydrolase family 32 (GH32), encoded by CD630_18050 in C. difficile 630. GH32 CAZYmes are important for fructan hydrolysis and are highly specific for their substrates (e.g. inulin, FOS, levan, sucrose) [32]. Together, these observations led us to hypothesize that FOS does not suppress CDI because C. difficile metabolizes FOS via a FOS-specific GH32 enzyme allowing it to persist during infection in mice fed FOS. To address this hypothesis, we performed high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) to determine the extent of FOS utilization by C. difficile grown in FOS-supplemented minimal medium. We determined that C. difficile does not utilize FOS but instead consumes the trace amounts of glucose and fructose in the FOS preparation (Figure 2B, peaks within gray bars correspond to glucose and fructose based on reference chromatograms in Figure S1). Therefore, this work supports previous findings that C. difficile does not readily consume MACs [31] and that it is likely that factors unrelated to FOS metabolism by C. difficile contribute to the inability of FOS to clear murine CDI.
The major metabolic end products of MAC metabolism by the gut microbiome are SCFAs, predominantly acetate, propionate, and butyrate [17,33,34]. Based on the metabolic capabilities of a given microbiome, MACs can differentially impact SCFA abundance and ratios in the gut. Our previous work and the work of others showed that SCFAs influence the fitness of C. difficile in animal models and in culture [8,19,28,35] and that FOS and inulin differentially impact the quantities and proportions of SCFAs produced by gut microbes in vitro [36]. We therefore hypothesized that FOS and inulin differentially impact CDI based on the quantities and types of SCFAs produced by the microbiome during infection. To address this hypothesis, we quantified acetate, propionate, and butyrate in the cecal contents of conventional mice fed FOS, inulin, or a MAC-deficient diet (see Figure 1) as described previously [8]. Mice fed FOS have lower levels of acetate, propionate, and butyrate in their ceca relative to those fed inulin (Figure 2C). In addition, less acetate was detected in the cecal contents of FOS-fed mice relative to mice fed a MAC deficient diet (Figure 2C), suggesting that alternative metabolic end products, distinct from acetate, propionate, and butyrate, are produced by FOS-fed microbiomes in this model. Consistent with our previous work, these data suggest that MACs that favor a SCFA-enriched gut environment discourage CDI.
Cecal butyrate concentrations differentiate mice that do and do not suppress CDI across diverse MAC types
The conclusions that elevated SCFAs negatively impact C. difficile burdens in the mouse gut are based on experiments that used a limited number of dietary conditions. Specifically, both a complex MAC-rich diet (5010 Purina LabDiet) and a diet containing inulin as the sole MAC source suppress CDI. On the other hand, MAC deficient diets or a diet containing FOS as the sole MAC source do not clear CDI (see Figure 1 and [8]). To further generalize these findings, we fed 5 additional diets containing different MAC sources to mice with experimental CDI. These diets contained one of three individual human milk oligosaccharides (HMOs; 2′-fucosyllactose (2′-FL), 6′-siaylyllactose (6′-SL), lacto-N-neo-Tetraose (LNnT)), a digestion resistant maltodextrin, or a complex mixture of MACs found within gum arabic. These MACs were selected based on evidence that HMOs impact SCFA production by gut microbes [37] and have a variety of beneficial effects on the eukaryotic host [38] and to understand whether the SCFAs produced by other structurally unrelated plant polysaccharides (distinct from fructans or the complex mixture of MACs present in standard rodent diets) impact C. difficile infection. We observed that these MAC types differentially impact C. difficile burdens and that out of these additional MACs tested, maltodextrin was the only one that consistently reduced C. difficile burdens below detection (Figure 3A). We then quantified acetate, propionate, and butyrate in the cecal contents of mice shown in Figure 3A to determine if SCFA concentrations differentiate mice with and without detectable fecal C. difficile in this cohort of mice fed inulin, gum arabic, resistant maltodextrin, 6′-SL, 2′-FL, and LNnT (Figure 3B). This diet-agnostic analysis of SCFA levels demonstrates that mice that cleared C. difficile below detection have significantly elevated levels of butyrate (but not acetate or propionate) in their cecal contents relative to mice with detectable C. difficile.
Fecal butyrate concentrations differentiate stool samples from humans with and without CDI
After learning that butyrate concentrations differentiate mice with and without detectable C. difficile in their feces, we wanted to know if butyrate concentrations are similarly associated with CDI in humans. Though previous work showed that SCFA concentrations increase in stool from CDI patients after a fecal transplant [39], the differences in concentrations of SCFAs in humans with CDI versus healthy controls was not previously determined. We quantified acetate, propionate, and butyrate in stool samples collected from patients who received care at Stanford Hospital in 2015. These stool samples were from patients with symptomatic CDI (diarrhea and positive for CDI (via Cepheid Xpert C. difficile)) and patients without CDI (negative for CDI (via Cepheid Xpert C. difficile)). In stool from the symptomatic C. difficile patients, we observed significantly lower concentrations of butyrate (but not acetate or propionate) relative to patients without CDI (Figure 4), which demonstrates that our findings in mice (Figure 3B) are generalizable to humans with CDI. Though acetate, propionate, and butyrate were previously shown to negatively impact the fitness of C. difficile and other bacterial pathogens [8,40], our observations from mice and humans provide the rationale for focused and specific investigation of butyrate.
Butyrate negatively impacts growth in diverse C. difficile isolates
Our previous work showing that butyrate impacts C. difficile growth was restricted to the commonly studied C. difficile 630 strain [8] and Figures 1-3). Though similar butyrate-dependent effects were observed in 4 unsequenced C. difficile isolates [18], we sought to further situate these findings in the context of a phylogenetically diverse sample of C. difficile strains. We grew 13 different C. difficile isolates with representatives from 10 ribotypes (including C. difficile 630; Table 1) in pure culture in the presence of 0, 6.25, 12.5, 25, and 50mM sodium butyrate and in matched concentrations of sodium chloride. For all C. difficile strains tested, butyrate negatively impacts growth kinetics (Figure S2), with notable concentration-dependent differences in maximum growth rate (Figure 5A) and lag time (Figure 5B). All strains tested had significantly longer lag times in the presence of 50mM butyrate compared to 0mM butyrate. Similarly, all but 2 strains tested (CD196 and TL178) exhibited significantly reduced maximum growth rates in the presence of 50mM butyrate compared to 0mM butyrate. The significance and magnitude of these effects were smaller for intermediate butyrate concentrations but were concentration-dependent.
Though bulk measurements of butyrate in human and mouse samples are lower than 50 mM (Figure 2, Figure 3, Figure 4, and [8,39], concentrations of butyrate produced by microbiome members in the gut at relevant spatial scales (e.g., when C. difficile is in close proximity to butyrate-producing commensals) remains unclear but is likely higher than what is observed via bulk measurements. Regardless, the concentration-dependent effects we observe for all strains (Figure 5) demonstrate that C. difficile growth is reliably impacted by butyrate and suggest that the molecular mechanisms underlying this response are conserved across diverse C. difficile strains.
DISCUSSION
This work adds to the growing body of literature that demonstrates that diet impacts CDI in animal models of infection. Specifically, it refines previous observations about the impacts of MACs on C. difficile fitness in the gut by showing that diets which lead to elevated butyrate production by the microbiome reduce burdens of C. difficile during infection. Taken together, our work and the work of others shows that inulin, maltodextrin, and xanthan gum are purified MACs that consistently suppress CDI while FOS, 2′-FL, 6′-SL, and LNnT are purified MACs that do not suppress CDI (Figure 3, [8,9]). Unlike a standard rodent diet that is a complex mixture of MACs [8], we show that a different complex mixture of MACs (gum arabic) does not suppress C. difficile burdens in mice (Figure 3). Importantly, given that our work exclusively used conventionally-reared Swiss-Webster mice and that differences in microbiome configuration dictate metabolites used and produced by a given community [41], it is possible that the MAC sources that did not clear CDI in our model would clear CDI in the context of a different microbiome or host. As such, future work should consider the variability of microbiome composition and metabolic outputs when designing dietary strategies for impacting CDI and other disease states.
C. difficile burdens are unlikely to be the only parameter impacted by MACs during infection, which highlights additional directions for future work. For example, though we observed that FOS does not suppress CDI in mice (Figure 1), it was previously shown that FOS increases survival time in hamsters infected with C. difficile [10] but the mechanism of this protection was not defined. As these and other MAC-driven impacts on the host immune system are better understood, they will likely contribute to the formulation of specific diet-based strategies to simultaneously bolster the host immune response while reducing the fitness of C. difficile, either through the manipulation of SCFA levels (which influence inflammation [42] and colonocyte metabolism [43,44] or by directly impacting the mucosal immune system (e.g., via HMOs which can influence inflammatory cell populations [45] and positively impact barrier function [46]. Future diet-based strategies to mitigate CDI will similarly be informed by the growing literature surrounding the impact of other dietary inputs on CDI (see Introduction).
Because butyrate levels differentiate mice and humans that have CDI from those that do not (Figure 2C, 3B, 4), continued focus on this SCFA in the context of CDI will yield important insights into the biology of C. difficile, the ecology of CDI, and future therapeutic approaches. We and others previously showed that butyrate negatively impacts growth in 5 distinct C. difficile strains [8,18] and in the current study we extend these findings to 12 additional C. difficile strains (Figure 5, Table 1), together demonstrating that these phenotypes are generalizable across a large sample of C. difficile clinical isolates. We recently developed a conceptual model to unify the seemingly paradoxical observations that growth and toxin production are differentially impacted by butyrate [35]. Specifically, C. difficile infection and proliferation is favored in a dysbiotic (butyrate deficient) gut environment where there is minimal competition for metabolites (e.g., amino acids, organic acids, sugars). Under these conditions, C. difficile produces no detectable toxin. However, as the microbiome recovers from dysbiosis, the availability of metabolites decreases and the concentrations of butyrate increases, resulting in reduced C. difficile fitness. In response to these conditions, C. difficile up-regulates its toxins, which increase inflammation [47], and presumably helps to re-establish facets of microbiome community function that allow C. difficile to thrive.
Future work based on the above conceptual model and the data presented in the current study will seek to understand the variety of host-by-microbiome-by-diet interactions that influence C. difficile fitness in the gut. Specific foci on the molecular mechanisms and genetic circuitry underlying the responses of C. difficile to butyrate will facilitate a better basic understanding of C. difficile and how it interacts with the host and the gut microbiome. In addition, continued research on these and other diet-driven effects on CDI are likely to yield insights that will aid in the development of specific and targeted manipulation of CDI, either through dietary intervention, therapeutic application of specific microbes (e.g., probiotics), or delivery of specific metabolites.
METHODS
Bacterial strains and culture conditions
Frozen stocks of C. difficile strains used in the study (Table 1; [48–50]) were maintained as -80°C stocks in 25% glycerol under anaerobic conditions in septum-topped vials. C. difficile was routinely cultured on CDMN agar, composed of C. difficile agar base (Oxoid) supplemented with 7% defibrinated horse blood (HemoStat Laboratories), 32 mg/L moxalactam (Santa Cruz Biotechnology), and 12 mg/L norfloxacin (Sigma-Aldrich) in an anaerobic chamber at 37° (Coy).
After 16-24 hours of growth, a single colony was picked into 5 mL of pre-reduced reinforced clostridial medium (RCM, Oxoid), modified reinforced Clostridial medium (mRCM: 10g/L beef extract, 3g/L yeast extract, 10g/L peptone, 5g/L dextrose, 5g/L sodium chloride, 3g/L sodium acetate, 0.5g/L cysteine hydrochloride) or PETC medium (ATCC medium 1754) without fructose (PETC-F), and grown anaerobically at 37°C for 16-24 hours. Liquid cultures were used as inocula for growth curves and for experiments using murine model CDI, below.
For in vitro growth curve experiments examining C. difficile fructan utilization, subcultures were prepared at a 1:200 dilution in pre-reduced PETC-F minimal medium supplemented with either 5 mg/mL inulin (OraftiHP; Beneo-Orafti group) or 5 mg/mL FOS (Orafti P95, Beneo-Orafti group) in sterile polystyrene 96 well tissue culture plates with low evaporation lids (Falcon).
Cultures were grown anaerobically as above in a BioTek Powerwave plate reader. At 15 minute intervals, the plate was shaken on the ‘slow’ setting for 1 minute and the optical density (OD600) of the cultures was recorded using Gen5 software (version 1.11.5). After 24 hours of growth, culture supernatants were collected, centrifuged (5 minutes at 2,500 x g), filtered (0.22 µm PVDF filter), and stored at -20°C for high performance anion exchange chromatography, below.
For in vitro growth curve experiments examining C. difficile growth in the presence of butyrate, subcultures were prepared at a 1:200 dilution in pre-reduced mRCM (RCM lacking starch and agar which reduces clumping artefacts in OD600 readings) in sterile polystyrene 96 well tissue culture plates with low evaporation lids (Falcon). Cultures were grown anaerobically in a BioTek Epoch2 plate reader. At 30-minute intervals the plate was shaken on the ‘slow’ setting for 1 minute and the OD600 of the cultures was recorded using Gen5 software (version 1.11.5).
Murine model of C. difficile infection
All animal studies were conducted in strict accordance with Stanford University Institutional Animal Care and Use Committee (IACUC) guidelines. Murine model CDI was performed on age- and sex-matched conventionally-reared Swiss-Webster mice (Taconic) between 8 and 17 weeks of age.
To reduce colonization resistance against C. difficile, mice were given a single dose of clindamycin by oral gavage (1 mg/mouse; 200 µL of a 5 mg/mL solution) and were infected 24 hours later with 200 µL of overnight culture grown in RCM (approximately 1.5×107 cfu/mL).
Feces were collected from mice directly into microcentrifuge tubes and immediately placed on ice. To monitor C. difficile burdens in feces, 1 µL of each fecal sample was resuspended in PBS to a final volume of 200 µL, 10-fold serial dilutions of fecal slurries (through 10−3-fold) were prepared in sterile polystyrene 96 well tissue culture plates (Falcon). For each sample, two 10 µL aliquots of each dilution (technical replicates) were spread onto CDMN agar supplemented with erythromycin (100 mg/L, Acros Organics). Erythromycin supplementation further reduces growth of bacteria from mouse feces and has no impact on C. difficile colony counts (data not shown). After 16–24 hours of anaerobic growth at 37°C, colonies were enumerated and technical replicates were averaged to determine C. difficile burdens in feces (limit of detection = 2×104 cfu/mL feces). Immediately following euthanasia at 19 days post infection, cecal contents were removed from mice, weighed, and flash frozen in liquid nitrogen. C. difficile was undetectable in all mice prior to inoculation with CDI.
Mouse diets
Mice were fed one of eight custom diets (Bio Serv) ad libitum: (1) a MAC-deficient control diet containing 68% glucose (w/v), 18% protein (w/v), and 7% fat (w/v) (MD, Bio-Serv); or diets containing 10% (w/v) of one of the following ingredients as a sole source of MAC: (2) inulin (Orafti HP; Beneo-Orafti group, Mannheim, Germany), (3) FOS (Orafti P95, Beneo-Orafti group, Mannheim, Germany), (4) gum arabic (Nutriloid Gum Arabic FT; TIC Gums, Belcamp, Maryland), (5) digestion resistant maltodextrin (Fibersol-2; ADM/Matsutani LLC, Chicago, Illinois), (6) lacto-N-neotetraose (LNnT; Kyowa Hakko, Tokyo, Japan), (7) 2′-fucosyllactose (2′-FL; Inalco SpA, Milano, Italy), or (8) 6′-sialyllactose (6′-SL; Inalco SpA, Milano, Italy). HMOs were enzymatically (LNnT) or chemically synthesized (2′-FL, 6′-SL). For MAC-containing diets, MAC ingredients were swapped for an equal quantity of glucose.
Human subjects/patient enrollment
Human stool samples were collected from patients receiving care at Stanford Health Care between January 2015 and November 2015 and participating in an IRB-exempt quality improvement project aimed at understanding the rates of C. difficile transmission in hematopoietic stem cell transplant patients. Samples are either from the patient’s first post-admission bowel movement or were collected at a frequency no more than once every 7 days post admission. Samples were collected and immediately assayed for C. difficile TcdB using the Xpert C. difficile assay (Cepheid). Patients with unformed, C. difficile+ stools, were considered to have CDI. After this diagnostic procedure, residual de-identified samples (regardless of CDI status) were stored at 4°C for no more than 48 hours and frozen at -80°C. Samples were subjected to targeted metabolomics, where the SCFAs acetate, propionate, and butyrate were quantified (see SCFA quantification, below).
Quantification of FOS-degradation products
To quantify FOS degradation by C. difficile, spent and non-inoculated PETC-F medium supplemented with 5 mg/mL FOS were filtered through 0.22 µm PVDF filters, dialysed through centrifuge filters (10 kDa MWCO, Millipore) and diluted with deionized water to bring the concentration of carbohydrate sources to a concentration of 1 µg/µL except for inulin (10 µg/µL). Samples were subjected to high performance anion exchange chromatography on a Dionex ICS-5000 system with an AS-AP autosampler and a pulsed amperometric detector, using a Dionex CarboPak PA1 column (4×250 mm Analytical, Thermo Scientific) with a corresponding 4×50 mm guard column. The following solvent gradient was used (A = 100 mM NaOH, B = 100 mM NaOH 1 M NaOAc): 0 to 60 minutes, 5% to 45% B; 60 to 70 minutes, 45% to 75% B. To prepare the reference chromatograms shown in Figure S1, individual 5 mg/mL solutions of fructose, glucose, sucrose, kestose, nystose, and FOS were prepared in distilled water, filtered through 0.22 µm PVDF filters, and subjected to HPAEC-PAD as described above.
SCFA quantification
Two methods were used to quantify SCFAs in cecal contents from mice and in human stool: (1) a GC-MS-based method used in our previous work [8] and (2) an LC-MS-based method developed to overcome restrictions to access of core facility equipment during the early stages of the COVID-19 pandemic at Stanford University.
GC-MS-based SCFA quantification. Cecal contents from mice or human stool (70-150 mg) were suspended in a final volume of 600 µl in ice-cold ultra-pure water and blended with a pellet pestle (Kimble Chase) on ice. The slurry was centrifuged at 2,350 × g for 30 seconds at 4°C and 250 µL of the supernatant was removed to a septum-topped glass vial and acidified with 20µL HPLC grade 37% HCl (Sigma Aldrich). Diethyl ether (500 µL) was added to the acidified cecal supernatant to extract SCFAs. Samples were then vortexed at 4°C for 20 minutes on ‘high’ and then were centrifuged at 1,000 × g for 3 minutes. The organic phase was removed into a fresh septum-topped vial and placed on ice. Then, a second extraction was performed with diethyl ether as above. The first and second extractions were combined for each sample and 250 µL of this combined solution was added to a 300 µL glass insert in a fresh glass septum-topped vial containing and the SCFAs were derivatized using 25 µL N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA; Sigma Aldrich) at 60°C for 30 minutes.
Analyses were carried out using an Agilent 7890/5975 single quadrupole GC/MS. Using a 7683B autosampler, 1 µL split injections (1:100) were made onto a DB-5MSUI capillary column (30 m length, 0.25 mm ID, 0.25 µm film thickness; Agilent) using helium as the carrier gas (1 mL/minute, constant flow mode). Inlet temperature was 200°C and transfer line temperature was 300°C. GC temperature was held at 60°C for 2 minutes, ramped at 40°C/min to 160°C, then ramped at 80°/min to 320°C and held for 2 minutes; total run time was 8.5 minutes. The mass spectrometer used electron ionization (70eV) and scan range was m/z 50–400, with a 3.75-minute solvent delay. Acetate, propionate, and butyrate standards (20 mM, 2 mM, 0.2 mM, 0.02 mM, 0 mM) were acidified, extracted, and derivatized as above, were included in each run, and were used to generate standard curves to enable SCFA quantification.
LC-MS-based SCFA quantification. The LC-MS-based SCFA quantification method was adapted from [51]. Briefly, cecal contents from mice (50 to 150 mg) were weighed on an analytical balance and diluted in extraction buffer containing: 80% HPLC-grade water (Fisher), 20% HPLC-grade acetonitrile (ACN; Fisher) and labelled isotopes of each SCFA measured (2.5 uM d3-acetic acid (Sigma Aldrich), 1 uM propionic-3,3,3-d3 acid (CDN Isotopes), 0.5 uM butyric-4,4,4-d3 acid (CDN Isotopes)). The volume of extraction buffer in microliters was 4X the mass of cecal contents in milligrams for each sample.
Acid-washed beads (150uM-212uM; SigmaAldrich G1145-10G) were added to the samples and the samples were shaken at 30 Hz for 10 minutes to homogenize and extract the metabolites. The samples were then incubated at -20°C for 1 hour and subsequently centrifuged at 4°C for 5 minutes at 12,000 rcf. 40 uL of the supernatant was transferred to a 96 well plate to which 20 uL of 200mM 3-nitrophenylhydrazine hydrochloride (Sigma Aldrich; dissolved in 50% ACN and 50% water) and 20 uL of 120 mM 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (Pierce; dissolved in 47% ACN, 47% water and 6% HPLC-grade pyridine (Sigma Aldrich)) were added. The plate was then sealed and shaken in an incubator at 37°C for 30 minutes. After 30 minutes the plate was cooled to 4°C and 20 uL of the reaction volume was transferred to 980 μL of a 90:10 (v/v) Water:ACN solution.
Analyses were carried out using an Agilent 6470 triple quadrupole LC/MS. Using a G7167B multisampler, 10uL injections were made onto an Acquity UPLC BEH C18 column (100 mm length, 2.1 mm inner diameter, 130 Å pore size, 1.7 um particle size; Waters) using water:formic acid (100:0.01, v/v; solvent A) and acetonitrile:formic acid (100:0.01, v/v; solvent B) as the mobile phase for gradient elution. The column flow rate was 0.35 mL/min; the column temperature was 40°C, and the autosampler was kept at 5°C. The binary solvent elution gradient was optimized at 15% B for 2 min, 15%–55% B in 9 min, and then held at 100% B for 1 min. The column was equilibrated for 3 min at 15% B between injections. The drying gas (N2) temperature was set to 300°C with a flow rate of 12 L/min. The sheath gas temperature was also set to 300°C with a flow rate of 12 L/min. The nebulizer gas was set to 25 PSI and the capillary voltage was set to 4200 V.
Quantification of analytes was done by standard isotope dilution protocols. In brief, serial dilutions of a 3 SCFA standard solution (10 mM, 1 mM, 0.1 mM, 0.01 mM, 0.001 mM, and 0 mM) were derivatized as above and included in each run to verify sample concentrations were within linear ranges. For samples within linear range, analyte concentration was calculated as the product of the paired internal standard concentration and the ratio of analyte peak area to internal standard peak area. A single product ion was used for each analyte, no secondary or qualifier ions were used. To ensure the highest signal-to-noise ratio, the following steps were taken. First, to ensure that the predicted singly derivatized species was the dominant precursor ion, full-mass Q1 scans were performed over the m/z range 100 to 300. Second, collision energies and fragmentor voltage were optimized using Agilent’s MassHunter Optimizer program with direct infusion of the derivatives from individual standard solutions containing 50mM of each fatty acid. Optimizer was set to search collision energies from -10V to -120V in 10V increments and select the two most intense product ions for optimization. Fragmentor voltage had minimal impact and was manually set to 75 V.
Measurement of maximum growth rate and lag time for in vitro growth experiments
Raw OD600 measurements of cultures grown in mRCM (see ‘Bacterial strains and culture conditions’, above) were exported from Gen5 and analyzed using the growth_curve_statistics.py script (see Code Availability, below). Growth rates were determined for each culture by calculating the derivative of natural log-transformed OD600 measurements over time. Growth rate values at each time point were then smoothed using a moving average over 150-min intervals to minimize artefacts due to noise in OD measurement data, and these smooth growth rate values were used to determine the maximum growth rate for each culture. To mitigate any remaining issues with noise in growth rate values, all growth rate curves were also inspected manually. Specifically, in cases where the growth_curve_statistics.py script selected an artefactual maximum growth rate, the largest local maximum that did not correspond to noise was manually assigned as the maximum growth rate. Additionally, lag time was calculated as half the time to reach the maximum growth rate.
Code availability
Python script that was used to compute maximum growth rate and lag time from growth curve data is freely available at https://github.com/HryckowianLab/Pensinger_2022.
Statistical analysis
Statistical analysis was performed using Graphpad Prism 9.1.0. Details of specific analyses, including statistical tests used, are found in applicable figure legends. * = p<0.05, ** = p<0.005, *** = p<0.0005, **** = p<0.0001.
Author Contributions
AJH, ATF, HAD, WVT, MMC, JOG, SKH, and BS performed experiments. AJH, ATF, HAD, MMC, WVT, JOG, CM, and DAP analyzed the data. DAP, AJH, HAD, JOG, and BS prepared the display items. EVR, CRB, JC, VA, RHB, LST, and JLS provided key insights, tools, and reagents. DAP and AJH wrote the paper.
All authors edited the manuscript prior to submission.
Declaration of Interests
This work was funded in part by Abbott. This funder contributed to the design of the experiments shown in Figure 3.
Figure Legends
Table 1. Bacterial strains used in this study. Related to Figures 1, 2, 3, and 5.
Figure S1. HPAEC-PAD Chromatograms. Reference chromatograms for fructose (light blue), glucose (orange), sucrose (gray), kestose (yellow), nystose (blue), and FOS (green) are shown. Chromatograms for FOS-supplemented PETC-F minimal medium (FOS + MM; dark blue) and spent PETC-F minimal medium (FOS + MM spent; peach) are also shown and duplicated from Figure 2B. Related to Figure 2.
Figure S2. Representative growth curves of thirteen C. difficile strains grown in the presence of sodium butyrate and sodium chloride. The thirteen C. difficile strains listed in Table 1 were grown anaerobically in mRCM supplemented with either 0, 6.25, 12.5, 25, or 50mM sodium butyrate or identical concentrations of NaCl for 24 hours. Each plot shows three representative growth curves per strain per condition and represents raw culture density (OD600) measurements for each strain tested. Symbols represent mean and standard deviation of replicates. Related to Figure 5.
Acknowledgments
We thank Keith Garleb (Abbott) for helpful comments and Niaz Banaei (Stanford University) for logistical assistance with human stool samples. This work was funded by Abbott (ZB40) and by grants from the following sources: NIH NIDDK (R01-DK085025 to JLS), NIH DCI (R01-CA200423 to CRB) an NIH postdoctoral NRSA (5T32AI007328 to AJH), a Stanford University School of Medicine Dean’s Postdoctoral Fellowship (AJH), a Feodor Lynen Postdoctoral Fellowship by the Alexander von Humboldt Foundation (BS), NSF Graduate Research Fellowships (WVT, CM), Howard Hughes Medical Institute (CRB), and startup funding from the University of Wisconsin-Madison (AJH). JLS received an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund and is a Chan Zuckerburg Biohub Investigator.