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Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics [1]

['Susan Hromada', 'Department Of Biochemistry', 'University Of Wisconsin-Madison', 'Madison', 'Wisconsin', 'United States Of America', 'Microbiology Doctoral Training Program', 'Ophelia S. Venturelli', 'Department Of Bacteriology', 'Department Of Chemical']

Date: 2023-05 

In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile’s antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile’s response to antibiotics, which could inform therapeutic interventions.

Funding: Research was sponsored by the National Institutes of Health and was accomplished under grant numbers R35GM124774 and R21AI159980. S.H. was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM008349. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: The datasets and computer code used in this study are available in the following databases: • Species abundance data, CFU data, hydrogen sulfide data and qRT-PCR data are available from Zenodo: https://doi.org/10.5281/zenodo.7626486 • RNA-seq data is available from Zenodo: https://doi.org/10.5281/zenodo.7049035 and https://doi.org/10.5281/zenodo.7049027 • Modeling computer scripts are available from Zenodo: https://doi.org/10.5281/zenodo.7726490 .

Using a bottom-up approach, we perform a detailed and quantitative characterization of how human gut microbes impact C. difficile’s response to these antibiotics. We show that gut microbes infrequently alter C. difficile’s minimum inhibitory concentration (MIC), but gut microbes frequently impact C. difficile’s response to subinhibitory concentrations of antibiotics. In communities with antibiotic-sensitive species that also compete with C. difficile, we observe that C. difficile’s growth is enhanced in the presence of low concentrations of antibiotics due to competitive release. A dynamic ecological model representing the antibiotic recapitulates these trends. In addition, we demonstrate that Desulfovibrio piger substantially increases C. difficile’s MIC for metronidazole. We investigate the mechanism determining the increased MIC of C. difficile using transcriptional profiling and media perturbations. Our data suggest D. piger’s impact on the environment induces a metal starvation transcriptional response in C. difficile, leading to down-regulation of enzymes required to reduce metronidazole to its active form. In sum, biotic interactions shape C. difficile antibiotic susceptibility at both subinhibitory and minimal inhibitory concentration regimes via distinct mechanisms. These results highlight the need to consider biotic interactions in the design of future therapeutic treatments to eradicate pathogens.

We used a diverse human gut community [ 4 ] to study the impact of microbial interactions on C. difficile’s antibiotic susceptibility. We focused on 2 antibiotics, vancomycin and metronidazole, that are used to treat C. difficile infections. Vancomycin is a glycopeptide that inhibits cell wall synthesis whose activity is specific to gram-positive bacteria [ 18 ]. Metronidazole is a DNA-damaging agent that is effective against both gram-positive and gram-negative anaerobic bacteria. Metronidazole is a prodrug which is inactive until its nitro group is reduced to nitroso radicals in the cytoplasm of anaerobic bacteria [ 19 ]. The proposed mechanism of metronidazole reduction is due to the cofactors ferredoxin and/or flavodoxin in reactions catalyzed by multiple enzymes including reductases, hydrogenases, and pyruvate ferredoxin/flavodoxin oxidoreductase (PFOR) [ 20 – 22 ]. Metronidazole, previously a recommended first-line treatment, is now only recommended in rare cases due to an observed decrease in its clinical effectiveness [ 23 , 24 ].

While previous studies have identified specific types of interspecies interactions that impact antibiotic susceptibility, the prevalence of susceptibility-altering microbial interactions across different microbial communities is not well understood. In a human urinary tract infection community, around a third of total interactions between species were estimated to yield a change in the susceptibility to 2 different antibiotics based on spent media experiments [ 12 ]. Studies of a fruit fly microbiome, a multispecies wound infection biofilm, and a multispecies brewery biofilm each identified a change in antibiotic susceptibility of a given species in the community compared to monospecies [ 13 – 15 ]. By contrast, no significant changes in antibiotic susceptibility were observed for 15 characterized species in a community of human gut microbes or an Escherichia coli pathogen introduced into a porcine microbiome [ 16 , 17 ]. Based on this observed variation in the contribution of interspecies interactions to antibiotic susceptibility across different systems, it is not known whether microbial interactions can impact C. difficile’s antibiotic susceptibility.

Previous studies have shown that interspecies interactions can alter a given microbe’s response to antibiotics by increasing or decreasing susceptibility compared to monoculture [ 9 ]. One example is exposure protection, where susceptible microbes are protected from an antibiotic by species that degrade the antibiotic [ 10 ]. In addition, a previous study showed that an increase in antibiotic susceptibility occurred when the growth of a resistant microbe depended on cross-feeding with another organism that was susceptible to the antibiotic [ 11 ].

Similar to other pathogens, C. difficile antibiotic susceptibility has been studied using in vitro experiments of monoculture growth. However, monoculture experiments do not consider how interactions with resident community members can modify the antibiotic susceptibility of a pathogen. For example, monospecies antibiotic susceptibility of the pathogen Pseudomonas aeruginosa did not always correlate with the efficacy of treatment for polymicrobial infections [ 8 ]. If microbial interactions substantially decrease the pathogen’s antibiotic susceptibility, treatments based on monoculture susceptibility to antibiotics may not be effective in eradicating the pathogen. Alternatively, if communities increase the pathogen’s antibiotic susceptibility, the standard antibiotic dosage may exceed the dose needed to eradicate the pathogen, yielding unnecessary and avoidable disruption to the native microbiota. Understanding how constituent members of microbiota alter a pathogen’s susceptibility to an antibiotic could be used to guide the design of treatments to eradicate the pathogen.

The bacterial pathogen Clostridioides difficile can infect the human gastrointestinal tract, an environment teeming with a dense microbiota. Gut microbiota can inhibit C. difficile’s growth and ability to persist over time in the human gut, a phenomenon known as colonization resistance [ 1 ]. The key role of colonization resistance is illustrated by the increased risk of C. difficile infection after treatment with antibiotics that decimate the microbiota [ 2 ] and by the efficacy of fecal microbiota transplants from healthy human donors in eliminating recurrent C. difficile infections [ 3 ]. Previous studies have provided a deeper understanding of interactions between gut microbiota and C. difficile. For example, interspecies interactions between individual gut microbes and C. difficile have been studied in vitro and analyses of human microbiome data have identified gut microbes whose presence or absence is associated with altered outcomes of C. difficile infection [ 4 – 6 ]. Multiple mechanisms of interaction have been determined, such as inhibition of C. difficile germination by Clostridium scindens via production of secondary bile acids [ 6 ] and promotion of C. difficile growth by Bacteroides thetaiotaomicron via succinate cross-feeding [ 7 ]. While much is known about how the microbiota impacts C. difficile growth, how the microbiota impacts C. difficile antibiotic susceptibility is largely unknown.

Results

Enhancement of C. difficile abundance in multispecies communities in the presence of low antibiotic concentrations We investigated if the OD600-based absolute abundance trends observed in pairwise communities persisted in multispecies communities that are more representative of human gut microbiota. The communities consisted of 2- and 3-member core communities (CC) predicted to display minimal biotic inhibition of C. difficile guided by a previously developed dynamic computational model of our system [4] (CC-2: D. piger and Faecalibacterium prausnitzii. CC-3: D. piger, F. prausnitzii, and Eggerthella lenta). In addition to this core community, we introduced at least 1 antibiotic-sensitive inhibitor, antibiotic-resistant inhibitor, or a combination of species in these 2 groups, creating 10 communities for metronidazole and 12 communities for vancomycin (3- to 6-members). To further explore the behavior of communities in response to antibiotic perturbations, we characterized the response of 3-, 4-, 5-, 13-, or 14-member communities (19 total) with no consistent core members to metronidazole. In 6 of 29 communities characterized in the presence of metronidazole, C. difficile’s MIC was 4-fold greater than in monoculture, increasing from 6 μg/mL to 24 μg/mL. The remaining communities displayed moderate (2-fold, 14 communities) or no change (9 communities) (S5A Fig, S1 Table). In 4 of 12 communities examined in the presence of vancomycin, C. difficile’s growth was strongly inhibited at all concentrations and no MIC could be calculated (S5B Fig, S2 Table, indicated as “NG”). The remaining 8 communities displayed either a moderate increase in MIC (2-fold, 2 communities) or no change (6 communities) (S5B Fig, S2 Table). Consistent with the pairwise community data, C. difficile’s metronidazole MIC was altered in certain communities, but C. difficile’s vancomycin MIC was not substantially altered in any of the characterized communities. We tested if the multispecies communities containing antibiotic-sensitive biotic inhibitors displayed an enhancement in C. difficile’s growth at subMICs. In response to metronidazole or vancomycin, C. difficile’s growth was enhanced in response to at least 1 subMIC in most communities (21 of 29 communities for metronidazole, 9 of 12 communities for vancomycin, Figs 3E and S6). Consistent with the pairwise community data, the degree of biotic inhibition of C. difficile was positively correlated with the magnitude of the maximum subMIC fold change (Fig 3F). In addition, the correlation was stronger for communities composed of only antibiotic-sensitive biotic inhibitors (Fig 3F). Consistent with these trends, the number of sensitive inhibitors in the community and the absolute abundance of sensitive inhibitors displayed a positive correlation with the maximum subMIC fold change (S7A Fig). Communities containing antibiotic-sensitive biotic inhibitors had a significantly higher average subMIC fold change than communities containing only resistant inhibitors or a mix of both inhibitor types (S7B Fig). Communities containing a mix of antibiotic sensitive and resistant inhibitors, mirroring the makeup of the gut microbiota, displayed low subMIC fold changes similar in magnitude to resistant biotic inhibitor communities (S7B Fig). In communities with a non-zero abundance of resistant biotic inhibitors, the correlation between the growth enhancement of C. difficile and the number of sensitive inhibitors or abundance of sensitive inhibitors vanished (S7A Fig). These data suggest that resistant biotic inhibitors can suppress the C. difficile growth enhancement caused by sensitive biotic inhibitors. We analyzed if the growth enhancement of C. difficile in multispecies communities could be predicted based on the sum of the growth enhancements of C. difficile in pairwise communities. While the relationship displayed a moderate correlation, it was not additive (S7C and S7D Fig). This demonstrates that community effects, such as interspecies interactions between constituent community members and growth enhancement suppression by resistant inhibitors, play key roles in determining the magnitude of growth enhancement of C. difficile at subMICs. Overall, these results demonstrate that C. difficile growth enhancement at subMICs can occur in multispecies communities, suggesting this response may occur in the human gut microbiome.

A dynamic ecological model representing pairwise interactions and monospecies antibiotic susceptibilities can capture the response of C. difficile to subMICs We hypothesized that a model capturing the dynamics of species growth, interspecies interactions, and monospecies antibiotic susceptibilities could recapitulate the observed trends in C. difficile growth enhancement at subMICs. We assumed that inferred interspecies interactions in the absence of antibiotics contributed to the community response to antibiotics. We tested whether a model that neglects complex antibiotic-dependent interspecies interactions could predict the qualitative trends of C. difficile growth at subMICs. The gLV model is a system of coupled ordinary differential equations that captures individual species’ growth rate and pairwise interactions with each community member. We use an expansion of the generalized Lotka–Volterra model (gLV) that captures antibiotic perturbations [30] (Fig 4A). In this expanded model, the growth of each species is modified by an antibiotic term consisting of the concentration of antibiotic and the susceptibility of each species (B i ) (Methods, Fig 4A). PPT PowerPoint slide

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TIFF original image Download: Fig 4. A modified generalized Lotka–Volterra model of community dynamics in response to antibiotics captures trends in antibiotic concentrations lower than the MIC in the presence of antibiotic sensitive biotic inhibitors. (A) Top: Schematic of the antibiotic expansion of the gLV model. Bottom: Schematic of accuracy metric used in panel B. (B) Qualitative accuracy of multiple models for pairwise and multispecies communities. Models include (1) standard gLV model lacking an antibiotic term; (2) gLV model with randomly shuffled antibiotic susceptibility parameters (average of 100 permutations), and 100 shuffled values shown as points, shaded according to the absolute value of the difference between the shuffled C. difficile antibiotic susceptibility parameter and the original C. difficile antibiotic susceptibility parameter; (3) gLV model with antibiotic susceptibility terms inferred from monospecies with all interaction parameters set to zero (a ij = 0, where i! = j); (4) full gLV model with antibiotic susceptibility terms inferred from monospecies data. (C) Simulated maximum subMIC fold changes for a focal species in a pairwise community for 48 h for a representative parameter set. Growth rates of the 2 species are equal (u i = u j = 0.25), intraspecies interactions are equal (a ii = a jj = −0.8), and initial ODs are equal (x i (0) = x j (0) = 0.0022). The interspecies interaction coefficients a ji are set to 0 and B i is set to −2. (D) Simulated max subMIC fold change for a focal species in a pairwise community for 1,000 randomly sampled parameter sets. Parameters B j and a ij are set to constant values of 0 or −6 and 0 or −1.25, respectively. Other parameters were randomly sampled between upper and lower bounds. The lower and upper bounds on each parameter value are a ji (−1.25, 1.25), growth rates (0, 1), intraspecies interactions (−1.25, 0), B i (−6, 0). Gray horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. The data and modeling scripts underlying panels BCD in this figure can be found in DOI: 10.5281/zenodo.7726490. MIC, minimum inhibitory concentration; subMIC, sub-minimum inhibitory concentration. https://doi.org/10.1371/journal.pbio.3002100.g004 The monospecies antibiotic susceptibility parameters were inferred from measurements of individual species growth in the presence of a range of antibiotic concentrations (2-fold dilutions) (Methods, S1 Fig). We used growth rate and interspecies interaction parameters that were previously inferred based on absolute abundance measurements of 159 monospecies and communities (2- to 14-member) in the absence of antibiotics [4]. We evaluated whether this model could qualitatively predict the trends in growth enhancement of C. difficile in the subMIC range (S8 and S9 Figs). C. difficile’s response to each antibiotic concentration was classified into 3 categories (C. difficile subMIC fold-change > 1, C. difficile subMIC fold-change ≤ 1, or no growth of C. difficile, Fig 4A). The expanded gLV model correctly predicts C. difficile’s qualitative response to 71% of antibiotic conditions (Figs 4B and S10A). We designed a set of null models to evaluate the contribution of different terms of the expanded gLV model to model performance. The full model displays higher accuracy than a null model that lacked antibiotic susceptibility terms or has randomly shuffled antibiotic susceptibility parameters (47% and 53% accuracy, Fig 4C). These data highlight that the fitted monospecies susceptibilities play a major role in the model’s predictive performance. The full model also outperforms a null model that lacks interspecies interaction terms (65%, Fig 4C), indicating that the inferred interspecies interactions contribute to the growth enhancement of C. difficile in response to subMICs. Taken together, these findings indicate that biotic inhibition and monospecies antibiotic susceptibility are major variables determining the growth of C. difficile in response to subMICs in microbial communities. We explored model simulations to determine if the enhancement of C. difficile growth at subMICs required a sensitive biotic inhibitor in a wide variety of communities, beyond those that were experimentally characterized. To this end, we simulated 1,000 pairwise communities with a wide range of growth rates, interspecies interactions, and antibiotic susceptibilities. When paired with a sensitive biotic inhibitor, growth is enhanced at subMICs, consistent with the trends observed in our experiments (Fig 4D). By contrast, growth enhancements are not observed in simulated communities that lack an antibiotic-sensitive biotic inhibitor of the focal species (Fig 4D). This trend is also present in larger communities (S10B Fig). The focal species’ growth enhancement at subMICs increases with biotic inhibition and antibiotic susceptibility of the inhibitor species (Fig 4E). Overall, these model simulations suggest that the antibiotic sensitive inhibitor trend we observe with C. difficile in human gut communities is generalizable to other species and communities with variable richness, interaction networks, and antibiotic susceptibilities.

D. piger substantially increases C. difficile’s minimum inhibitory concentration to metronidazole Changes in antibiotic susceptibility of a pathogen due to significant microbial interactions could reduce the efficacy of antibiotic treatments. In the presence of D. piger, we observed a substantial increase in C. difficile MIC, in addition to a moderate growth enhancement of C. difficile at subMICs (Fig 2B). Consistent with the proposed antibiotic-sensitive biotic inhibition mechanism, D. piger was a weak biotic inhibitor of C. difficile and was more sensitive to metronidazole than C. difficile (Figs 2A and 3C). However, the substantial increase in C. difficile MIC in the presence versus absence of D. piger (≥24 μg/mL compared to 6 μg/mL) was not explained by the proposed antibiotic-sensitive biotic inhibition mechanism and the expanded gLV antibiotic model failed to predict this trend (S8A Fig). While competitive interactions (i.e., resource competition or biological warfare) contributed to the observed subMIC growth response, the mechanism of increased metronidazole MIC in the presence of D. piger was unknown. We investigated the robustness of this trend across different environmental conditions and for different C. difficile isolates. C. difficile displayed an increased MIC in coculture with D. piger and in monoculture in D. piger’s spent media (≥8-fold increase in MIC) (Fig 5A). These data indicate that the increase in C. difficile metronidazole MIC was not dependent on cell-to-cell contact with D. piger or prior exposure of D. piger to metronidazole. C. difficile displayed a higher metronidazole MIC in D. piger spent media harvested at late exponential phase/early stationary phase than in spent media harvested at earlier time points in exponential phase (S11A and S11B Fig). Further, multiple C. difficile clinical isolates had an increased MIC in D. piger spent media (S11C Fig). Finally, C. difficile exhibited a higher metronidazole MIC in coculture with D. piger in a different chemically defined growth medium (S11D Fig). These data demonstrate that the protective effect of D. piger on C. difficile’s response to metronidazole was robust to variations in C. difficile’s strain background, media composition, and the growth phase of D. piger. PPT PowerPoint slide

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TIFF original image Download: Fig 5. D. piger increases the metronidazole MIC of C. difficile and induces metal starvation genome-wide transcriptional response. (A) Abundance of C. difficile at 41 h in response to different metronidazole concentrations. The OD600 value for C. difficile in the presence of D. piger is calculated OD600 (OD600 multiplied by relative abundance from 16S rRNA gene sequencing). The x-axis is semi-log scale. Data points represent biological replicates. Lines indicate average of n = 2 to n = 4 biological replicates. (B) Schematic of genome-wide transcriptional profiling experiment. The x-axis is semi-log scale of metronidazole (MTZ) concentration. Lines indicate average C. difficile OD600 at 14 h for n = 4 biological replicates. Circles indicate conditions sampled for RNA-Seq. (C) C. difficile DEGs between culture conditions. DEGs are defined as genes that displayed greater than 2-fold change and a p-value less than 0.05. (D) Clustered heatmap of RPKM for each gene (rows) and in each sample (columns) for C. difficile. Each column represents the average of n = 2 biological replicates. Hierarchical clustering was performed based on Euclidean distance using the single linkage method of the Python SciPy clustering package. (E) Volcano plot of log transformed transcriptional fold changes for C. difficile in the presence of D. piger. Gray vertical lines indicate 2-fold change and the gray horizontal line indicates the statistical significance threshold (p = 0.05). Blue indicates genes annotated to be involved in metal import. Red indicates genes predicted to contain iron-sulfur clusters by MetalPredator [31]. (F) Enriched gene sets in C. difficile grown in the presence of D. piger compared to C. difficile grown in fresh media. All gene sets with significant enrichment scores from GSEA are shown. Gene sets are defined using modules from the KEGG. (G) Bar plot of the log transformed fold changes of a set of genes across different conditions. Gray horizontal lines indicate 2-fold change. The data underlying panels AB in this figure can be found in DOI: 10.5281/zenodo.7626486 and the data underlying panels CDEFG in this figure can be found in DOI 10.5281/zenodo.7049035 and 10.5281/zenodo.7049027 and S3 and S4 Tables. DEG, differentially expressed gene; GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MIC, minimum inhibitory concentration; RPKM, reads per kilobase million. https://doi.org/10.1371/journal.pbio.3002100.g005 D. piger could alter C. difficile’s metronidazole MIC via a direct chemical interaction with metronidazole which reduces its efficacy (e.g., degradation or sequestration) or an indirect effect that modifies the activities of C. difficile’s intracellular networks which in turn yields an increase in metronidazole MIC. To test for a direct chemical interaction, we incubated metronidazole in either D. piger spent media or fresh media and characterized C. difficile’s growth response to each of these conditions in fresh media. C. difficile’s metronidazole MIC was equal in these conditions, indicating that compounds present in D. piger’s spent media do not directly interact with metronidazole to reduce its activity (S11E Fig).

Differentially expressed genes in C. difficile in the presence of D. piger are linked to metronidazole resistance Since the majority of C. difficile’s DEGs could be explained by metal limitation, we investigated the connection between metal limitation and C. difficile metronidazole MIC. To identify potential connections, we compared the set of DEGs in the presence of D. piger to genes previously shown to play a role in metronidazole resistance in C. difficile. In 2 studies of metronidazole-resistant C. difficile mutants, iron-related genes were implicated in metronidazole resistance. In a study of C. difficile strain ATCC 700057, multiple mutants with increased metronidazole resistance acquired a truncation in feoB1, which resulted in reduced intracellular iron and a shift to flavodoxin [41]. Similarly, in a metronidazole-resistant mutant of a NAP1 strain, iron-uptake genes were down-regulated and a shift to flavodoxin was observed [42]. In each of these studies, the proposed mechanism of metronidazole resistance was attributed to down-regulation of enzymes predicted to reduce metronidazole to its active form. In the D. piger conditions, enzymes hypothesized to reduce metronidazole were also down-regulated, namely ferredoxin genes (fdxA, CDR20291_0114, CDR20291_3444), pyruvate-ferredoxin/flavodoxin oxidoreductase (PFOR) (nifJ), and hydrogenases (hydA, hydN1, hydN2) (Table 1, Fig 5G) [20,21]. Down-regulation of these enzymes in D. piger conditions may reduce the rate of conversion of metronidazole into its active form, thus increasing the tolerance of C. difficile. The down-regulated enzymes are each predicted to contain iron clusters (S4 Table), and hydrogenases are known to contain nickel cofactors [39], suggesting that their down-regulation and the subsequent decrease in metronidazole susceptibility could be attributed to metal limitation. This mechanism of resistance has been proposed in other species beyond C. difficile. For example, Bacteroides fragilis metronidazole-resistant mutants displayed reduced PFOR expression [21]. Therefore, if this mechanism was responsible for the increase in C. difficile’s metronidazole MIC by D. piger, Bacteroides species should also display a higher MIC. Indeed, B. thetaiotaomicron displayed a higher metronidazole MIC in D. piger spent media compared to fresh media (S14 Fig). We also identified that cbiN, a putative cobalt transporter that was down-regulated in the D. piger conditions (Table 1, Fig 5G) and in iron-limited media [35] has been previously implicated in metronidazole resistance. A single SNP present in cbiN distinguished a metronidazole-resistant R010 isolate of C. difficile from a metronidazole sensitive R010 isolate isolated from the same patient [43]. In our data, cbiN was down-regulated by 10-fold in the D. piger coculture and 4-fold in D. piger spent media. Another enzyme that was down-regulated in the D. piger conditions, altronate hydrolase (uxaA), has potential connections with metronidazole resistance. Notably, altronate hydrolase was substantially down-regulated in the coculture with D. piger, where the magnitude of this decrease in transcript abundance was the second largest in the transcriptome (>1,000-fold reduction, Table 1, Fig 5G). Altronate hydrolase catalyzes the dehydration of the six-carbon altronate as part of galacturonate degradation [44]. One of the 17 mutations that distinguished a metronidazole-resistant NAP1 C. difficile strain from the metronidazole sensitive C. difficile reference strain occurred in the altronate hydrolase gene. The mutation in altronate hydrolase was one of 3 frameshift mutations in the mutant and likely rendered altronate hydrolase nonfunctional [45]. Studies in E. coli have demonstrated that this enzyme requires iron or manganese for its catalytic activity [46], consistent with its strong down-regulation in metal-limited media [35]. The mutations in cbiN and uxaA in metronidazole-resistant C. difficile isolates suggests that the observed down-regulation of cbiN and uxaA in response to metal limitation may contribute to C. difficile’s increased metronidazole MIC. These genes are potential links between metal limitation and metronidazole susceptibility, in addition to the down-regulation of metal-containing oxidoreductases, hydrogenases, and ferredoxins predicted to convert metronidazole into its active form.

Hydrogen sulfide production by D. piger promotes metal sequestration The global changes in C. difficile’s gene expression profile suggest that D. piger created a metal-limited environment. We hypothesized that these metal limitations could have been caused by hydrogen sulfide produced by D. piger [47]. In D. piger cultures, we observed a characteristic black precipitate (ferrous sulfide) that forms when iron combines with produced hydrogen sulfide [48]. Other divalent metals can precipitate with sulfide as well [49,50] and may be precipitating in addition to ferrous sulfide in the presence of D. piger. To estimate how much metal is precipitated by D. piger produced sulfide, we quantified the amount of sulfide in a monoculture of D. piger over time (Methods). The amount of sulfide peaked in late exponential phase at 1.4 mM (Fig 6A). Hydrogen sulfide is volatile and escapes during growth. Therefore, the total amount of produced hydrogen sulfide was likely higher than the measured concentration. While C. difficile also produces a small amount of hydrogen sulfide, the amount of hydrogen sulfide in the C. difficile and D. piger coculture was similar to the amount in the D. piger monoculture (S15A Fig). The produced hydrogen sulfide in the D. piger spent media and D. piger coculture (>1.4 mM) was in excess of the total divalent metal concentration in the media (1.2 mM iron and micromolar concentrations of other metals, S15B Fig). Therefore, the produced hydrogen sulfide could precipitate all divalent metals in the media, creating a metal-limited environment for C. difficile. This would explain the metal-limited signature in C. difficile’s gene expression profile. Supporting this hypothesis, iron depletion due to the precipitation with excess sulfide has been previously shown to induce Fur-regulated genes in C. difficile [51]. PPT PowerPoint slide

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TIFF original image Download: Fig 6. Supplementation of D. piger spent media with metals eliminates the protective effect on C. difficile in the presence of metronidazole. (A) Line plot of absolute abundance (black) and hydrogen sulfide production (red) of D. piger in monoculture. Data points represent biological replicates. Each biological replicate is the average of 2 technical replicates. Line indicates average of n = 4 biological replicates. (B) Line plot and bar plot of C. difficile metronidazole (MTZ) susceptibility in D. piger spent media (SM) with and without metal supplementation at 48 h. Metal supplementation condition contained 1 mM of Co, Mn, Ni, Zn, and Fe. The x-axis is semi-log scale (line plot). Data points represent biological replicates. Lines indicate average of n = 4 biological replicates. Asterisks indicate significant difference between conditions with and without metal supplementation (*P < 0.05, **P < 0.01, ***P < 0.001) according to an unpaired t test, “ns” indicates not significant. Statistical significance was performed at the lower of the 2 MICs and concentrations between the MICs of the 2 conditions. Bar plot displays MIC of data shown in line plot. Data points represent the MIC of n = 4 biological replicates. Bar represents the MIC determined based on average OD600 of n = 4 biological replicates. (C) Line plots and bar plots of C. difficile antibiotic susceptibility in D. piger spent media (SM) with and without supplementation of individual metals at 48 h. Each x-axis is semi-log scale. Data points represent biological replicates. Lines indicate the average of n = 4 biological replicates. Bar plot displays MIC of data shown in line plot. Data points represent MIC of n = 4 biological replicates. Bar represents the MIC determined based on an average OD600 of n = 4 biological replicates. Statistical significance as described in panel B. For metals with a change in MIC between the 2 conditions, statistical significance was tested at the lower of the 2 MICs and any concentrations between the MICs of the 2 conditions. (D) Bar plot of relative gene expression of 3 genes in C. difficile grown in fresh media, D. piger spent media (DP SM), and D. piger spent media with metal supplementation as detected by qRT-PCR. Fold change was calculated using the 2−ΔΔCt method (Methods). Data points represent biological replicates, with each point calculated as the average of 3 technical replicates. Bar indicates the average of n = 4 biological replicates. Statistical significance was determined based on ΔCt values (S15 Fig). (E) Schematic of proposed mechanism for D. piger alteration of C. difficile metronidazole susceptibility. (Left) In monoculture, metals are available in the environment, and metal containing enzymes in C. difficile are expressed and reduce the prodrug metronidazole to its active form. (Right) In coculture with D. piger, hydrogen sulfide produced by D. piger sequesters metals, which forms metal sulfide precipitates. In response to metal limitation, the expression of metal binding proteins in C. difficile that reduce the conversion of metronidazole from prodrug to its active form is reduced. This in turn reduces the rate of conversion of metronidazole from its inactive to active form and increases the tolerance of C. difficile. The data underlying panels ABCD in this figure can be found in DOI: 10.5281/zenodo.7626486. MIC, minimum inhibitory concentration. https://doi.org/10.1371/journal.pbio.3002100.g006

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