In this research, methods of increasing the corrosion resistance of reinforced concrete were experimentally investigated. The study used silica fume and fly ash at optimized percentages of 10 and 25% by cement weight, polypropylene fibers at a ratio of 2.5% by volume of concrete, and a commercial corrosion inhibitor, 2-dimethylaminoethanol (Ferrogard 901), at 3% by cement weight. The corrosion resistance of three types of reinforcements, mild steel (STt37), AISI 304 stainless steel, and AISI 316 stainless steel, was investigated. The effects of various coatings, including hot-dip galvanizing, alkyd-based primer, zinc-rich epoxy primer, alkyd top coating, polyamide epoxy top coating, polyamide epoxy primer, polyurethane coatings, a double layer of alkyd primer and alkyd top coating, and a double layer of epoxy primer and alkyd top coating, were evaluated on the reinforcement surface. The corrosion rate of the reinforced concrete was determined through results of accelerated corrosion and pullout tests of steel-concrete bond joints and stereographic microscope images. The samples containing pozzolanic materials, the corrosion inhibitor, and a combination of the two showed significant improvement in corrosion resistance by 7.0, 11.4, and 11.9 times, respectively, compared to the control samples. The corrosion rate of mild steel, AISI 304, and AISI 316 decreased by 1.4, 2.4, and 2.9 times, respectively, compared to the control sample; however, the presence of polypropylene fibers reduced the corrosion resistance by 2.4 times compared to the control. One of the main reasons for the decreasing durability of reinforced concrete is the corrosion of steel reinforcement in concrete structures. Reinforcement corrosion in concrete is an electrochemical process which consists of anode, cathode, and an electrolyte which is the aqueous soluble electrolyte inside the concrete cavities ,2,3. TheThe corrosion of reinforcement in existing structures affects bond behavior, structural performance, and fatigue damage ,5,6. PreIn general, the methods of increasing the corrosion resistance of reinforced concrete structures can be divided into several general categories; (1) incorporating additives in the concrete mix design, (2) changing of steel reinforcement, (3) adding corrosion inhibitors to concrete mix design, (4) using a coating on steel reinforcement in concrete, (5) considering cathodic protection with direct current and sacrificing anodes, and (6) adopting electrochemical extraction of chloride ion to reduce corrosion ,12,13,14Various additives have been suggested by researchers to enhance the durability properties of concrete ,16,17. AAccording to Malhotra‘s research , the useDotto et al.  studied Another effective additive for concrete is polypropylene fibers. These fibers have a melting point of approximately 165 °C and are chemically neutral, making them resistant to many chemicals. Their hydrophobic surface prevents them from absorbing the cement paste. Thus, the addition of these fibers does not affect the water content in concrete. With shear stresses in the range of 45–65 ksi, these fibers are used in multi-strand form. Optimal use of fibers delays crack propagation, reducing steel rebar corrosion and improving concrete’s compressive strength ,49,50. ABy increasing the resistance of rebars, their resistance to corrosion can be improved. According to the Norwegian Urban Research Association report, the corrosion resistance of AISI 304 and AISI 316 steels is 19 and 25 times higher than that of mild steel, respectively . Addari The use of corrosion inhibitors is aimed at preventing the reaction of chloride ions with the reinforcement surface and increasing the time it takes for chloride ions to penetrate the concrete. The ideal inhibitor is a chemical compound that can prevent corrosion of the reinforcement without harming the concrete properties. 2-Dimethylaminoethanol, sold under the brand Ferrogard 901 and belonging to the Amino Alcohol Inhibitors group, has been studied for its inhibitory mechanism. Jamil et al. ,57 reporOne of the most effective ways to prevent steel-reinforced concrete from corroding is to use protective coatings on the reinforcement surface. Galvanized coating, made of multiple layers of iron-zinc alloys that are metallurgically bonded to the underlying steel, is a commonly used corrosion-resistant coating . AnotherIt is likely that using inhibitors and additives in concrete, as well as applying protective coatings on reinforcements, can significantly improve the corrosion resistance of reinforcements. However, the combined effects of these methods have not been thoroughly studied. In this study, five different concrete mixes were prepared, using Ferrogard 901 inhibitors, silica fume, fly ash, polypropylene fibers, and combinations of these additives, to examine their impact on the corrosion rate of concrete. Three types of reinforcements were used: st37 (mild steel), AISI 304 (stainless steel), and AISI 316 (stainless steel). Nine different coating systems were applied on the reinforcement surface to increase corrosion resistance: hot-dip galvanized coating, alkyd-based primer, zinc-rich epoxy primer, alkyd top coating, polyamide epoxy top coating, polyamide epoxy primer, polyurethane coatings, double layer of alkyd primer and alkyd top coating, and a double layer of epoxy primer and alkyd top coating. An accelerated corrosion test (ACT) and a pullout test of the reinforcements were conducted, and stereographic microscope images were used to assess the corrosion rate of each system and compare the effectiveness of all the protective techniques quantitatively.This research offers a comprehensive understanding of how to enhance the corrosion resistance of steel-reinforced concrete. By examining various concrete mixes, additives, reinforcing bars, coating systems, and primers, it provides a valuable source of information on how to select the most appropriate mixture, rebar, coating, and primer to meet specific requirements. A systematic study is carried out to evaluate the combined or individual impact of these systems and to compare their performance, leading to a unique conclusion on the best approach to increase corrosion resistance.3, and tensile strength of 350 MPa. Additionally, FerroGard 901 inhibitor from the SIKA  was added to the concrete mix as an inhibitory additive. In this study, three types of alloys were used, including mild steel (st37) and two different grades of stainless steel (AISI 316 and AISI 304) based on the American Iron and Steel Institute . The chemical composition of the alloys is shown in In this study, five different concrete mixes were prepared. The first mix was prepared according to the ACI recommendations with a water-to-cement ratio of 0.40 and exposed aggregate and cement to corrosive environments. To evaluate the effect of the FerroGard 901 inhibitor on corrosion resistance, the second mix included the inhibitor at a 3% weight ratio of cement. The third mix was created by adding fly ash (at 25% weight ratio of cement) and silica fume (at 10% weight ratio of cement) to the first mix, with a water-to-binders ratio of 0.40. The fourth mix used 5% fly ash, 10% silica fume, and the FerroGard 901 inhibitor simultaneously. The fifth mix evaluated the influence of polypropylene fibers, at an optimum percentage of 2.5% by volume of concrete.In each mix, sand and aggregate were mixed for two minutes, followed by the addition of cement in one minute. Then, water was slowly added to the mix over two minutes, along with the superplasticizer and FerroGard 901 inhibitor. The maximum time allowed for filling the mold was half an hour, after which the concrete was vibrated with a frequency of 30 Hz using a vibration table and poured into the molds in two stages with a 20-s interval. The concrete samples were removed from the molds after 24 h and immersed in 27 °C fresh water for 14 days to complete the hydration reaction.2 cross-sectional area of the 120 mm steel reinforcement was used for corrosion resistance testing, with the reinforcement placed in the middle of the cylindrical mold. The molds were carefully sealed to prevent any water leakage.The 10 mm diameter steel reinforcement was cut into 120 mm length rebars. The rebars were then treated in a Sonica-1200M sonicator , with a 0.005% urotrop in inhibitor in a hydrochloric acid solution to remove iron rust (for st37 reinforcements). After being treated, the rebars were washed with tap water and sanded with 60 to 600 sandpapers to achieve the best surface quality for painting. The paint thickness was selected based on the manufacturer’s recommendation for corrosive environments, as shown in In this study, the accelerated corrosion test with anodic potential was conducted according to ASTM-G 109, West Conshohocken, PA, USA . This teFour power supplies were used simultaneously with a maximum voltage of 32.5 V and a current of 4 amperes. The device had 64 inputs, each connected to an auxiliary electrode, and 8 microcontrollers were programmed to measure and record the flow at 1 h intervals. The software displayed the changes in corrosion current for each specimen at each time interval. The positive pole of the sources was connected to a board with 64 outputs, and each output was connected to a sample of steel reinforcement. The current varied in the range of 100 µA to 500 mA due to changes in concrete strength after the application of anodic potential.The pullout test of steel–concrete bond joints was carried out using a 150-ton capacity tensile testing machine manufactured by Santam , with a loading speed of 10 mm/minute. A retainer was made to securely hold the concrete specimen in the center of the testing device and apply concentric forces to it, reducing any potential twisting between the concrete and steel reinforcement , the rebar does not satisfy the inhibitory criterion and results in changes in the reinforcement displacement in concrete and eventually creates mechanical cracks and structural destruction. In corrosive environments, due to the corrosion process in the interface and reduction of bonds between rebar and concrete, evaluation and analysis of reinforcement in the concrete according to the mentioned criterion are of great importance. Therefore, the pullout test was conducted on rebar encapsulated in concrete for calculating the existing shear stress, and the deviation from the required value was evaluated. By anticipating the corrosion rate by increasing the effective length of reinforcement or the number of reinforcements used per unit of concrete level , it coulD is the diameter of rebar, l is the length of rebar in concrete, and τ is the shear stress between rebar and concrete. In the present study, D = 10 mm, and τ = 9.25 MPa. To calculate the shear force  was calnsidered .For selected specimens, the amount of shear force, coefficient of application for epoxy coatings, and normalized data compared to the control specimen (non-coated steel reinforcement in the first mix design without corrosion after 14 days of curing) are summarized in According to Considering the abovementioned results, the use of Ferrogard 901 inhibitor would not be approved by the structural designer. In the fifth mix design, with the adoption of polypropylene fibers, the non-coated reinforcement had the lowest adhesion to concrete among all samples due to the very severe corrosion of the reinforced concrete. Interestingly, no debonding was observed in the mild steel rebars coated with zinc-rich epoxy primer in concrete with polypropylene fibers. This shows the complete protection of the coating and proper adhesion to the porous mix design. In practice, the bond strength for ribbed steel and galvanized steel is necessarily the same because the bond strength is mainly due to the mechanical locking between the ribbed bumps and concrete .Stereograph microscope images of the five mix designs are compared in Corrosion occurred in the steel-reinforced concrete with a corrosion rate of 56 mpy in accelerated corrosion conditions simulated with 32 V anodic potential. Thus, the first mix design, without the use of concrete additives and with a water/cement ratio of 0.40 and type 2 Portland cement, and the use of non-coated steel reinforcement in environments containing chloride ion is not recommended.The adoption of mild steel reinforcement with hot-dip galvanized coating, AISI 316 rebar, and AISI 304 rebar, due to the existing protective layer, increased the corrosion resistance of reinforced concrete in the first mix design by 2.57, 4.96, and 1.96 times compared to the non-coated mild steel reinforcement, respectively.The inclusion of Ferrogard 901 inhibitor (second mix design) increased the polarization resistance, decreased the corrosion rate by 1.9 times, and significantly increased the structural durability (cracking time) by up to 11.4 times compared to the first mix design.Simultaneous addition of 25% fly ash and 10% silica fume in the third mix design reduced the porosity and increased the corrosion resistance of all protective systems, which substantially decreased the corrosion rate by 1.47 times in the non-coated reinforcement in comparison with the first mix design.The use of the inhibitor delayed the concrete cracking time by up to 1.6 times compared to the adoption of pozzolanic materials.Simultaneous use of 10% silica fume, 25% fly ash, and 3% Ferrogard 901 inhibitor (fourth mix design with type 2 Portland cement and water/cement ratio of 0.40) led to an increase in the cracking time of 11.9 times and a decrease of corrosion rate of non-coated mild steel reinforcement of 2.4 times compared to the first mix design.The adoption of 2.5% polypropylene fibers (vol%) reduced the corrosion rate of steel reinforcement by 2.6 times.After 500 h anodic potential application, polyurethane top coating and zinc-rich epoxy primer with respective values of 0.87 and 0.60 compared to the control sample provided the highest adhesion to the concrete surface.The simultaneous inclusion of silica fume, fly ash, and Ferrogard 901 inhibitor in concrete mix design is recommended for decreasing the porosity of concrete and significantly increasing the corrosion resistance of rebars.This research provides a comprehensive source of information on how to increase the corrosion resistance of rebars by evaluating different concrete mixes, admixtures, rebars, coating systems, and primers. The simultaneous or sole effect of these systems as well as the comparison between their performance by conducting a systematic study provide unique results regarding the selection of the most suitable admixture, rebar, coating system, and primer for application in corrosive environments. The following main conclusions can be derived from the results of this study:


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.In the human gut, the growth of the pathogen  This study examines how interactions between constituent members of the human gut microbiome influence the pathogen C. difficile’s response to clinically relevant antibiotics, providing key insights into ecological principles and molecular mechanisms that influence antibiotic susceptibility in this health-relevant system. 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 [C. difficile infection after treatment with antibiotics that decimate the microbiota [C. difficile infections [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 [C. difficile germination by Clostridium scindens via production of secondary bile acids [C. difficile growth by Bacteroides thetaiotaomicron via succinate cross-feeding [C. difficile growth, how the microbiota impacts C. difficile antibiotic susceptibility is largely unknown.The bacterial pathogen sistance . The keycrobiota  and by tfections . Previounfection –6. Multi-feeding . While mC. 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 [Similar to other pathogens, fections . If micrPrevious studies have shown that interspecies interactions can alter a given microbe’s response to antibiotics by increasing or decreasing susceptibility compared to monoculture . One exaEscherichia coli pathogen introduced into a porcine microbiome [C. difficile’s antibiotic susceptibility.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 . Studiescrobiome ,17. BaseC. 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 [We used a diverse human gut community  to studybacteria . Metronibacteria . The probacteria –22. Metrbacteria ,24.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.Using a bottom-up approach, we perform a detailed and quantitative characterization of how human gut microbes impact C. difficile infections occur in the context of complex resident gut communities. However, antibiotic treatments to eliminate C. difficile infection are designed based on the susceptibility of C. difficile in monoculture, which neglect the role of interspecies interactions in shaping antibiotic susceptibility. Understanding how the gut microbiota alters C. difficile antibiotic susceptibility could inform the treatment of C. difficile by antibiotics. To investigate this question, we evaluated C. difficile’s response to antibiotics in the presence of synthetic communities of gut microbes . The 13-member human gut community was designed to span the phylogenetic diversity of the human gut microbiome. The interactions between these gut microbes and C. difficile have been deciphered in the absence of antibiotics [Fig 1B). We selected the antibiotics metronidazole and vancomycin for their clinical relevance in the treatment of C. difficile infections and for the differences in their activity spectra. Metronidazole has broad spectrum activity against the 13 gut microbes, whereas the activity of vancomycin is specific for gram-positive species. To determine how C. difficile would respond to both low and high antibiotic concentrations in the human gut, we characterized C. difficile’s MIC and its growth in the presence of subinhibitory concentrations across a range of ecological contexts .ibiotics  C. difficile in communities, we used a broth dilution method based on the clinical method detailed by the Clinical and Laboratory Standards Institute [Fig 1D). The MIC for each species in the community was determined as the lowest antibiotic concentration where growth at 48 h was below a threshold  and in each pairwise community containing C. difficile . Consistent with the mechanism of the antibiotics, all species were susceptible to metronidazole in the range of antibiotics tested, whereas gram-negative species  were not susceptible to vancomycin .Using this method, we determined the MIC of each species in monoculture  in 5 pairwise communities compared to monospecies . However, C. difficile had a large change in MIC (≥4-fold) in coculture with D. piger  . In the remaining 4 pairwise communities, C. difficile’s growth was strongly inhibited in all concentrations and no MIC could be calculated . In sum, the MIC of C. difficile was infrequently impacted by pairwise interspecies interactions in the human gut community.In most pairwise communities, the metronidazole and vancomycin MICs of C. difficile abundance using both OD600 multiplied by 16S rRNA gene sequencing relative abundance and count of colony-forming units (CFUs) on C. difficile selective agar plates to validate the OD600-based absolute abundance method . Our results showed that CFU counting and the OD600-based absolute abundance method yielded the same MIC value in 11 out of 12 conditions . In 1 condition, C. difficile’s growth was strongly inhibited below the MIC threshold for all concentrations. Therefore, an MIC could not be determined using the OD600-based method, although an MIC could be revealed using the CFU method. Overall, these trends demonstrate that the OD600-based absolute abundance method is a reliable way to measure C. difficile MIC, although the 2 methods can vary in sensitivity.For select pairwise communities, we quantified C. difficile’s response to subinhibitory antibiotic concentrations (concentrations lower than C. difficile’s MIC or subMICs).Pathogens may encounter antibiotic concentrations lower than the MIC during antibiotic treatments. If the pathogen has gained resistance to the antibiotic, the entire dose regimen could be subinhibitory. Alternatively, even if the target dose is greater than the MIC, sub-minimum inhibitory concentrations (subMICs) can occur at the beginning and end of dosing regimens and between daily dosages . TherefoC. difficile’s abundance was similar or reduced in the subMIC range than in the absence of antibiotic . However, in a subset of pairwise communities, C. difficile’s abundance in the subMIC range was enhanced compared to the absence of antibiotic . For example, C. difficile monospecies abundance was similar in the presence of 0.75 μg/mL metronidazole and the absence of antibiotic. However, in the presence of B. thetaiotaomicron, the abundance of C. difficile was significantly higher in the presence of 0.75 μg/mL metronidazole than in the absence of antibiotic . To provide further insights, we computed the subMIC fold change at each subMIC, defined as the species absolute abundance at the given subMIC divided by the species absolute abundance in the absence of antibiotic . Using this metric, C. difficile’s growth was enhanced for at least 1 subMIC in 1 pairwise community in the presence of vancomycin and 11 pairwise communities in the presence of metronidazole . In 7 pairwise communities, the growth enhancement of C. difficile in the presence of metronidazole was significantly greater than the enhancement observed for the C. difficile monoculture in the presence of metronidazole . In sum, our results illustrate that approximately half of the species altered C. difficile’s response to low concentrations of metronidazole.In many pairwise communities, C. difficile’s growth was moderately enhanced in the presence of a single subMIC . Time-series OD600 measurements of C. difficile monoculture revealed that this moderate growth enhancement was due to a difference in growth phase in the presence and absence of the antibiotic at the measured time point (48 h) . By contrast, C. difficile’s growth was enhanced in 2 pairwise communities in the presence of subMICs compared to no treatment over multiple time points that was not due to differences in growth phase . Thus, while differences in monoculture growth phases yielded a modest enhancement of C. difficile in response to a single subMIC, a different mechanism determined the observed larger and sustained growth enhancements in pairwise communities.In monoculture, C. difficile, which would in turn enhanced C. difficile growth, representing competitive release [C. difficile. By contrast, if a biotic inhibitor displayed a higher resistance to the antibiotic than C. difficile, the degree of biotic inhibition would remain unchanged at subMICs, and thus, C. difficile’s growth would not change substantially compared to its growth in absence of the antibiotic . Supporting this hypothesis, the maximum subMIC fold change of C. difficile displayed a strong positive correlation with the degree of biotic inhibition in the presence of the coculture partner . In addition, the correlation was enhanced in pairwise communities containing gut microbes that were sensitive to the antibiotic . In the presence of both individual antibiotics, the maximum subMIC fold change of C. difficile was significantly higher when paired with biotic inhibitors with higher sensitivity to the antibiotic than C. difficile . In sum, antibiotic induced inhibition of susceptible biotic inhibitors enhanced C. difficile’s growth in response to subMICs. This suggests that C. difficile’s growth in the gut could be enhanced in the subMIC regime if patients harbor antibiotic-sensitive biotic inhibitors.We hypothesized the action of the antibiotic in certain pairwise communities relieved the biotic inhibition of  release ,29. ThisC. difficile subMIC growth enhancements measured using the OD600-based absolute abundance method were due to increases in C. difficile abundance as opposed to other factors such as changes in cell morphology. To this end, we determined the subMIC fold changes of C. difficile in select pairwise communities that were characterized using CFU counting . Our results showed that the CFU method and OD600-based absolute abundance method identified a significant subMIC fold change in the C. difficile-B. thetaiotaomicron and C. difficile-D. piger pairs in presence of metronidazole . The methods also agreed in detecting no significant subMIC fold changes in 10 other pairwise communities in metronidazole or vancomycin . However, the CFU method did not detect significant subMIC fold changes in 3 pairwise communities for which the OD600-based method detected significant fold changes . These discrepancies may be due to differences in sensitivities of the 2 methods, as the spot dilution CFU method . Our results demonstrated a significant C. difficile subMIC fold change at multiple concentrations in the pairwise community, while no significant fold changes were observed in the C. difficile monoculture . This demonstrates that the observed C. difficile growth enhancement using the OD600-based method  was consistent with CFU counting. Overall, our results demonstrate that C. difficile subMIC growth enhancements can also be detected by the CFU-based absolute abundance method.To determine if subMIC fold changes could be resolved using the lower sensitive CFU method, we increased the resolution of subinhibitory concentrations tested. The finer resolution may capture subMICs with larger fold changes that are large enough to be detectable by the CFU method. Additionally, the finer resolution may result in multiple subMICs with significant fold changes, increasing confidence in the results. We used the CFU method with a finer resolution of antibiotic concentrations to measure growth enhancement in the C. difficile guided by a previously developed dynamic computational model of our system [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  with no consistent core members to metronidazole.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 r system   or no change (9 communities) (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 ( indicated as “NG”). The remaining 8 communities displayed either a moderate increase in MIC  or no change (6 communities) . 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.In 6 of 29 communities characterized in the presence of metronidazole, 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 . 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 . In addition, the correlation was stronger for communities composed of only antibiotic-sensitive biotic inhibitors . 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 . 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 .We tested if the multispecies communities containing antibiotic-sensitive biotic inhibitors displayed an enhancement in 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 . 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 . 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.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 . 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 (Bi) .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  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 [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) , S1 Fig.s 2- to 1-member iC. difficile in the subMIC range . C. difficile’s response to each antibiotic concentration was classified into 3 categories . The expanded gLV model correctly predicts C. difficile’s qualitative response to 71% of antibiotic conditions . 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 . 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 , 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 evaluated whether this model could qualitatively predict the trends in growth 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 . By contrast, growth enhancements are not observed in simulated communities that lack an antibiotic-sensitive biotic inhibitor of the focal species . This trend is also present in larger communities . The focal species’ growth enhancement at subMICs increases with biotic inhibition and antibiotic susceptibility of the inhibitor species . 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.We explored model simulations to determine if the enhancement of D. piger, we observed a substantial increase in C. difficile MIC, in addition to a moderate growth enhancement of C. difficile at subMICs . 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 . 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 . While competitive interactions  contributed to the observed subMIC growth response, the mechanism of increased metronidazole MIC in the presence of D. piger was unknown.Changes in antibiotic susceptibility of a pathogen due to significant microbial interactions could reduce the efficacy of antibiotic treatments. In the presence of 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) . 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 . Further, multiple C. difficile clinical isolates had an increased MIC in D. piger spent media . Finally, C. difficile exhibited a higher metronidazole MIC in coculture with D. piger in a different chemically defined growth medium . 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.We investigated the robustness of this trend across different environmental conditions and for different D. piger could alter C. difficile’s metronidazole MIC via a direct chemical interaction with metronidazole which reduces its efficacy  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 .D. piger on C. difficile’s intracellular network activities which in turn could alter C. difficile’s response to metronidazole. We first considered resource competition as a possible indirect effect because resource competition is a common mechanism driving interspecies interactions in microbial communities. C. difficile’s abundance was not substantially lower in D. piger’s spent media than monoculture , suggesting that resource competition was not a major driver of the increased MIC. In addition, supplementing concentrated fresh media into the D. piger spent media, which would alleviate any resource competition, did not alter C. difficile’s metronidazole MIC . Therefore, these data suggest that D. piger did not affect the metronidazole MIC of C. difficile via resource competition.We considered indirect effects of D. piger affected the metronidazole MIC of C. difficile by altering patterns in gene expression. We performed genome-wide transcriptomic profiling of C. difficile in late exponential phase in the following environments: (1) monoculture in fresh media; (2) monoculture in D. piger spent media; and (3) coculture with D. piger. Each of these cultures was exposed to a subMIC of metronidazole that reduced growth by approximately 50% or no treatment was applied . The antibiotic concentration was chosen to allow sufficient growth for transcriptional profiling.We considered if C. difficile’s gene expression profile in D. piger conditions  was significantly altered compared to C. difficile monoculture . Of these genes, 72 in the D. piger coculture and 47 in the D. piger spent media exhibited >8-fold change, demonstrating that the presence of D. piger had a large impact on C. difficile’s gene expression profile. In the presence of metronidazole, a smaller percentage of genes were differentially expressed between the D. piger and fresh media conditions . Large shifts in C. difficile gene expression occurred due to the addition of metronidazole, with 25% to 49% of genes differentially expressed . The 3 metronidazole conditions clustered closely together, indicating that the antibiotic induced similar changes in gene expression regardless of the media condition or ecological context .D. piger conditions. Many of the genes up-regulated in D. piger and D. piger antibiotic conditions compared to fresh media had annotated roles in metal acquisition . To acquire iron, C. difficile uses iron permeases to import free ferrous iron into the cell [feoAB1, feoAB3) in D. piger and D. piger metronidazole conditions . Alternative transporters were also up-regulated, such as uptake genes for ferrichrome siderophores  and catecholate siderophores  as well as a zinc transporter gene (zupT). These genes  are regulated by the iron regulator fur [D. piger conditions was responsible for the observed changes. Consistent with the metal limitation signature, ferrous transporters that were not up-regulated (feoAB2) have been documented as unresponsive to iron limitation [To provide deeper insights into specific genes that varied across conditions, we analyzed the genes with the largest expression changes in the cell ,33. We oator fur  and haveator fur –36, suggmitation ,34.C. difficile were linked to iron metabolism. Flavodoxin (fldX) was the most up-regulated gene in the D. piger conditions . Flavodoxin can replace iron-requiring electron transfer proteins (such as ferredoxin) in iron-limiting conditions [ribACH) were up-regulated in D. piger conditions (Table 1). Previous studies have shown that flavodoxin was regulated by fur and de-repressed in iron-limiting and zinc-limiting conditions [C. difficile’s down-regulated genes (62 of 678 genes) in the D. piger coculture condition were predicted to contain iron-sulfur clusters . In sum, genes for the metal-independent flavodoxin were up-regulated, whereas transcripts for iron-requiring proteins were down-regulated, consistent with a metal-limited environment for C. difficile created by D. piger.In addition to genes directly related to metal transport, many of the DEGs in nditions . Flavodonditions ,34–36, anditions . Iron-conditions . Using aD. piger conditions, we performed a gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) modules . Many of the identified pathways could be connected to metals. For example, many of the enzymes in the down-regulated Wood–Ljungdahl pathway have iron-sulfur clusters, namely carbon monoxide dehydrogenase  and formate dehydrogenase H (fdhF) [hydAN1N2) in the Wood–Ljungdahl pathway contains a nickel-iron cluster. These enzymes have been shown to be down-regulated under iron-limited conditions [Fig 5F) [D. piger also affected the expression of C. difficile toxins  that were down-regulated between 4- and 27-fold in the D. piger conditions (S3 Table).To identify other biological pathways that were significantly altered in H (fdhF) –40. In anditions . Pathwaynditions . Similar[Fig 5F) . D. pigeC. difficile, we evaluated the relationship between gene expression changes in C. difficile in the presence of D. piger and gene expression changes in C. difficile in iron-limited media previously characterized in a separate study [C. difficile in the D. piger coculture and C. difficile in monoculture in the absence of metronidazole with the log2 fold changes observed between C. difficile in iron-limited and iron-rich media in the previous study. These fold changes displayed an informative relationship and were qualitatively consistent for 89% of genes between the 2 studies . Similarly, the fold changes of C. difficile cultured in D. piger spent media showed 87% qualitative agreement with the fold changes in the C. difficile iron-limitation study . The informative relationship between these datasets suggests that the shifts in gene expression for the majority of C. difficile’s genes in the D. piger conditions can be explained by metal limitation.To provide further insights into the contribution of iron limitation on the patterns of gene expression in te study . For allD. piger created a metal-limited environment for C. difficile, as metal acquisition and alternative genes involved in flavin metabolism were up-regulated, whereas transcripts for metal-requiring proteins were down-regulated . These trends were observed in both D. piger coculture and D. piger spent media and were also consistent with the trends in the D. piger conditions with metronidazole .Overall, these data suggest that 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.Since the majority of 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 [D. piger conditions, enzymes hypothesized to reduce metronidazole were also down-regulated, namely ferredoxin genes , pyruvate-ferredoxin/flavodoxin oxidoreductase (PFOR) (nifJ), and hydrogenases   [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 [In 2 studies of metronidazole-resistant avodoxin . Similaravodoxin . In each . Down-reofactors , suggestC. difficile. For example, Bacteroides fragilis metronidazole-resistant mutants displayed reduced PFOR expression [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 .This mechanism of resistance has been proposed in other species beyond pression . TherefocbiN, a putative cobalt transporter that was down-regulated in the D. piger conditions  and in iron-limited media [cbiN distinguished a metronidazole-resistant R010 isolate of C. difficile from a metronidazole sensitive R010 isolate isolated from the same patient [cbiN was down-regulated by 10-fold in the D. piger coculture and 4-fold in D. piger spent media.We also identified that ed media  has been patient . In our 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 . Altronate hydrolase catalyzes the dehydration of the six-carbon altronate as part of galacturonate degradation [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 [E. coli have demonstrated that this enzyme requires iron or manganese for its catalytic activity [Another enzyme that was down-regulated in the radation . One of nctional . Studiesactivity , consistactivity .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.The mutations 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 [D. piger cultures, we observed a characteristic black precipitate (ferrous sulfide) that forms when iron combines with produced hydrogen sulfide [D. piger.The global changes in D. piger . In D. p sulfide . Other d sulfide ,50 and mD. piger produced sulfide, we quantified the amount of sulfide in a monoculture of D. piger over time . 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 . 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 . 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 [To estimate how much metal is precipitated by ver time . The amoifficile .C. difficile’s metronidazole MIC, we tested whether removing hydrogen sulfide from D. piger spent media eliminates this protective effect. The majority of hydrogen sulfide was eliminated from D. piger spent media by purging with nitrogen gas for 15 min . We visually observed that nitrogen-purged spent media formed less ferrous sulfide precipitate than the untreated spent media . Consistent with the proposed mechanism, the metronidazole MIC of C. difficile was reduced in the nitrogen-purged spent media compared to the untreated spent media . This suggests that hydrogen sulfide contributed to the increase in C. difficile metronidazole MIC via metal precipitation.Based on the hypothesis that metal precipitation by hydrogen sulfide leads to an increase in D. piger spent media with metals that have precipitated would reduce the protective effect on C. difficile’s metronidazole MIC. We characterized C. difficile’s metronidazole MIC in media supplemented with cobalt, iron, manganese, nickel, and zinc since multiple divalent metals can form sulfide precipitates [C. difficile [D. piger spent media to account for unreacted hydrogen sulfide that could precipitate the supplemented metals. Our results showed that C. difficile’s metronidazole MIC was substantially reduced in D. piger spent media supplemented with the five-metal combination compared to D. piger spent media without this addition . By contrast, C. difficile’s antibiotic MIC was not altered in fresh media supplemented with the five-metal combination, demonstrating that this effect was dependent on environmental modification by D. piger . Supplementation of D. piger spent media with individual metals revealed that none of the individual metals decreased metronidazole MIC as substantially as the combination of metals .Additionally, we hypothesized that supplementing ipitates ,50 and lifficile ,36. We ifldX, feoB1, and zupT) was significantly reduced in C. difficile cultured in D. piger spent media supplemented with the metal combination compared to the spent media without metal supplementation . Overall, this data suggests that metal limitation in D. piger spent media caused a global shift in C. difficile’s transcriptome and substantially reduced metronidazole susceptibility. Notably, both of these effects were eliminated by metal supplementation. Combining our data together, we propose a biological mechanism for the effect of D. piger on C. difficile’s metronidazole susceptibility . In this proposed mechanism, hydrogen sulfide produced by D. piger sequesters divalent metals in the media. This in turn creates a metal limited environment for C. difficile, which leads to down-regulation of enzymes requiring metal cofactors for their activities. The down-regulated genes include enzymes that perform redox reactions that reduce metronidazole to its active form. Therefore, in coculture with D. piger, the activated concentration of metronidazole is reduced, yielding a substantially higher antibiotic MIC as a consequence of a reduction in DNA damage.Consistent with these results, the expression of 3 genes that indicate metal limitation . By contrast, enhancements in the abundance of C. difficile at subMICs were more frequently observed, occurring in 52% of characterized communities . Our work demonstrates that pathogen growth can be altered by interspecies interactions across a wide range of antibiotic concentrations, which should be considered in the design of antibiotic treatments.A fundamental question is uncovering the role of biotic interactions in shaping antibiotic susceptibility in microbiomes. In particular, understanding how microbial interactions alter the antibiotic susceptibility of major human pathogens such as C. difficile’s MIC compared to monospecies in 4% of pairwise communities  and 16% of multispecies communities in response to single antibiotics . This is qualitatively consistent with the small fraction of interspecies interactions shown to modify susceptibility in other microbial communities [C. difficile, future work could apply our approach to study the impact of gut microbes on the response of C. difficile to antibiotics that are risk factors for initiating C. difficile infections such as clindamycin, cephalosporins, and fluoroquinolones [We observed a ≥4-fold increase in munities –15. Whilinolones .C. difficile metronidazole MIC in the presence of specific gut may contribute to the ineffectiveness of metronidazole in treating C. difficile infections in the human colon. Due to the low achieved metronidazole concentration in the human colon [C. difficile metronidazole MIC could allow the pathogen to survive. Based on our results, future testing of C. difficile antibiotic susceptibility could include conditions of C. difficile cultured with physiologically relevant microbial communities. For example, C. difficile could be cultured with a panel of resuspended fecal samples from multiple donors with disparate human gut microbiome compositions. Antibiotic treatments could be scored by balancing minimization of the disruption of healthy gut microbiota with minimization of the variability of C. difficile’s MIC across donor samples.The observed increase in an colon , even moD. piger depletes bioavailable divalent metals in the environment, resulting in transcriptional down-regulation of enzymes in C. difficile requiring metal cofactors. These enzymes are proposed to reduce metronidazole to its active form. This implies that a lower expression level of these enzymes protects C. difficile from the action of the antibiotic . Iron limitation has been shown to increase microbial resistance to antibiotics with proton motive force (PMF)-dependent uptake due to the decrease in iron-sulfur containing complexes crucial for generating the PMF [C. difficile metronidazole resistance [C. difficile to metronidazole [We propose a mechanism wherein  the PMF . Because the PMF , iron lisistance ,42. Intenidazole . This isC. difficile MIC, whereas the addition of individual metals, including iron alone, did not have this effect. We hypothesize that multiple metal cofactors are required for the enzymatic activities that contribute to metronidazole reduction. While some enzymes proposed to contribute to metronidazole reduction, such as PFOR, only require iron cofactors, others such as hydrogenase require both iron and nickel cofactors, supporting this hypothesis [Staphylococcus aureus [In our metal supplementation experiments, the cocktail of 5 metals caused a substantial decrease in pothesis ,21,39,57s aureus .D. piger and C. difficile. There are multiple commensal species in addition to D. piger that can produce hydrogen sulfide [C. difficile metronidazole MIC through a similar mechanism. Beyond C. difficile, metronidazole is a widely used antibiotic to treat anaerobic bacterial infections [S14 Fig).Our proposed mechanism of metronidazole protection has implications beyond  sulfide . These sfections , suggest−3 M range [−3 M based on fecal concentrations, with 30% estimated to be present in a bioavailable form [C. difficile infection, iron concentrations vary significantly as damage of epithelial cells can release heme into the environment [C. difficile metronidazole susceptibility in an infection model in the murine gut. If the mechanism persists in in vivo, reduction of dietary protein could be considered during metronidazole treatment, as lower dietary protein has been shown to reduce hydrogen sulfide production by gut microbiota [Estimates of sulfide concentration in the colon  are ble form ,63). Theironment , leadingironment  could sicrobiota ,66.C. difficile’s subMIC response in approximately half of the characterized communities . Our CFU experiments demonstrated that the growth enhancement in a subset of conditions could be explained by changes in C. difficile abundance . This does not exclude the possibility that some fraction of the observed C. difficile growth enhancements based on the OD600 method could be impacted by changes in cell morphology. Nevertheless, the relationship between the degree of biotic inhibition by susceptible competitors and focal strain growth enhancements at subinhibitory antibiotic concentrations is consistent in our computational model. This model does not capture changes in cell morphology, yet is able to recapitulate the overall trends in C. difficile growth enhancements .We observed alterations in C. difficile at subMICs is an example of the ecological principle of competitive release, where resistant species expand after removal of antibiotic-sensitive competitors [C. difficile. We demonstrated that the magnitude of the focal strain’s growth enhancement depends on the degree of biotic inhibition . In addition, we showed that resistant inhibitors can suppress the growth enhancement caused by antibiotic sensitive biotic inhibitors . Additionally, our computational model identified general ecological principles driving subMIC growth enhancements, which can be applied to other antibiotic treated communities.Our observed growth enhancement of petitors . Competipetitors  and a 2-petitors . Here, wC. difficile may encounter subinhibitory concentrations during metronidazole treatment [C. difficile or other pathogens at subinhibitory concentrations, antibiotic treatments could be combined with bacterial therapeutics consisting of cocktails of resistant biotic inhibitors to suppress pathogen growth enhancement. This benefit of resistant commensal species has been suggested previously [Bacteroides thetaiotaomicron and Prevotella copri are good candidates for such a cocktail as they are resistant biotic inhibitors that inhibit C. difficile in communities across all antibiotic concentrations . Use of narrow spectrum antibiotics or selective bacteriophages [Our findings suggest that growth enhancement of pathogens could occur in high richness communities such as the human gut microbiome in the presence of subinhibitory concentrations of antibiotics in the absence of resistant inhibitors. Metronidazole and vancomycin concentrations in the colon are estimated based on fecal concentrations. Therefore, it is unknown whether subinhibitory concentrations of these antibiotics occur in the colon. Based on fecal concentrations, it has been suggested that reatment . To avoieviously . Our datiophages  that speC. difficile growth in the presence of antibiotics. These results suggest that the discovery of interactions that sensitize C. difficile to antibiotics may be challenging. Therefore, future ecologically informed therapeutic strategies could exploit the strong biotic competition of C. difficile by diverse human gut species [C. difficile to antibiotics.Overall, we found 2 classes of interactions that yield enhanced  species  that are2, 15 ± 1% CO2 and balance N2 (Coy Lab products). Starter cultures were prepared by inoculating 100 μL of single use glycerol into 5 mL of ABB. D. piger starter cultures were supplemented with 28 mM sodium lactate (Sigma-Aldrich) and 2.7 mM magnesium sulfate (Sigma-Aldrich). E. rectale starter cultures were supplemented with 33 mM sodium acetate (Sigma-Aldrich). To ensure organisms were at similar growth stages at experimental set up, starter cultures were inoculated either 16 or 41 h prior, depending on the organism’s growth rate  and incubated at 37°C with no shaking.Metronidazole (Sigma-Aldrich M1547) and vancomycin (Sigma-Aldrich V1130) stocks of 1 mg/mL were made in water, filter sterilized with 0.2 μM filters, and stored at −20°C as single use aliquots. Stocks were diluted in 2-fold dilution series at 10× the desired concentration. Cultures were inoculated into ABB in 96 deep well plates. Monospecies were inoculated with a starting OD600 of 0.0022. Pairs were inoculated with a starting OD600 of 0.00022 for g for 15 min after which the supernatant was removed and the pellets were stored at −80°C. OD600 was measured for 2 dilutions of each sample and the dilution in the linear range of the instrument (Tecan Infinite Pro 200) was selected, and 16S rRNA sequencing was performed to determine relative abundances as described below. Species absolute abundance was calculated by multiplying the relative abundance of each species by the community OD600. The MIC was defined as the lowest concentration for which all concentrations greater to and equal restrict organism growth to significantly less (p < 0.05) than 0.05 OD600 or 5,000 CFU/mL as determined by a one-sample t test.After 48 h, cultures were mixed by pipetting and 200 μL aliquots were removed for sequencing and measuring OD600. Sequencing aliquots were spun down at 1,739 DNA extraction, library preparation, and sequencing were performed as described previously . BrieflySequencing data was analyzed as described previously . BrieflyC. difficile selective plates were created by autoclaving C. difficile agar (Oxoid CM0601) and adding horse blood , norfloxacin , moxalactam , and erythromycin  after media cooled to 55°C. After 48 h of incubation, cultures were mixed and diluted into PBS. Four dilutions of each sample were spotted on C. difficile selective agar plates, with 2 technical replicates per sample. Plates were incubated at 37°C for 48 h at which point colonies were counted in the dilution spot containing between 5 and 50 colonies. The CFU/mL for each sample was calculated as the average of the 2 technical replicates times the dilution factor. The lower limit of detection for the assay was 5,000 CFU/mL.N-coupled first-order ordinary differential equations (Eq 1):N is the number of species, Xi is the abundance of species i, ri is the basal growth rate of species i, αij is the growth modification of species i by species j, and Xj is the abundance of species j. The parameter αij is constrained to be negative when i  =  j, representing intraspecies competition. In the antibiotic term, Bi is the sensitivity of species i to antibiotic, and a is the concentration of the antibiotic. We modify the model to make the antibiotic concentration modified constant over time.The antibiotic perturbation extension of the generalized Lotka–Volterra model  is a setri and interaction parameters aij come from our previous work [Bi were inferred in this study from time-series measurements of monospecies antibiotic titrations using a custom python script. The equation used to infer Bi from monospecies data was Eq 1, with αij = 0 for all j! = i. The minimize function of the Scipy optimize package was used to determine the optimal Bi for each species xi, that resulted in the lowest cost between the antibiotic gLV model and the time-series measurements across all the antibiotic concentrations in the titration. The antibiotic concentrations were scaled so that the concentrations range between 0 and 1. The cost in the optimization was computed by simulating the species abundance in each antibiotic condition with an ODE solver and summing the mean-squared error between the abundance of the species in the simulation and the abundance of the species in the data at each time point in each antibiotic concentration.Values for growth rates parameters ous work . Values Bi = 0 for all species.Null model 1 (“gLV”) is the generalized Lotka–Volterra model. This is the same as the generalized Lotka–Volterra model with antibiotic perturbation (Eq 1) without the antibiotic term. This can also be thought of as the generalized Lotka–Volterra with antibiotic perturbation with all species antibiotic susceptibility set to zero (not susceptible). Null model 1 is Eq 1 with Bi for species i is equal to the unshuffled B of any one of the 14 species). The accuracy of Null model 2 was averaged across 1,000 sets of shuffled parameters. The distance between the shuffled B for C. difficile and the unshuffled B for C. difficile was calculated as the absolute abundance of the difference between shuffled BCD and unshuffled BCD.Null model 2 (“gLV + shuffled antib. suscept.”) is the generalized Lotka–Volterra model with antibiotic perturbation (Eq 1) with a shuffled set of antibiotic susceptibility parameters. For metronidazole conditions, the 14 metronidazole susceptibility parameters were shuffled and for the vancomycin conditions, the 14 vancomycin susceptibility parameters were shuffled  is the generalized Lotka–Volterra model with antibiotic perturbation (Eq 1) with no interspecies interactions. Null model 3 is Eq 1 with D. piger starter culture was incubated for 48 h. The culture was then diluted into ABB to an OD600 of 0.0022. The cultures were incubated at 37°C with no shaking. Unless otherwise indicated, cultures were incubated for 12 h. After incubation, cultures were spun down at 1,739 g for 15 min. Supernatant was removed and filter sterilized with Steriflip 0.2 μM filters.D. piger spent media was prepared following the procedure described above. An aliquot of metronidazole was incubated in D. piger spent media or rich media (ABB) at 37°C. After 12 h of incubation, the incubated metronidazole was diluted into a C. difficile culture in fresh media (ABB) following the antibiotic titration method described above.C. difficile R20291 reference genome (GenBank assembly accession GCF_000027105.1) was run on the Metal Predator web-server [The protein FASTA sequence from the b-server .The amount of metal in fresh media  was analyzed via Inductively Coupled Plasma Mass Spectroscopy by the Wisconsin State Lab of Hygiene.In metal supplementation experiments, supernatants were supplemented with iron (II) sulfate (Alfa Aesar), manganese (II) sulfate monohydrate (Sigma-Aldrich), nickel sulfate (II) hexahydrate (Sigma-Aldrich), zinc (II) sulfate heptahydrate (Alfa Aesar), and/or cobalt (II) chloride hexahydrate (Sigma-Aldrich). Metal compounds were prepared as 100× stocks in water and filter sterilized with 0.2 μM filters.g for 10 min and the supernatant was transferred to a new plate to measure OD670.Hydrogen sulfide was quantified using the Cline Assay. Immediately before performing the assay, sodium sulfide (Alfa Aesar) was added to nitrogen purged water into a sealed vial. The stock was then diluted into 1% zinc acetate to 1 mM and further diluted into 1% zinc acetate to desired concentrations for a standard curve. Samples were removed from the anaerobic chamber and immediately diluted 5-fold into 1% zinc acetate. Cline reagent was prepared in advance , 2.4 g iron (III) chloride , 50 mL concentrated hydrochloric acid, 50 mL water, stored in the dark) of which 3 μL was added to the standards and samples. Samples were mixed by pipetting and incubated for 20 min in the dark before measuring absorbance at 670 nm. If the samples contained cells, after incubation the samples were spun down at 1,739 D. piger spent media was prepared following the procedure described above. C. difficile and D. piger starter cultures were incubated for 48 h. C. difficile monoculture and CD-DP coculture conditions were inoculated from starter cultures into 96 deep well plates. For monoculture conditions, C. difficile was inoculated to an OD600 of 0.0022. For CD-DP coculture, C. difficile was inoculated to an OD600 of 0.0022 and D. piger was inoculated to an OD600 of 0.0198. Immediately after inoculation, antibiotics were added as a 1:10 dilution of 10× antibiotic stocks. The plates were covered with gas-permeable seals (BreathEasy) and were incubated at 37°C with no shaking. After 14 h, 800 μL of RNAprotect (Qiagen) was added to 400 μL of culture after which the cultures were mixed by pipetting and then incubated for 5 min at room temperature. Cultures were then centrifuged at room temperature for 10 min at 1,739 g and supernatant was carefully removed. Pellets were stored at −80°C.RNA was extracted using acidic phenol bead-beating method. Pellets were resuspended in 500 μL 2× Buffer B  and transferred to 2 mL microcentrifuge tubes containing 500 μL Phenol:Chloroform:IAA  and 210 μL 20% sodium dodecyl sulfate and were bead-beated with acid washed beads (Sigma G1277) for 3 min. All solutions were RNAse-free. Samples were centrifuged at 4°C for 5 min at 8,000 rpm, and 600 μL of the upper aqueous phase was added to 60 μL 3 M sodium acetate and 660 μL cold isopropanol and chilled on ice for 5 min before freezing for 5 min at −80°C. Samples were centrifuged at 4°C for 15 min at 18,200 g, the supernatant was decanted, and the pellet was washed with cold 100% ethanol. The pellets were dried in a biosafety cabinet for 10 min and then resuspended in 100 μL RNAse-free water. Samples were purified using RNeasy Mini Kit (Qiagen) and genomic DNA was removed using RNAse-Free DNase Set (Qiagen). Two replicates of each condition were sent to GENEWIZ  for sequencing. GENEWIZ depleted rRNA with Ribozero rRNA Removal Kit (Illumina) before cDNA library preparation using NEBNext Ultra RNA Library Prep (NEB). GENEWIZ sequenced the libraries on Illumina HiSeq. Data was de-multiplexed using Illumina’s bcl2fastq 2.17 software, where one mismatch was allowed for index sequence identification.C. difficile: GenBank assembly accession GCF_000027105.1. D. piger: Genbank assembly accession GCA_000156375.1). FeatureCounts was used to map reads to features on the genome. Reads per kilobase million (RPKM) values were computed using a custom python script. The DESeq2 Bioconductor library v4.0.3 was used in R v4.0.4 to quantify differential gene expression using a negative binomial generalized linear models with apeglm shrinkage estimator. When calculating RPKM of C. difficile genes in the C. difficile-D. piger coculture, the “reads mapped” in the denominator was the number of reads mapped to the C. difficile genome. Similarly, when quantifying differential gene expression for C. difficile genes in the C. difficile-D. piger coculture, only reads mapped to the C. difficile genome were provided to DeSeq2.The data was quality checked using FastQC. The BBDuk, BBSplit, and BBMap tools from BBTools suite were used to trim adapters, deplete rRNA, and map the remaining mRNA reads to the reference genomes (C. difficile cultures were prepared the same as the transcriptomic samples and were incubated for the same duration (14 h). RNA was extracted using the same method as the transcriptomic samples and was purified using RNeasy Mini Kit (Qiagen). Genomic DNA was removed using RNAse-Free DNase Set (Qiagen). We performed cDNA synthesis with 1 ug of total RNA using the iScript Select cDNA Synthesis Kit (Bio-rad). We performed qRT-PCR (Bio-rad CFX Connect) with SsoAdvanced Universal SYBR Green SuperMix (Bio-rad). We used previously published primers for the fldX, feoB1, zupT, and rrs genes [−ΔΔCt method [rrs is the reference gene [control. Each Ct value was the average of 3 technical replicates.rs genes ,73. We ct method , where:C. difficile R20291 (KEGG T number T00998) were used as gene sets and were supplied as a user defined annotation with the TERM2GENE field. The analysis was run with the log2FCs calculated by DeSeq2. The p-value cutoff used was 0.05 and the minimum gene set size used was 3.GSEA was performed using the GSEA method of the ClusterProfiler R package (v4.2.2) . KEGG moS1 Fig Line plots of monospecies OD600 across antibiotic concentrations for metronidazole (MTZ) and vancomycin (VAN) at 48 h. Each x-axis is semi-log scale. Colored data points indicate biological replicates. Colored lines indicate the average of n = 2 to n = 4 biological replicates. Bold number indicates MIC of data. Black lines indicate model fit. (C) Table of scaled antibiotic susceptibility parameter B for each species. The data underlying panels AB in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S2 Fig(A) Schematic of 2 methods for determining absolute abundance: calculated absorbance at 600 nm (OD600) or CFU counting.  Double axis line plots of absolute abundance at 48 h of C. difficile in the presence of (B) metronidazole (MTZ) and (C) vancomycin (VAN). Left axis (black) is calculated OD600 (OD600 multiplied by relative abundance from 16S sequencing) and right axis (red) is CFU per mL. The x-axis is semi-log scale. Black points indicate biological replicates. Red points indicate biological replicates, each the average of 2 technical replicates. Lines represent the mean of n = 4 biological replicates. Bold number indicates the MIC based on calculated OD600 (black) or CFU (red). “NG” indicates no growth above the MIC threshold for any conditions, and no MIC was able to be determined. Blue outlines indicate conditions for which the MIC was consistent between the 2 methods. Data collected on a separate day from C. difficile subMIC fold change in data from panels BC. Black indicates significant fold change (greater than 1) in at least 1 concentration, white indicates no significant fold change at any concentration. Significance was determined using unpaired t test between the abundance of C. difficile at a given subMIC and the abundance of C. difficile in the absence of antibiotic, P < 0.05. C. difficile abundance determined by CFU counting (left heatmap) or calculated OD600 method (right heatmap). The data underlying panels BCD in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S3 Fig Line plots of subMIC fold change for pairwise communities in metronidazole (MTZ) or vancomycin (VAN) at 48 h. SubMIC fold change is calculated as the species absolute abundance at the subMIC divided by the species absolute abundance in the absence of the antibiotic. Black horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. Each x-axis is semi-log scale. Data points indicate biological replicates. Line represents mean of n = 3 to n = 8 biological replicates in panel A and n = 1 to n = 8 biological replicates in panel B. (C) Line plots of C. difficile monospecies over time. (Top) C. difficile OD600 at 0 μg/mL and 1.5 μg/mL metronidazole. (Bottom) C. difficile subMIC fold change μg/mL metronidazole. Gray horizontal line at y = 1 indicates no change in growth compared to no antibiotic condition. SubMIC fold change calculated as in panels AB. Points represent biological replicates. Line represents average of n = 4 biological replicates. Statistical significance analyzed for all time points where C. difficile OD600 was greater in the presence of antibiotic than the absence of antibiotic . (D) Microscopy images of select C. difficile conditions from panel C: no antibiotic at 24 and 48 h and 1.5 μg/mL metronidazole at 48 h. Exponential phase C. difficile cells in presence of subMIC of metronidazole do not show morphological difference from exponential phase C. difficile cells grown in absence of antibiotic. (E) Line plots of C. difficile in pair with Bacteroides thetaiotaomicron over time. (Top) C. difficile OD600 in the presence of 0 μg/mL or 1.5 μg/mL metronidazole. (Bottom) C. difficile subMIC fold change in the presence of 1.5 μg/mL metronidazole. Gray horizontal line at y = 1 indicates no change in growth compared to no antibiotic condition. SubMIC fold change calculated as in panels AB. Points represent biological replicates. Line represents average of n = 4 biological replicates. Statistical significance analyzed for all time points where C. difficile OD600 was greater in the presence of antibiotic than the absence of antibiotic . (F) Line plots of C. difficile in pair with Clostridium hiranonis over time. (Top) C. difficile OD600 in the presence of 0 μg/mL or 0.1875 μg/mL vancomycin. (Bottom) C. difficile subMIC fold change in the presence of 0.1875 μg/mL vancomycin. Gray horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. SubMIC fold change calculated as in panels AB. Points represent biological replicates. Line represents average of n = 4 biological replicates. Statistical significance was analyzed for all time points where C. difficile OD600 was greater in the presence of antibiotic than the absence of antibiotic . (G) Box plot of maximum subMIC fold change for C. difficile in pairwise communities at 48 h. The maximum subMIC fold change is the maximum of the average subMIC fold change across all subMICs. The average subMIC fold change was calculated for each subMIC concentration by taking the average C. difficile absolute abundance at the subMIC divided by the average C. difficile absolute abundance in the no antibiotic condition . C. difficile did not grow at any concentrations of vancomycin in CD-BO, CD-BT, CD-BU, and CD-PC pairs so these communities were not included in the analysis. Each data point represents 1 pairwise community. Gray horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. Species are categorized as sensitive inhibitors if (1) the absolute abundance of C. difficile in a given pairwise community was significantly lower than the monoculture C. difficile abundance, in the absence of antibiotics, as determined by an unpaired t test; and (2) if the monospecies MIC was less than the monospecies MIC of C. difficile. Asterisks indicate significant difference  according to a one-sided Mann–Whitney U test. The data underlying panels ABCEFG in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S4 FigC. difficile abundance in presence of metronidazole in (black) monoculture or in (brown) coculture with B. vulgatus. Data points indicate biological replicates. Lines indicate the average of n = 4 biological replicates. Asterisks indicate significance between C. difficile CFU/mL at given concentration and C. difficile CFU/mL in the absence of antibiotic . The data underlying this figure can be found in DOI: 10.5281/zenodo.7626486.Line plot of (TIF)Click here for additional data file.S5 Fig Line plots of absolute abundance across A metronidazole (MTZ) and B vancomycin (VAN) concentrations at 48 h. Each x-axis is semi-log scale. Y-axis is calculated OD600 (OD600 multiplied by relative abundance based on 16S sequencing). Data points indicate biological replicates. Lines indicate the average of n = 1 to n = 4 biological replicates in panel A and n = 3 to n = 4 biological replicates in panel B. Color indicates species, see C. difficile MIC (see 10.5281/zenodo.7626486. MIC see . The dat(TIF)Click here for additional data file.S6 Fig Line plots of subMIC fold change in metronidazole (MTZ) or vancomycin (VAN) at 48 h. SubMIC fold change is calculated as the species absolute abundance in a given subMIC divided by the species absolute abundance in the absence of the antibiotic. Black horizontal line at y = 1 indicates no change in growth compared to no antibiotic condition. Each x-axis is semi-log scale. Data points indicate biological replicates. Lines indicate the average of n = 1 to n = 4 biological replicates in panel A and n = 3 to n = 4 biological replicates in panel B. Color indicates species, see 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S7 Fig(A) Scatterplots of maximum C. difficile subMIC fold change in multispecies communities at 48 h as a function of the number (left) or abundance (right) of sensitive biotic inhibitors for both metronidazole and vancomycin. The x-axis represents the number of sensitive biotic inhibitors in a given community in the beginning of the experiment (left) or summed absolute abundance of sensitive biotic inhibitors in a given community at 48 h in the absence of the antibiotic (right). The average subMIC fold change was calculated for each subMIC concentration by computing the average C. difficile absolute abundance in a given subMIC divided by the average C. difficile absolute abundance in the absence of the antibiotic, average of n = 1 to n = 4 biological replicates. The maximum subMIC fold change (y-axis) is the maximum of the average subMIC fold change across all subMICs. The sensitive and resistant inhibitors are determined as in (B) Box plot of the maximum subMIC fold change for C. difficile in multispecies communities. The maximum subMIC fold change (y-axis) is calculated as in panel A. Each data point represents 1 community. Community type (x-axis) determined as in P < 0.05, **P < 0.01, ***P < 0.001) according to a one-sided Mann–Whitney U test. (C) Schematic of subMIC difference metric. The x-axis is semi-log scale. Gray horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. (D) Scatterplots comparing the predicted value of the C. difficile subMIC difference in a given community (x-axis) with average experimental value of the C. difficile subMIC difference in the same community (y-axis). The predicted value was calculated by summing the C. difficile subMIC differences of each CD-SpX pairwise community for all SpX. SubMIC differences greater than 0 have subMIC fold changes greater than 1. SubMIC differences less than 0 have subMIC fold changes less than 1. Each point represents a given community at given concentration of metronidazole or vancomycin. Number of sensitive inhibitors (color) was determined as in 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S8 Fig Line plots of model prediction of absolute abundance of C. difficile and gut microbe pairwise communities at 48 h in presence of A metronidazole (MTZ) or B vancomycin (VAN). Each x-axis is semi-log scale. Y-axis is calculated OD600 (OD600 multiplied by relative abundance from 16S sequencing). Points indicate experimental data. Lines indicate model simulations. Color indicates species, see C. difficile . The data underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S9 Fig Line plots of model prediction of absolute abundance of species in multispecies communities at 48 h in the presence of A metronidazole (MTZ) or B vancomycin (VAN). Each x-axis is semi-log scale. The y-axis denotes calculated OD600 (OD600 multiplied by relative abundance from 16S sequencing). Data points indicate experimental data. Lines indicate model simulations. Color indicates species, see C. difficile . The data underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S10 Fig(A) Antibiotic gLV model prediction of pairwise and multispecies community data in the presence of each antibiotic concentration. These predictions are summarized in the blue “gLV + monospecies antib susceptibility” bar in (B) Simulated maximum subMIC fold change for a focal species cocultured with a 4 other species for 1,000 randomly generated parameter sets. All parameters were randomized between a set of bounds. Bounds: aji , growth rates , intraspecies interactions , and antibiotic susceptibility . Color of the data point indicates the abundance of focal species in the community in the absence of antibiotics . A community is classified as containing a susceptible inhibitor if for any non-focal species j, Bj < −0.1 and afocal,j < −0.1. Gray horizontal line at y = 1 indicates no change in growth compared to the no antibiotic condition. The data and modeling scripts underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7726490.(TIF)Click here for additional data file.S11 Fig(A) Line plot of the growth curve of D. piger. Colored data points indicate the time at which 5 separate D. piger spent media samples were harvested. Data points represent biological replicates. The line represents the mean of n = 5 biological replicates. (B) Line plot of C. difficile abundance at 41 h in 5 different D. piger spent medias in the presence of metronidazole (MTZ). The x-axis is semi-log scale. Data points indicate biological replicates. Line represents the mean of n = 4 biological replicates. (C) Line plot of the abundance of 4 clinical C. difficile isolates in the presence of metronidazole. Each x-axis is semi-log scale. The y-axis denotes the integral of C. difficile OD600 from 0 to 48 h. Data points indicate biological replicates. The line represents the mean of n = 3 biological replicates. Asterisks indicate a significant difference  according to an unpaired t test. (D) Line plot of C. difficile abundance at 48 h in a rich chemically defined media (“DM38”) in the presence of metronidazole. The x-axis is semi-log scale. For the D. piger coculture condition, OD600 denotes calculated OD600 (OD600 multiplied by relative abundance from 16S rRNA gene sequencing). Data points indicate biological replicates. The line represents the mean of n = 6 (fresh media) or n = 3 (DP SM) biological replicates. Asterisks indicate a significant difference  according to an unpaired t test. (E) Line plot of C. difficile abundance at 48 h in fresh media in the presence of metronidazole that was incubated in fresh media or D. piger spent media. The x-axis is semi-log scale. Data points indicate biological replicates. Line represents the mean of n = 4 biological replicates. No significant difference  according to an unpaired t test. (F) Line plot of C. difficile abundance at 48 h in fresh media, D. piger spent media, or D. piger spent media with a fresh media spike. The x-axis is semi-log scale. Data points indicate biological replicates. The line represents the mean of n = 4 biological replicates. Asterisks indicate significant difference  according to an unpaired t test. The data underlying panels ABD in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S12 Fig(A–E) Left: Schematic of 2 conditions being compared. Middle: Volcano plot of the fold change in C. difficile transcript abundance. Gray vertical lines indicate 2-fold change (1 in log2) and gray horizontal line indicates statistical significance (p = 0.05). Blue indicates genes annotated to be involved in metal import. Red indicates genes predicted to contain iron-sulfur clusters by MetalPredator. Right: Enriched KEGG modules in C. difficile. All KEGG pathways with significant enrichment scores from Gene Set Enrichment Analysis (GSEA) are shown. The data underlying all panels in this figure can be found in (TIF)Click here for additional data file.S13 Fig Scatterplots to evaluate the patterns in gene expression for gene orthologs between CDR20291 (this study) and CD630Δerm (Berges and colleagues [C. difficile in coculture with D. piger (panel A) or C. difficile in D. piger spent media (SM) (panel B) and C. difficile in fresh media in this study. Each data point indicates a gene. Percentages indicate percentage of genes in each quadrant. Black line indicates best fit linear regression for all data points. Gray line is y = x. The data underlying all panels in this figure can be found in lleagues ). Genes lleagues . Y-axis:(TIF)Click here for additional data file.S14 FigB. thetaiotaomicron abundance at 48 h in fresh media or in 75% D. piger spent media, 25% fresh media. The x-axis is semi-log scale. Data points represent biological replicates. Lines indicate the average of n = 3 biological replicates. The data underlying this figure can be found in DOI: 10.5281/zenodo.7626486.Line plot of (TIF)Click here for additional data file.S15 Fig(A) Bar plot of the amount of hydrogen sulfide in multiple conditions after 10 h of incubation. “Blank” represents a fresh media control. The y-axis denotes the percentage of the amount of hydrogen sulfide in the D. piger monoculture. Data points represent the mean of n = 2 technical replicates. Bar represents the mean of n = 8 biological replicates. Asterisks indicate significant difference  according to an unpaired t test. (B) Bar plot of the concentration of divalent transition metals in fresh media. Points represent the mean of n = 2 technical replicates. Bar represents the mean of n = 3 biological replicates. The data underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S16 Fig(A) Bar plot of hydrogen sulfide concentration in different medias. Data points represent n = 4 biological replicates. Bar represents the average of biological replicates. (B) Image of D. piger spent media 2 h after nitrogen purge (right) or no treatment (left). Precipitation of a black ferrous sulfide turns the media a darker color. (C) Line plot of C. difficile OD600 at 41 h in different medias in the presence of metronidazole. The x-axis is semi-log scale. Data points represent biological replicates. Lines indicate the average of n = 4 to n = 8 biological replicates. (D) Bar plot displaying MIC of data shown in C. Data points represent the MIC of n = 4 to n = 8 biological replicates. Bar represents the MIC determined based on an average OD600 of n = 4 to n = 8 biological replicates. The data underlying panels ACD in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S17 Fig(A) Line plots and bar plots of C. difficile metronidazole susceptibility in fresh media with and without metal supplementation. (Top) Bar plots of C. difficile metronidazole MIC in untreated fresh media (gray) and media with 1 mM metal supplementation (blue). In the metal combination condition (“Combo”), all 5 metals were supplemented at 1 mM. Data points represent the MIC of n = 4 biological replicates. Bar represents the MIC determined based on an average OD600 of n = 4 biological replicates. (Bottom) Line plots of C. difficile OD600 at 45 h in untreated fresh media (gray) or media supplemented with 1 mM metal (blue) in the presence of metronidazole (MTZ). Each x-axis is semi-log scale. Data points represent biological replicates. Lines indicate the average of n = 4 biological replicates. (B) Bar plot of delta Ct (ΔCt) values of 3 genes in C. difficile grown in fresh media, D. piger spent media (DP SM), or D. piger spent media with metal supplementation based on qRT-PCR. 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. Asterisks indicate significant difference  according to an unpaired t test. The data underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S18 Fig(A) Pie charts of the change in C. difficile MIC in pairwise and multispecies communities compared to monoculture. (B) Pie charts of the growth enhancement of C. difficile in pairwise and multispecies communities. Growth enhancement is defined as a maximum subMIC fold change that is greater than 1 and is greater than the maximum subMIC fold change in C. difficile monoculture for that antibiotic . The data underlying all panels in this figure can be found in DOI: 10.5281/zenodo.7626486.(TIF)Click here for additional data file.S1 TableC. difficile monoculture MIC from same experimental day given for comparison, as monoculture MIC can vary due to preculture conditions and glycerol stock batches.MICs in μg/mL. (XLSX)Click here for additional data file.S2 TableC. difficile monoculture MIC from same experimental day given for comparison, as monoculture MIC can vary due to preculture conditions and glycerol stock batches. “No growth” represents no growth of C. difficile across all concentrations and therefore no MIC could be determined.MICs in μg/mL. (XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.