Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

Damien J Cabral; Swathi Penumutchu; Elizabeth M Reinhart; Cheng Zhang; Benjamin J Korry; Jenna I Wurster; Rachael Nilson; August Guang; William H Sano; Aislinn D Rowan-Nash; Hu Li; Peter Belenky

doi:10.1016/j.cmet.2019.08.020

. Author manuscript; available in PMC: 2020 Oct 1.

Published in final edited form as: Cell Metab. 2019 Sep 12;30(4):800–823.e7. doi: 10.1016/j.cmet.2019.08.020

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

Damien J Cabral ¹, Swathi Penumutchu ¹, Elizabeth M Reinhart ¹, Cheng Zhang ², Benjamin J Korry ¹, Jenna I Wurster ¹, Rachael Nilson ¹, August Guang ^3,⁴, William H Sano ^1,⁵, Aislinn D Rowan-Nash ¹, Hu Li ², Peter Belenky ^1,^6,^*

PMCID: PMC6948150 NIHMSID: NIHMS1051195 PMID: 31523007

SUMMARY

Although antibiotics disturb the structure of the gut microbiota, factors that modulate these perturbations are poorly understood. Bacterial metabolism is an important regulator of susceptibility in vitro and likely plays a large role within the host. We applied a metagenomic and metatranscriptomic approach to link antibiotic-induced taxonomic and transcriptional responses within the murine microbiome. We found that antibiotics significantly alter the expression of key metabolic pathways at the whole-community and single-species levels. Notably, Bacteroides thetaiotaomicron, which blooms in response to amoxicillin, upregulated polysaccharide utilization. In vitro, we found that the sensitivity of this bacterium to amoxicillin was elevated by glucose and reduced by polysaccharides. Accordingly, we observed that dietary composition affected the abundance and expansion of B. thetaiotaomicron, as well as the extent of microbiome disruption with amoxicillin. Our work indicates that the metabolic environment of the microbiome plays a role in the response of this community to antibiotics.

In Brief

Using a combined metagenomic and metatranscriptomic approach, Cabral et al. find that antibiotics change both the metabolic environment within the gut and the transcriptional response of the microbiome at the whole-community and species-level. This suggests that host diet and metabolic state might affect the microbiome during antibiotic therapy.

Graphical Abstract

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INTRODUCTION

Since the discovery of penicillin in the early 20^th century, antibiotics have revolutionized modern medicine and saved billions of lives. However, decades of overuse have led to the emergence of multi- or pan-resistant bacterial infections that greatly increase morbidity and mortality, resulting in over 25,000 annual deaths in the United States alone (Alanis, 2005; Falagas and Bliziotis, 2007; Services, 2013; Wright, 2012). More recently, microbiome research has led to an increased awareness of the detrimental effects that antibiotics have on the trillions of symbiotic bacteria that inhabit the gastrointestinal tract. Treatment with broad-spectrum antibiotic drugs has been shown to induce rapid, though typically transient, reductions in bacterial counts and diversity within the microbiome (Dethlefsen and Relman, 2011). This disruption, broadly referred to as dysbiosis, can lead to decreased colonization resistance against various fungal and bacterial pathogens, leading to a number of opportunistic infections (Blaser, 2011; Mukherjee et al., 2014; Peleg et al., 2010; Preidis and Versalovic, 2009; Rafii et al., 2008). Most notably, antibiotic treatment increases the risk of Clostridioides-difficile-induced colitis, a major nosocomial infection estimated to have resulted in 29,000 deaths in 2011 alone (Chang et al., 2008; Lessa et al., 2015; Theriot et al., 2016). Furthermore, long-term microbiome dysbiosis has been associated with a number of chronic conditions, including inflammatory bowel disease, asthma, diabetes mellitus, various autoimmune diseases, obesity, and certain neuropsychiatric conditions (Blaser, 2011; De Luca and Shoenfeld, 2019; Dickerson et al., 2017; Foster and McVey Neufeld, 2013; Foster et al., 2017; Hartstra et al., 2015; Leong et al., 2018; Lynch and Boushey, 2016; Riiser, 2015; Rogers et al., 2016; Tremlett et al., 2017; Vieira et al., 2014). Novel therapies, particularly fecal microbiota transplants (FMT), have shown considerable promise in preventing or reversing some of these adverse effects (Di Luccia et al., 2015; Kassam et al., 2013; Khanna et al., 2016; Suez et al., 2018; van Nood et al., 2013; Vrieze et al., 2012). However, to combat the dual threats of resistance- and antibiotic-induced dysbiosis, it is imperative to develop strategies that improve the efficacy of our current drug arsenal while also reducing treatment-associated microbiome disruption. Any strategy to do so requires deepening our understanding of antibiotic activity in vivo and identifying strategies utilized by bacteria to survive treatment in the microbiome.

To date, many studies have explored the effects of either short- or long-term antibiotic usage on the structure or gene content of the microbiome via 16S rRNA amplicon sequencing or shotgun metagenomics (Bokulich et al., 2016; Cabral et al., 2017; Dethlefsen and Relman, 2011; Jakobsson et al., 2010; Rodrigues et al., 2017; Suez et al., 2018; Theriot et al., 2016; Yang et al., 2017; Zaura et al., 2015). However, these studies have not answered how and why certain bacteria are capable of tolerating antibiotic therapy in vivo whereas others are not. Similarly, our current understanding of the spectrum of antibiotic efficacy is confined largely to in vitro assays that fail to recapitulate the complex environment that bacteria encounter within a host. Thus, traditional classifications of antibiotics as broad-versus narrow-spectrum or bacteriostatic versus bactericidal might have less relevance with respect to their effect on the microbiota.

Host metabolism is likely one of the most critical environmental factors that affect the microbiome. A number of studies have found that host diet and host metabolic state have perhaps the most profound effects on the composition and activity of the human gut microbiota (Carmody et al., 2015; Kisuse et al., 2018; Rothschild et al., 2018; Turnbaugh, 2017; Turnbaugh et al., 2006, 2009). Furthermore, bacterial metabolism might also modulate antibiotic activity within these communities. Recent work has demonstrated that efficacy of bactericidal antibiotics and metabolic activity are tightly linked in vitro (Allison et al., 2011; Belenky et al., 2015; Dwyer et al., 2014; Kohanski et al., 2007; Lobritz et al., 2015; Thomas et al., 2013). Three major classes of bactericidal drugs—beta-lactams, aminoglycosides, and fluoroquinolones—have been shown to increase the abundance of reactive oxygen species (ROS), nicotinamide adenine dinucleotide (NAD)⁺, and tricarboxylic acid (TCA) cycle intermediates, suggesting a temporary induction of metabolic activity (Belenky et al., 2015; Dwyer et al., 2014; Ferrándiz et al., 2015; Hoeksema et al., 2018; Lobritz et al., 2015; Yang et al., 2018). This increase in metabolic activity is likely caused in part by the induction of deleterious futile cycles that elevate ATP consumption (Adolfsen and Brynildsen, 2015; Belenky et al., 2015; Cho et al., 2014; Lobritz et al., 2015; Neijssel et al., 1990). Furthermore, artificially elevating respiratory metabolism via genetic disruptions or supplementation with TCA cycle metabolites can promote susceptibility to bactericidal drugs (Lobritz et al., 2015; Meylan et al., 2017, 2018; Su et al., 2018). Conversely, reducing metabolic capacity by inhibiting the electron transport chain and promoting the use of fermentation reduces susceptibility to bactericidal drugs (Dwyer et al., 2014; Lobritz et al., 2015).

Typically, the gut microbiota consists of a diverse array of microbes that utilize fermentation or respiration (anaerobic or aerobic) depending on environmental factors such as the presence of oxygen, availability of electron acceptors, redox potential, and the availability of nutrients (Brown et al., 2013; Faber et al., 2017; Fischbach and Sonnenburg, 2011; Hughes et al., 2017; Jones et al., 2007; Ravcheev et al., 2013; Reese et al., 2018). In a healthy gut, the majority of microbes are likely fermenting, though some commensals, such as Bacteroides thetaiotaomicron, can utilize alternative electron acceptors to perform anaerobic respiration (Brown et al., 2013; Fischbach and Sonnenburg, 2011). Under dysbiotic conditions (such as those induced by antibiotic treatment and inflammation), redox potential within the gut has been proposed to increase and promote respiration by facultative anaerobes such as those from the family Enterobacteriaceae (Hughes et al., 2017; Reese et al., 2018). Additionally, potentially pathogenic bacteria such as Salmonella enterica and Escherichia coli, utilize aerobic respiration to facilitate colonization of the gut (Faber et al., 2017; Jones et al., 2007). However, it is currently unclear whether and how the utilization of fermentation or respiration within the gut affects antibiotic susceptibility in vivo.

Importantly, modulation of central carbon metabolism is known to be a potent tolerance mechanism in several clinically relevant pathogens. In Mycobacterium tuberculosis, diverting metabolic flux away from the TCA cycle via the glyoxylate shunt or triacylglycerol synthesis can induce a semi-dormant state that is highly tolerant to antibiotic treatment (Baek et al., 2011; Nandakumar et al., 2014; Rowan et al., 2016). Similarly, studies utilizing Staphylococcus epidermidis and Staphylococcus aureus have demonstrated that a dysfunctional TCA cycle provides a fitness advantage in the presence of beta-lactam antibiotics and that clinical isolates of these bacteria display reduced TCA flux compared with wild-type (Thomas et al., 2013; Wang et al., 2018). Lastly, E. coli and S. aureus persister and biofilm cells are known to have reduced metabolic activity, which contributes to their tolerance to antibiotics (Allison et al., 2011; Cabral et al., 2018; Conlon et al., 2016; Meylan et al., 2017; Shan et al., 2017). It is likely that metabolic state might also affect antibiotic susceptibility within the microbiome, though this has not been studied to date.

The vast majority of the research linking metabolism to antibiotic susceptibility has focused on easily culturable, pathogenic bacteria grown in vitro under highly artificial conditions. It is largely unknown what effect, if any, metabolism has on antibiotic efficacy and tolerance in vivo among the trillions of bacteria that constitute the microbiome. As the cost of next-generation sequencing continues to decrease and existing databases of microbial genomes are improved, it is now possible to utilize shotgun metagenomics and metatranscriptomics to provide an unparalleled view of how antibiotics affect the composition of the microbiome while also tracking antibiotic-associated transcriptional responses. In this study, we utilize a multi-omic approach to characterize the functional response of the gut microbiota, particularly as it relates to metabolism, at both the community and single-species level. This approach enables a degree of functional profiling that is unobtainable using either 16S amplicon or metagenomic sequencing alone. Using a combination of metagenomics and metatranscriptomics, we are able to link changes in transcriptional activity to antibiotic susceptibility within the microbiome. We hypothesize that the differential utilization of carbon sources and metabolic pathways will mediate antibiotic tolerance responses within the microbiome. If true, this would have important implications regarding the effect of host diet and/or metabolic state in the development of antibiotic-induced dysbiosis.

Here, we describe a study to profile the effects of antibiotics from three different classes on the structure and function of the murine microbiome. The three antibiotics chosen for this study were amoxicillin (beta-lactam), ciprofloxacin (fluoroquinolone), and doxycycline (tetracycline). Beta-lactams and fluoroquinolones have been demonstrated to induce bacterial metabolism and have bactericidal activity in vitro (Belenky et al., 2015; Kohanski et al., 2007; Lobritz et al., 2015; Meylan et al., 2017). Conversely, tetracyclines are known to inhibit bacterial metabolism and are bacteriostatic in vitro (Lobritz et al., 2015). Additionally, all three antibiotics are prescribed frequently in a clinical setting and have high oral bioavailability (Agwuh and MacGowan, 2006; Arancibia et al., 1980; Drusano et al., 1986; Olesen et al., 2018).

Using species-specific taxonomic profiling, we observed that amoxicillin drastically reduced the relative abundance of nearly all species, with the notable exception of several members of the Bacteroides genus. Conversely, both doxycycline and ciprofloxacin reduced the relative abundance of several Bacteroides, with a concomitant increase in Firmicutes. These changes in taxonomic structure were associated with significant effects on the relative abundance of transcripts. Many of the changes we observed were related to metabolism, which was largely decreased at the whole-community level after treatment. At the single-species level, we detected similar metabolism-related transcriptional changes, with some notable exceptions. B. thetaiotaomicron, which increases in relative abundance during amoxicillin treatment, increases the expression of numerous genes related to stress responses and polysaccharide utilization while reducing the expression of hexose utilization genes. We also found that treatment with amoxicillin but not ciprofloxacin drastically reduced the concentration of glucose within the murine cecum. Subsequent in vitro experiments demonstrated that utilization of polysaccharides rather than glucose significantly decreased sensitivity to amoxicillin in both B. thetaiotaomicron and the related species Bacteroides fragilis. Additionally, we found that dietary supplementation with glucose is capable of altering the response of the murine gut microbiota to amoxicillin. Specifically, we observed that dietary supplementation with glucose alters the response of B. thetaiotaomicron in vivo, such that this bacterium was more abundant prior to treatment, but had a reduced expansion after amoxicillin. Overall, we found that short-term dietary modulation had robust effects on the extent of microbiome perturbation during amoxicillin treatment. Thus, these data suggest that the availability of nutrients to the microbiota might be an important determinant of antibiotic-induced disruption.

RESULTS

Previous studies using 16S rRNA amplicon or metagenomic sequencing have found that antibiotics induce rapid and dramatic disruptions of microbiome community structure (Bokulich et al., 2016; Dethlefsen and Relman, 2011; Rodrigues et al., 2017). However, these studies have not provided mechanistic insight into the bacterial responses that contribute to either death or tolerance. To provide this functional information, we employed an analytical pipeline that utilizes a combination of shotgun metagenomic and metatranscriptomic sequencing (Figure 1A). We treated mice with three different clinically relevant drugs: amoxicillin (a beta-lactam), ciprofloxacin (a fluoroquinolone), and doxycycline (a tetracycline). Mice received either a single antibiotic or a pH-matched vehicle control (n = 4 per group); dosages were based on those used in previous mouse studies (Abgueguen et al., 2007; Marx et al., 2014; Yang et al., 2017). Amoxicillin was administered ad libitum in the drinking water for 12 h, whereas ciprofloxacin and doxycycline were administered for 24 h. These time points were selected because they were sufficient to induce changes in community structure in preliminary studies, while being short enough to prevent the microbiome from reaching a new steady state. After treatment, cecal contents were collected and the total RNA and DNA were extracted for shotgun sequencing.

Figure 1. — For a Figure360 author presentation of this figure, see https://doi.org/10.1016/j.cmet.2019.08.020.

(A) Experimental design utilized in this study. Figure was created using BioRender.

(B) Average relative abundance of bacterial phyla. Phyla with a relative abundance of less than 1% in an average of all samples were grouped into “Other.” Data are represented as average relative abundance ± SEM for n = 4. See also Figure S1A.

(C) Alpha diversity of each sample as measured by number of observed species (top) and the Shannon diversity index (bottom). Whiskers represent mean ± SEM for n = 4 (*p < 0.05, Mann-Whitney U test).

(D) PCoA of beta diversity via Bray-Curtis Dissimilarity.

(E) Heatmap of antibiotic-induced changes in relative abundance of the fifty most abundant species. Rows were clustered according to NCBI’s Common Tree and color-coded according to phylum. Cell color denotes the average percent change in relative abundance versus the corresponding time-matched vehicle control.

Metagenomic data were analyzed using Kraken2 and Bracken to identify species-level shifts in community composition (Lu et al., 2017; Wood and Salzberg, 2014). Metatranscriptomic reads were analyzed at the whole-community level with the HMP Unified metabolic access network 2 (HUMAnN2) (Franzosa et al., 2018) and simple annotation of Metatranscriptomes by sequence analysis 2 (SAMSA2) (Westreich et al., 2018) pipelines to identify differentially abundant pathways, subsystems, and transcripts. Lastly, a previously published pipeline (Deng et al., 2018) was utilized to align metatranscriptomic reads to the genomes of individual bacterial species to identify species-specific transcriptional changes among highly abundant members of the microbiota in response to different classes of antibiotics (Figure 1A).

Amoxicillin, Doxycycline, and Ciprofloxacin Elicit Unique Changes in Microbiome Structure

Using shotgun metagenomics, we profiled the shifts in microbiome composition elicited by three clinically relevant antibiotics compared with time-matched controls. At the phylum level, we observed that each antibiotic induced unique taxonomic changes (Figure 1B). Of all antibiotics tested, amoxicillin therapy elicited the most profound changes in community composition, drastically increasing the relative abundance of Bacteroidetes while decreasing the abundance of most other phyla compared with time-matched controls (Figure 1B). In contrast, samples treated with either ciprofloxacin or doxycycline displayed a higher relative abundance of Firmicutes and a reduced abundance of Bacteroidetes than those treated with amoxicillin. Despite this commonality, we observed several interesting differences; for example, Verrucomicrobia was observed at a higher relative abundance during doxycycline therapy than in controls but was reduced during ciprofloxacin treatment (Figure 1B).

These trends are also reflected in both alpha and beta diversity metrics (Figures 1C and 1D). Of the antibiotics tested, amoxicillin treatment had the biggest effect on the overall alpha diversity of this community as measured by Shannon diversity and the number of observed species, though the latter was not statistically significant (Mann-Whitney U test, p = 0.057) (Figure 1C). Interestingly, ciprofloxacin induced a small, yet significant, increase in Shannon diversity despite reducing the number of observed species, suggesting that this antibiotic increased the evenness of the community despite reducing its overall richness (Figure 1C). Doxycycline, in contrast, did not elicit a significant change with either diversity metric (Figure 1C). When beta diversity was analyzed using the Bray-Curtis Dissimilarity metric in principle coordinate analysis (PCoA), we observed that there were no significant differences between control groups (PERMANOVA, p = 0.123). However, each antibiotic treatment clustered separately from its corresponding controls (PERMANOVA, amoxicillin: p = 0.03; doxycycline: p = 0.016; ciprofloxacin: p = 0.034). Amoxicillin clustered separately from all other treatment groups along principle coordinate axis 1 (PCo1), which explained the largest amount of variation in the dataset (74.4% of variation) (Figure 1D). It is possible that this shift along PCo1 could be explained by the elevated relative abundance of Bacteroidetes after amoxicillin treatment, given that this was not observed with the other two antibiotics (Figure 1B). Due to similar changes in community structure, doxycycline and ciprofloxacin clustered closer together, whereas amoxicillin samples clustered separately from any other group (Figure 1D). Despite their relative similarities, however, we were still able to detect a significant difference in beta diversity between the ciprofloxacin and doxycycline groups (PERMANOVA, p = 0.031).

Unlike traditional 16S rRNA amplicon sequencing, shotgun metagenomics enables species-level analysis of community composition. Examination of the data at the species level revealed several notable trends. Lactobacillus johnsonii, which has been explored as a potential probiotic for numerous conditions, decreased in relative abundance during treatment with amoxicillin and ciprofloxacin but increased during treatment with doxycycline (Figure 1E) (Bergonzelli et al., 2006; Chiva et al., 2002; Krasse et al., 2006; Van Gossum et al., 2007; Yamano et al., 2006). This trend was particularly striking in the ciprofloxacin treatment, where L. johnsonii decreased in relative abundance despite Firmicutes making up a larger share of the community (Figures 1B and 1E). Additionally, species-level metagenomics indicated that the expansion of Bacteroidetes observed during amoxicillin treatment was driven primarily by members of the Bacteroides genus, particularly, B. thetaiotaomicron and B. vulgatus. Although these species increased in relative abundance during amoxicillin therapy, they decreased in response to the other two drugs (Figure 1E). It is known that B. thetaiotaomicron can colonize germ-free mice, but few studies have shown that this bacterium naturally colonizes specific-pathogen-free mice. Using a second metagenomic classifier, MetaPhlan2, we detected B. thetaiotaomicron at levels comparable to those detected with Kraken2 (Figure S1A). To further confirm its identity, we performed metagenomic assembly of our amoxicillin-treated samples, which produced a complete genome that was subsequently identified as B. thetaiotaomicron (Figure S1C). Furthermore, we isolated two potential B. thetaiotaomicron strains from the cecum of mice for further investigation. Phylogenetic analysis of these isolates and the metagenomic assembly demonstrated that they were, in fact, isolates of B. thetaiotaomicron on the basis of 16S rRNA (Figure S1B) and whole-genome (Figure S1C) sequence similarity. Together, these data confirm the identity of B. thetaiotaomicron in our dataset.

Antibiotic Treatment Is Associated with Broad Reductions in Metabolic Gene Expression within the Gut Microbiota at the Whole-Community Level

To date, many studies have profiled the effect of antibiotic usage on the composition of the gut microbiome (Behr et al., 2018; Bokulich et al., 2016; Cabral et al., 2017; Dethlefsen and Relman, 2011; Langdon et al., 2016; Suez et al., 2018; Zarrinpar et al., 2018; Zaura et al., 2015). In comparison, little is known about the functional changes within these communities or potential tolerance mechanisms employed by resident members of the microbiota in response to antibiotic therapy. Addressing this knowledge gap might yield potential strategies that limit collateral damage to the commensal microbiota and reduce the risk of antibiotic-induced dysbiosis. To provide mechanistic insight unavailable with metagenomics, we first analyzed our metatranscriptomic reads via the SAMSA2 pipeline (Westreich et al., 2018). This analytical pipeline rapidly aligns metatranscriptomic reads to various databases (such as RefSeq, SEED Subsystems, virulence factors, and CAZymes) and enables differential expression analysis between experimental conditions. However, this tool does not normalize for changes in the underlying community structure; thus, these data are likely driven by changes in microbiome composition elicited by antibiotics. Despite this, it provides useful insight into the changes in overall transcript abundance during antibiotic treatment.

At the whole-community level, we found that each antibiotic elicited a unique profile at the transcript and SEED subsystem levels (Figures 2A–2C; Tables S2 and S3). In the doxycycline condition, we observed a significant decrease in the protein metabolism subsystem, which includes various transcripts that encode for ribosomal proteins, translation elongation factors, tRNA ligases, and chaperone proteins, among others (Figure 2D; Tables S2 and S3). This observation is consistent with doxycycline’s known inhibitory effect on translation through interaction with the 30S ribosomal subunit (Chukwudi, 2016). Additionally, doxycycline treatment reduced the relative abundance of DNA, nucleoside, and nucleotide metabolism transcripts, which might be indicative of a generalized inhibition of DNA replication, similar to what has been observed in vitro (Figure 2D; Table S3) (Pato, 1977). Interestingly, we also observed increases in iron acquisition and metabolism during doxycycline treatment. Tetracycline class antibiotics are known to have iron-chelating properties (Figure 2D; Table S3) (Grenier et al., 2000); therefore, the detected increase in abundance of iron acquisition transcripts could reflect a decreased availability of free iron in the presence of doxycycline. Lastly, we also observed an increase in the relative abundance of phage-related transcripts with doxycycline treatment (Figure 2D; Table S3). Although a potential mechanism for this change is unclear at this time, previous work has demonstrated that tetracycline antibiotics can increase plaque formation by bacteriophages in vitro (Loś et al., 2008).

Figure 2. — (A–C) Volcano plots of the metatranscriptomic profile of the murine cecal microbiome during doxycycline (A), ciprofloxacin (B), and amoxicillin (C) treatment. Points in red represent transcripts for which a statistically significant change in expression was detected (Benjamini-Hochberg adjusted p value < 0.1). Genes of interest are labeled. See Table S2 for full results.

(D–F) Differentially expressed (Benjamini-Hochberg adjusted p value < 0.1) level 1 SEED subsystems in the murine cecal metatranscriptome during doxycycline (D), ciprofloxacin (E), and amoxicillin (F) treatment. See Table S3 for full results. Data are represented as average log₂ fold change ± standard error for n = 4.

(G) Differentially expressed (Benjamini-Hochberg adjusted p value < 0.1) level 2 SEED subsystems in the murine cecal metatranscriptomes during amoxicillin treatment. See also Table S3. Data are represented as average log₂ fold change ± standard error for n = 4.

In the ciprofloxacin treatment group, we observed an increase in the relative abundance of phage-related transcripts. Previous in vitro data have demonstrated that ciprofloxacin treatment induces the expression of phages in S. aureus (Figure 2E; Table S3) (Goerke et al., 2006). Additionally, some temperate phages (such as bacteriophage λ) are known to induce the lytic cycle in response to DNA damage, which can be caused by ciprofloxacin treatment (Shao et al., 2015; Tamayo et al., 2009). Therefore, this finding might suggest that ciprofloxacin treatment leads to increased phage production in the gut microbiota. Conversely, we found that nucleotide and nucleoside metabolism, which includes genes responsible for nucleotide synthesis and recycling, were decreased in response to this antibiotic (Figure 2E; Table S3). Ciprofloxacin, a fluoroquinolone-class antimicrobial, is known to inhibit bacterial DNA gyrase and topoisomerase IV, resulting in the arrest of DNA replication and the accumulation of double-strand breaks that subsequently lead to cell death (Conley et al., 2018; Dwyer et al., 2007; LeBel, 1988). Therefore, this finding indicates that ciprofloxacin might lead to a reduction in DNA replication (and likely cell division) within members of the microbiota. Additionally, this finding could suggest that ciprofloxacin is more effective against members of the microbiota that are replicating more frequently. Regardless, these findings suggest that DNA replication plays a role in the response of the gut microbiota to ciprofloxacin.

Overall, we found that the taxonomic response elicited by amoxicillin was the most robust of the three antibiotics tested. For this reason, we focused on this antibiotic for the majority of our further analyses. In amoxicillin-treated microbiota, we observed broad reductions in the relative abundance of a number of key cellular pathways, including cell division and nitrogen metabolism, which could be indicative that the microbiome after therapy is in a less-active metabolic state (Figures 2F and 2G; Tables S2 and S3). Counterintuitively, we observed a decrease in sporulation pathways during amoxicillin treatment, perhaps reflecting the drastic reduction of spore-forming Firmicutes elicited by this antibiotic (Figures 1B and 2F; Table S3). Interestingly, we also observed a similar reduction in sporulation gene levels with ciprofloxacin without a corresponding reduction of Firmicutes; however, the mechanism of this reduction is unclear at this time (Figure 2E; Table S3).

Additionally, amoxicillin increased the relative abundance of genes related to membrane transport (Figure 2F; Table S3). A closer examination of these data reveals that the observed increase in membrane transport is driven partly by increases in solute carriers related to osmotic stress and solute transporters (Figure 2G; Table S3). Additionally, we found that amoxicillin treatment resulted in profound changes to the relative abundance of key metabolic genes. Specifically, we detected a decrease in sugar phosphotransferase systems (PTSs), which are responsible for importing hexoses and disaccharides, whereas glycoside hydrolases (GHs), which break down complex polysaccharides, were increased (Figure 2G; Table S3). We also detected statistically significant decreases in membrane-bound hydrogenases and electron transport, which might suggest that the community surviving amoxicillin is less-metabolically active compared with untreated controls. Similarly, we observed decreases in DNA polymerase III and various amino acid or tRNA subsystems (Figure 2G; Table S3). Together, these data indicate that each antibiotic elicits unique and extensive changes to the relative abundance of key metabolic transcripts in the gut microbiota.

Amoxicillin Treatment Alters CAZyme Expression within the Gut Microbiome

Analysis of metatranscriptomic data via SAMSA2 suggested broad reductions in metabolic transcript abundance in response to antibiotic therapy. However, this tool does not account for changes in population composition induced by antibiotic treatment. We subsequently analyzed our data via the HUMAnN2 pipeline, which enables normalization of metatranscriptomic reads by using paired metagenomic data to account for changes in community structure (Franzosa et al., 2018). This is particularly advantageous in this study, as antibiotics induce changes in the taxonomic composition that likely drive transcriptional changes at the whole-community level. Thus, although SAMSA2 can profile relative transcript abundance within the microbiome, HUMAnN2 can provide relative expression amounts. In conjunction, the application of both tools in parallel provides a more comprehensive approach to profile the transcriptional response to antibiotic treatment.

Similar to the data generated from SAMSA2, we found that each antibiotic elicited a unique gene expression profile at the Gene Ontology (GO) term level as measured using HUMAnN2 (Figure 3A; Table S4). At the MetaCyc pathway level, we observed that all three drug treatments reduced the expression of numerous energy, amino acid, and nucleotide metabolic activities (Figures 3B–3D; Table S4). During ciprofloxacin treatment, we observed an increase in anaerobic energy metabolism and a decrease in aerobic respiration (Figure 3B; Table S4). Interestingly, ciprofloxacin treatment also reduced several pathways for nucleotide biosynthesis or salvage. This observation is consistent with our findings via SAMSA2 and might point to reduced levels of DNA replication after ciprofloxacin therapy. Analysis of the doxycycline treatment group revealed a single pathway, L-serine and glycine biosynthesis, that was found to be more abundant in antibiotic samples compared with controls (p = 0.033) (Table S4). In contrast, 108 metabolic pathways with diverse functions were found to be more abundant in control samples than in doxycycline-treated samples (Figure 3C; Table S4). This observation indicates that doxycycline is associated with profound reductions in overall gene expression and is consistent with previous literature demonstrating that bacteriostatic drugs have a similar effect in vitro (Kohanski et al., 2007; Lobritz et al., 2015). In general, we found that our whole-community level metatranscriptomic data for both ciprofloxacin and doxycycline were largely consistent with the mechanism of action and previous in vitro findings for both antibiotics (Chopra and Roberts, 2001; LeBel, 1988).

Figure 3. — (A) Heatmap of log₂ fold changes of observed GO terms in the murine cecal metatranscriptome for each antibiotic treatment. Cell color denotes the average log₂ fold change versus control for n = 4. For full GO term identifiers and abundances, see Table S4.

(B–D) Linear discriminant analysis (LDA) score of differentially expressed MetaCyc pathways during ciprofloxacin (B), doxycycline (C), and amoxicillin (D) treatment. LDA scores were calculated using LEfSe. Color indicates general metabolic processes associated with each pathway. For full pathway names and statistics, see Table S4.

(E) Differentially expressed CAZymes (Benjamini-Hochberg adjusted p value < 0.1) in metatranscriptomic reads obtained from amoxicillin-treated mice. Colors correspond to enzyme classification in the dbCAN database. Black and gray bars correspond to enzymes that act on either alpha- or beta-glyosidic bonds, respectively. Data are shown as average log₂ fold change ± standard error for n = 4. See also Table S5.

(F) Average substrate utilization of cecal microbiota isolated from amoxicillin- (n = 4) or control- (n = 5) treated mice. Data were obtained using Biolog AN Microplates and bars represent average well-color development at OD590 ± SEM for each compound class across replicate mice and wells for each class of substrates (***p < 0.001; unpaired t test). See also Figure S2.

(G) Quantitation of 16S rRNA copies in the Biolog assay inoculum as measured by qPCR. Data are represented as average Ct value ± SEM. See also Figure S2.

Recent in vitro data indicate that active microbial metabolism contributes to beta-lactam toxicity and that reduced metabolic activity promotes tolerance and persistence (Adolfsen and Brynildsen, 2015; Amato et al., 2014; Belenky et al., 2015; Cabral et al., 2018; Conlon et al., 2016; Henry and Brynildsen, 2016; Kohanski et al., 2007; Lewis, 2007; Lobritz et al., 2015; Meylan et al., 2017; Orman and Brynildsen, 2013; Rowan et al., 2016; Shan et al., 2017; Thomas et al., 2013). Thus, we would anticipate that amoxicillin treatment in vivo would select for members of the microbiota that are either intrinsically less-metabolically active or can alter metabolism in response to stress. Consistent with this hypothesis, we detected only two MetaCyc pathways that were found to be more abundant in amoxicillin-treated samples than in controls: putrescine biosynthesis IV (PWY-6305) and superpathway of hexuronide and hexuronate degradation (GALACT-GLUCUROCAT-PWY) (Figure 3D; Table S4). Interestingly, both of these pathways have been previously implicated in bacterial stress responses. In E. coli, putrescine synthesis plays a role in the response to osmotic shock and low pH (Carper et al., 1991; Kashiwagi et al., 1992; Schiller et al., 2000; Schneider et al., 2013), whereas hexuronide degradation is responsible for the catabolism of β-D-glucuronosides that are commonly found in animal mucus and bile (Chang et al., 2004; Fabich et al., 2008; Lippel and Olson, 1968; Tutukina et al., 2016). Conversely, we found that amoxicillin treatment reduced the expression of a number of key central carbon metabolism pathways in relation to controls (Figure 3D; Table S4). Specifically, we detected decreases in glycolysis pathways from glucose (ANAGLYCOLY-SIS-PWY), glucose-6-phosphate (GLYCOLYSIS), and fructose-6-phosphate (PWY-5484), among others (Figure 3D; Table S4). Similarly, we found a decrease in the TCA cycle, suggesting that carbon metabolism is broadly reduced after amoxicillin treatment. We also detected reductions in peptidoglycan biosynthesis and several deoxyribonucleotide and ribonucleotide biosynthesis and salvage pathways, which play important roles in cell division and DNA replication, respectively (Figure 3D; Table S4). Together, these observations suggest that amoxicillin treatment results in broad reductions in metabolic gene expression at the whole-community level. This reduction in metabolic gene expression could occur via two related mechanisms. First, amoxicillin might select for sub-populations of bacteria within these communities that have reduced metabolic gene expression. Alternatively, bacteria might actively reduce the expression of such genes in response to antibiotic stress as a tolerance mechanism. However, these explanations are not mutually exclusive, and both might play a role in the observed reduction in metabolic gene expression.

On the basis of these findings, it is apparent that the microbial community after amoxicillin treatment displayed altered carbohydrate metabolism in relation to untreated controls. A number of previous studies have characterized the metabolic capacity of the microbiota to utilize carbohydrates through the analysis of carbohydrate active enzymes (CAZymes) (Bhattacharya et al., 2015; Cantarel et al., 2012; Hehemann et al., 2012; Huang et al., 2018a; Smits et al., 2017; Soverini et al., 2017). However, none of these studies have analyzed the effect of antibiotic therapy on CAZyme expression within the microbiota. Furthermore, most of these studies have utilized metagenomic sequencing and thus provide no insight into the transcriptional response of CAZymes to perturbation. To characterize the effect of amoxicillin treatment on carbohydrate utilization in vivo, we subsequently analyzed the differential transcript abundance of CAZymes within our SAMSA2 metatranscriptomic dataset (Figure 3E; Table S5). Overall, we found that amoxicillin treatment had a profound effect on CAZymes, resulting in the differential transcript abundance of 68 CAZymes across 6 different classes (Figure 3E; Table S5). Due to the increased relative abundance of Bacteroides in amoxicillin samples compared with controls, we hypothesize that many of these changes are driven by this genus (and the corresponding reduction in nearly all Firmicutes species). Among these 68 CAZymes, 33 increased in response to amoxicillin treatment whereas 35 decreased, compared with controls (Table S5). A detailed analysis of these differentially abundant CAZymes reveals several notable trends that provide insight into carbohydrate utilization during amoxicillin treatment. Of the 33 CAZymes that increased during amoxicillin therapy, 21 (63.6%) appeared to act primarily on α-glycosidic bonds. Conversely, 26 of the 35 CAZymes (74.3%) that were decreased in amoxicillin treatment acted on carbohydrates containing β-glycosidic bonds (Figure 3E). This discrepancy suggests that amoxicillin treatment fundamentally alters the type of carbohydrates utilized by the microbiota. To analyze the source of these carbohydrates, we subsequently classified the differentially expressed CAZymes according to their substrates as described previously (Cantarel et al., 2012; Smits et al., 2017). Of the 31 glycoside hydrolase (GH) or pectate lyase (PL) families that increased in abundance during amoxicillin therapy, 12 (38.7%) utilize animal- or host-derived carbohydrates. In comparison, of the 25 GH or PL families that are decreased in amoxicillin-treated microbiota, only 2 (8%) were found to act on host-derived substrates. Therefore, amoxicillin treatment appears to increase the abundance of transcripts for such substrates, indicating that the microbiota in this state might preferentially utilize host-derived carbohydrates containing alpha-glycosidic bonds. Additionally, this observation supports our earlier finding that amoxicillin treatment increases the expression of genes involved in the degradation of hexuronide and hexuronate, which are common components of animal mucus and bile (Chang et al., 2004; Fabich et al., 2008; Lippel and Olson, 1968; Tutukina et al., 2016). However, amoxicillin also increases the relative transcript abundance of CAZymes utilizing various polysaccharides that typically serve as energy storage molecules (Nácher-Vázquez et al., 2017; Peshev and Van den Ende, 2014; Pollock and Cairns, 1991; Wilson et al., 2010); for example, 5 (16.1%) CAZymes that increased during treatment act on fructans, glycogen, starch, or dextran. Therefore, we hypothesize that the utilization of such polysaccharides might serve as a tolerance mechanism.

The observed reduction in central carbon metabolism, nucleotide biosynthesis, and peptidoglycan biosynthesis pathways are consistent with our earlier hypothesis and might suggest that the microbial community after amoxicillin treatment is less metabolically active. Additionally, our metatranscriptomic data suggest that amoxicillin treatment drastically alters carbohydrate utilization within the microbiome. To test our hypothesis more directly, we profiled the metabolic capacity of the microbial community after amoxicillin treatment via the Biolog platform (Hayward, CA, USA). This platform consists of a 96-well plate that contains 95 different carbon sources, including carbohydrates (primarily mono-, di-, and oligosaccharides), carboxylic acids, amino acids, and nucleotides. Additionally, each well contains a tetrazolium dye that undergoes a colorimetric shift in response to the oxidation of substrates and can be used as a metric of cellular respiration. Past studies have utilized this platform to characterize the capacity of microbial communities or single isolates to metabolize various substrates (Garland and Mills, 1991; Huang et al., 2018a; Röling et al., 2000; Zhang et al., 2014). To utilize this platform to test the metabolic capacity of the microbiota, we first treated C57BL/6J mice with either amoxicillin or a vehicle control as described previously (See STAR Methods). After treatment, bacteria were isolated from the cecal contents of these mice and were subsequently inoculated at a similar density into Biolog AN Microplates. In addition to the utilization of optical density to normalize the inocula for this assay, we also quantified the bacteria present in each sample by 16S rRNA qPCR and colony-forming unit (CFU) counts (Figures 3G and S2B). It should be noted that both of these techniques have some limitations in quantifying bacterial abundance in microbiome samples because of 16S rRNA copy number variation between bacteria and the inability of some gut taxa to be cultured. Altogether, however, these results demonstrate that the input from each mouse was consistent.

After two days of incubation under anaerobic conditions, we found that the microbiota isolated from the amoxicillin-treated mice displayed a marked decrease in its ability to metabolize carbohydrates (Figures 3F and S2A). Surprisingly, there was no significant defect observed in the ability of the amoxicillin-treated community to metabolize carboxylic acids, amino acids, or nucleotides. Due to the length of incubation, it is unlikely that these results are reflective of a preserved transcriptional state after antibiotic treatment. Rather, these observations are likely driven by the shifts in underlying community composition and suggest that the taxonomic changes elicited by amoxicillin treatment reduce the capacity of the microbiota as a whole to utilize certain carbohydrates.

Amoxicillin Therapy Changes the Expression of Key Metabolic Genes within Individual Members of the Microbiota

Thus far, our data have demonstrated that antibiotic treatment, particularly amoxicillin, is associated with profound reductions in the expression or relative abundance of genes related to core metabolic processes at the whole-community level. Next, we profiled the effect of each antibiotic on gene expression for individual members of the microbiota. To accomplish this, we utilized a previously published pipeline to determine the effect of antibiotic treatment on the gene expression profiles of the ten most abundant bacteria in our metatranscriptomic samples (Deng et al., 2018). From this analysis, we obtained differential expression profiles with each antibiotic for the following bacteria: Ruminococcus albus, Roseburia hominis, Oscillibacter valericigenes, Akkermansia muciniphila, Butyrivibrio proteoclasticus, Clostridium saccharolyticum, Clostridium sp. SY8519, Cutibacterium acnes, Ralstonia pickettii, and B. thetaiotaomicron. For the purposes of this analysis, we focused largely on the amoxicillin-treated samples because of the robust community-level changes we observed earlier. However, the full results of our differential expression analysis for all three antibiotics can be found in Tables S6–S8.

Of the ten bacteria analyzed, we were unable to gain any insights from C. acnes and R. pickettii because of the low number of reads aligning to these species. Of the remaining eight species, we found that most altered the expression of genes that play key roles in carbon metabolism, DNA replication, or cell division (Figures 4A, 4C, 4E, and S3; Tables S6–S8). R. albus increased the expression of an ATP-consuming phosphoenolpyruvate carboxykinase (RUMAL_RS09090), which has been shown to play a role in succinic acid production in a number of different bacteria (Table S6) (Song and Lee, 2006). We detected an increase in expression of the same gene in Clostridium sp. SY8519, which might suggest that this gene plays a larger (but unknown) role in the response of the microbiota to amoxicillin (Table S6). Although these two bacteria increased the expression of central carbon metabolic genes, R. hominis appeared to reduce the expression of several genes that play central roles in carbon and energy metabolism, such as ATP synthase (RHOM_RS06785) and phosphoglucomutase (RHOM_RS02235) (Figures 4A and 4B; Table S6). Additionally, the expression of DNA gyrase subunit A (RHOM_RS00030), an important protein in DNA replication, also decreases (Reece and Maxwell, 1991). Of the genes that increased during amoxicillin treatment in R. hominis, the most notable was 5-dehydro-4-deoxy-D-glucuronate isomerase (RHOM_RS08025), which has been shown to play a role in pectin degradation in other bacteria (Figures 4A and 4B; Table S6) (Condemine and Robert-Baudouy, 1991).

Figure 4. — (A, C, and E) Relative abundance of *R. hominis* (A), *O. valericigenes* (C), and *C. saccharolyticum* (E) as measured by species-level metagenomics analysis. Data are represented as average relative abundance ± SEM for n = 4 (*p < 0.05, Mann-Whitney U test). See also Figure S3.

(B and D) Select genes in *R. hominis* (B) and *O. valericigenes* (D) for which a statistically significant difference in expression was detected during amoxicillin treatment *in vivo* (Benjamini-Hochberg adjusted p value < 0.1). Data are represented as log₂ fold change ± standard error for n = 4. Full results can be found in Table S6.

(F) Volcano plot of the transcriptional response of *C. saccharolyticum* within the murine gut microbiota during amoxicillin treatment. Points in red represent genes for which p < 0.05. Genes of interest to this study are labeled. See also Table S6 for full results.

(G) Percent spore formation in *C. saccharolyticum* cultures treated with increasing concentrations of amoxicillin *in vitro*. Data are represented as average ± SEM for n ≥ 7 (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, unpaired t test).

(H) Scanning electron micrograph (25,000X) of representative amoxicillin-treated (500 ng/mL) *C. saccharolyticum* cultures. Sporulated cells are indicated with a white arrow.

O. valericigenes reduced the expression of several genes relating to DNA replication and cell division, such as MreB-Mrl family cell-shape-determining protein (OBV_RS16450), ATP-dependent RecD-like DNA helicase (OBV_RS16500), and DNA polymerase I (OBV_RS13160), among others (Figures 4C and 4D; Table S6). Together, these results seem to indicate that the population of O. valericigenes that survives amoxicillin therapy might replicate less frequently than untreated controls. A. muciniphila, another major human commensal, appears to alter the expression of a number of genes that play a role in metabolism and DNA replication. In response to amoxicillin, A. muciniphila decreases the expression of a number of genes related to peptidoglycan catabolism (AMUC_RS06460) and DNA helicases (AMUC_RS05760; AMUC_RS00080; AMUC_RS06465) (Table S6). Though we detected relatively few changes in gene expression for B. proteoclasticus, we did observe a reduction in genes that play a role in polysaccharide and arabinose degradation (BPR_RS10720; BPR_RS14265) (Table S6).

In C. saccharolyticum, we observed an increased expression of several genes related to sporulation, such as stage IV sporulation protein A (CLOSA_RS10490) and spore coat protein CotJC (CLOSA_RS14455), despite decreasing in relative abundance (Figures 4E and 4F; Table S6). These observations led us to hypothesize that this bacterium may induce sporulation in response to amoxicillin therapy. To validate this finding, we subsequently treated C. saccharolyticum cultures with varying concentrations of amoxicillin in vitro (Figure 4G). Although C. saccharolyticum was highly susceptible to amoxicillin, we observed the formation of spores after several h of treatment (Figures 4G and 4H). Together, these findings suggest that C. saccharolyticum induces spore formation in response to amoxicillin treatment in vivo as a tolerance mechanism. This finding is particularly interesting in light of our SAMSA2 data, which demonstrated that a number of sporulation-related genes and subsystems decreased at the whole-community level during amoxicillin treatment (Figure 2F). Furthermore, metabolic dormancy has been shown to be a key component of sporulation in related bacteria, suggesting an additional role of metabolism in the response of C. saccharolyticum to amoxicillin (Errington, 2003).

B. thetaiotaomicron Increases the Expression of Polysaccharide Utilization Genes in Response to Amoxicillin Treatment

Of all the species analyzed in this study, we focused most extensively on B. thetaiotaomicron because of its known importance as a human commensal and potential role as a “keystone” taxon within the gut microbiome (Banerjee et al., 2018; Curtis et al., 2014). B. thetaiotaomicron has long been known to play a major role in carbohydrate digestion within the gut, dedicating nearly 20% of its genome to polysaccharide utilization loci (Martens et al., 2011). In total, B. thetaiotaomicron encodes 172 GHs, 163 outer membrane proteins responsible for polysaccharide binding and 11 different enzymes responsible for degrading host-derived carbohydrates such as those found in mucin (Bäckhed et al., 2005; Bjursell et al., 2006; Comstock and Coyne, 2003; Curtis et al., 2014; Wexler, 2007). Of these genes, those found in the sus region have been most extensively profiled and are responsible for starch binding, uptake, and digestion (Foley et al., 2016; Joglekar et al., 2018; Koropatkin et al., 2008; Luis and Martens, 2018; Martens et al., 2008, 2011, Reeves et al., 1996, 1997; Shipman et al., 2000).

Using metagenomics, we found that this bacterium significantly increased in relative abundance during amoxicillin treatment while decreasing in response to both ciprofloxacin and doxycycline (p = 0.0286) (Figure 5A). In general, each of these antibiotics appeared to elicit a unique gene expression profile within B. thetaiotaomicron (Figure 5B). We subsequently sought to analyze the differentially expressed genes under each antibiotic treatment to profile the functional response elicited by these drugs in vivo. During ciprofloxacin treatment, 78 genes were differentially expressed, the large majority of which decreased (Table S7). Of note, we detected an increase in expression of an enzyme responsible for utilization of polysaccharide levan, whereas expression of several genes encoding oxidoreductase electron transport chain proteins or ATP synthases were decreased (Figure 5C; Table S7). In response to doxycycline therapy, we observed broad reductions in expression of 358 genes encoding diverse functions, mirroring our whole-community metatranscriptomic results within B. thetaiotaomicron (Figure 5D; Table S8). Conversely, only seven genes increased in expression, six of which were related to ribosome composition (Figure 5D; Table S8). These broad reductions suggest that B. thetaiotaomicron is less active after ciprofloxacin or doxycycline therapy.

Figure 5. — (A) Relative abundance of *B. thetaiotaomicron* as measured by species-level metagenomics. Data are represented as average relative abundance ± SEM for n = 4 (*p < 0.05, Mann-Whitney U test).

(B) Heatmap of average log₂ fold changes in expression of all *B. thetaiotaomicron* genes during antibiotic treatments. Log₂ fold change was calculated using DESeq2 (version 1.20.0) to compare each antibiotic treatment to its corresponding negative control. Full results and gene identifiers can be found in Tables S6–S8.

(C–E) Select *B. thetaiotaomicron* genes for which a statistically significant difference in expression was detected during ciprofloxacin (C), doxycycline (D), and amoxicillin (E) treatment *in vivo* (Benjamini-Hochberg adjusted p value < 0.1). Data are represented as average log₂ fold change versus control ± standard error for n = 4. Full results can be found in Tables S6–S8.

(F) Confirmation of increased expression of *B. thetaiotaomicron sus* transcripts during amoxicillin treatment *in vivo* by using RT-qPCR. Data are represented as average log₂ fold change (amoxicillin versus control) ± SEM for n = 4.

(G) GSEA of upregulated *B. thetaiotaomicron* genes during amoxicillin treatment *in vivo*. Dashed line represents an adjusted p value of 0.1 and denotes statistical significance.

Of the three antibiotics tested, amoxicillin was the only treatment during which the relative abundance of B. thetaiotaomicron increased compared with controls (Figure 5A). For this reason, we were particularly interested in the transcriptional response of this bacterium to amoxicillin to gain insights into how it survived antibiotic treatment and to identify potential tolerance mechanisms. In general, the transcriptional profile of B. thetaiotaomicron in amoxicillin samples appears indicative of a tolerance response to antibiotic therapy (Figure 5E; Table S6). We found that B. thetaiotaomicron increased the expression of several known antioxidant enzymes such as superoxide reductase and thioredoxin, which have been shown to play a role in bacterial stress responses (Dwyer et al., 2007; Serrano et al., 2007; Touati, 2000; Zeller and Klug, 2006). Additionally, we observed an increase in the expression of a metallo-beta-lactamase, which likely contributes to the survival of B. thetaiotaomicron during amoxicillin treatment (Figure 5E; Table S6).

Interestingly, we also detected signatures of a metabolic response to amoxicillin. At the single-gene level, we observed numerous changes relating to carbon metabolism. Notably, we found that B. thetaiotaomicron decreased the expression of a putative hexose phosphate transport protein in response to amoxicillin (Figure 5E; Table S6). Additionally, we found increased amounts of several genes involved in central carbon metabolism, such as malate dehydrogenase and 6-phospho- fructokinase (Figure 5E; Table S6). Most notably, we observed that B. thetaiotaomicron increased the expression of a number of genes responsible for polysaccharide utilization, namely those from the sus operon. Similarly, we detected increased expression of a number of enzymes that play a role in carbohydrate metabolism such as pectate lyase (BT_2254), alpha-glucosidases (BT_3703 and BT_3294), alpha-xylosidase (BT_3169), fucR (BT_1272), and arabinosidase (BT_3655), among others. We confirmed the increased transcript abundance of several sus genes within our samples by using RT-qPCR, showing that these genes are highly expressed during amoxicillin therapy (Figure 5F). Lastly, we performed gene set enrichment analysis (GSEA) on the up- and downregulated B. thetaiotaomicron genes during amoxicillin treatment. No pathways were significantly enriched in the downregulated gene set; however, we found the genes that were upregulated during amoxicillin treatment were significantly enriched for a number of metabolic processes, such as amino acid and secondary metabolite biosynthesis (Figure 5G). We found that the most enriched gene set in our data were the biosynthesis of antibiotics (Figure 5G); although the exact mechanism is unclear, it has been shown that increased expression of this pathway could be reflective of a stress response or altered nutrient availability (Esnault et al., 2017; Martín, 2004). Additionally, several other notable pathways, such as carbon metabolism, pentose phosphate pathway, and glycolysis and/or gluconeogenesis, also approached significance but fell just above the false discovery rate (FDR) cut-off. Together, these findings might be indicative of increased polysaccharide utilization and a reduced reliance on simple sugars. We hypothesize that increased polysaccharide fermentation might serve as a tolerance mechanism in B. thetaiotaomicron during antibiotic therapy. If true, these findings could have major implications regarding the effect of diet and nutrient composition on the response of members of the gut microbiota to antibiotics.

Polysaccharide Utilization Decreases Susceptibility of B. thetaiotaomicron In vitro

On the basis of our metatranscriptomics data, we hypothesized that nutrient availability and utilization, particularly with regard to carbohydrates, play a role in B. thetaiotaomicron sensitivity to amoxicillin. Previously published data indicate that B. thetaiotaomicron is somewhat sensitive to beta-lactam class antibiotics in vitro (Fernández-Canigia et al., 2012; Fox and Phillips, 1987). However, our results here indicate that B. thetaiotaomicron is comparatively insensitive to amoxicillin in vivo. To explain this surprising contradiction, we determined the minimal inhibitory concentration (MIC) of each of the three antibiotics in a number of different media conditions with varying nutrient compositions under anaerobic conditions. In both brain heart infusion (BHI) and modified gifu anaerobic medium (mGAM) media, B. thetaiotaomicron (VPI-5482) was least susceptible to amoxicillin and was comparatively more sensitive to ciprofloxacin and doxycycline (Figures 6A and 6B). Interestingly, we found that all three antibiotics tested had different MICs in BHI compared with mGAM (Figures 6A and 6B). However, because neither media is defined, it was unclear which nutrients were responsible for the observed discrepancy in sensitivity. To investigate the effect of carbohydrate utilization on amoxicillin sensitivity in B. thetaiotaomicron, we determined the MIC in a defined minimal media (Martens et al., 2008) supplemented with either glucose or one of four polysaccharides (dextrin, levan, pectin, or pullulan) as the sole carbon source at a final concentration of 0.5% (w/v). We found that growth on any of the four polysaccharides resulted in an approximately four-fold increase in the MIC of amoxicillin compared with growth on glucose (Figure 6C). Previous studies have demonstrated that beta-lactam efficacy and growth rate are tightly linked; increased growth rates resulted in increased lysis (Lee et al., 2018). To determine whether the observed tolerance was due to a reduced growth on polysaccharides, we subsequently quantified the growth rate of B. thetaiotaomicron in each minimal media formulation in the absence of amoxicillin. We found that the doubling time of B. thetaiotaomicron was slightly faster on polysaccharides compared with glucose, thus making the observed tolerance to amoxicillin more striking (Koropatkin et al., 2008; Martens et al., 2011; Rogers et al., 2013). Thus, this finding demonstrates that the observed tolerance to amoxicillin in the presence of polysaccharides was not attributable to a reduced growth rate (Figure 6C). We also obtained similar results for growth rate and amoxicillin sensitivity by using B. thetaiotaomicron isolates obtained from the murine cecum (Figures S1D and S1E). Thus, it is likely that the mechanism of amoxicillin tolerance during polysaccharide growth is conserved among B. thetaiotaomicron strains of disparate origins.

Furthermore, we found that polysaccharide utilization actually sensitizes B. thetaiotaomicron to ciprofloxacin (Figure 6D) and has an even greater negative effect on its tolerance of doxycy- cline (Figure 6E). This finding suggests that the protective effect of polysaccharide utilization is not a general antibiotic tolerance mechanism, but rather is specific to amoxicillin. If polysaccharide utilization decreased amoxicillin susceptibility in relation to glucose, we hypothesized that increased concentrations of glucose might further sensitize B. thetaiotaomicron to this antibiotic. Consistent with this, we found that increasing concentrations of glucose promoted amoxicillin toxicity (Figure 6F). Additionally, we obtained similar, although much less pronounced, results by using a closely related species, B. fragilis, finding that growth on dextrin provided protection against amoxicillin while increasing concentrations of glucose further sensitized it to this drug (Figure 6G). These findings suggest that the utilization of polysaccharides leads to tolerance to amoxicillin in members of the Bacteroides genus. Furthermore, as the majority of microbiota-available carbohydrates are supplied by the host diet, these results suggest that dietary composition could play a role in modulating the response of the microbiota to antibiotics.

Host Diet Modulates the Response of the Microbiome to Amoxicillin in Mice

Our in vitro data indicate that the susceptibility of B. thetaiotaomicron to amoxicillin varies considerably depending on whether it utilizes glucose or a polysaccharide as its primary carbon source. However, these nutrient conditions likely differ considerably from those found in vivo, where bacteria encounter complex mixtures of carbohydrates of both host and dietary origin. Standard rodent diets typically contain large quantities of fiber and amounts of mono-, di-, and polysaccharides that vary depending on formulation (Dalton, 1963). In vitro, we found that increasing concentrations of glucose when added to minimal media containing dextrin-sensitized B. thetaiotaomicron to amoxicillin, supporting the possibility that there might be competing effects on antibiotic sensitivity in complex nutrient environments (Figure 7A).

Figure 7. — (A) MIC assessment of *B. thetaiotaomicron* with amoxicillin in minimal media containing varying concentrations of glucose and dextrin. Data are represented as the average percent growth compared with untreated control cultures ± SEM.

(B) Quantification of glucose concentrations within the cecum of mice after antibiotic treatment and/or dietary modification. Data are represented as average concentrations of cecal glucose ± SEM. Statistical significance was determined using an unpaired t test with Welch’s correction (**p < 0.01, n = 5).

(C) Experimental design utilized to test the effect of dietary glucose supplementation on the response of the murine gut microbiome to amoxicillin treatment. Figure was created using BioRender.

(D) Absolute quantification of total bacteria loads within the murine cecum after dietary modification and/or amoxicillin treatment. Data are represented as average log₁₀ 16S rRNA gene copies per gram of cecal content ± SEM. Statistical significance was determined using an unpaired t test with Welch’s correction (**p < 0.01, n = 5).

(E) Absolute quantification of *B. thetaiotaomicron* within the murine cecum after dietary modification and/or amoxicillin treatment. Data are represented as average log₁₀ 16S rRNA gene copies per gram of cecal content ± SEM. Statistical significance was determined using an unpaired t test with Welch’s correction (*p < 0.05, n = 5).

(F) Quantification of glucose concentrations within the cecum of mice after dietary modification and/or antibiotic treatment. Data are represented as average concentration of cecal glucose ± SEM. Statistical significance was determined using an unpaired t test with Welch’s correction (*p < 0.05, **p < 0.01, n = 4–8 per group).

On the basis of the composition of our rodent diet, we hypothesized that B. thetaiotaomicron experiences a protective environment in the gut microbiome, with high amounts of fiber and low amounts of mono- and disaccharides. To elucidate the metabolic environment within the gut, we subsequently quantified the concentration of glucose present in the murine cecum after treatment with either an antibiotic or vehicle control. Interestingly, we observed that amoxicillin treatment robustly and significantly reduced cecal glucose concentrations in relation to control (Figure 7B). However, we observed no reduction in cecal glucose concentration after ciprofloxacin therapy, suggesting that a reduction in cecal glucose levels was not a generalized response to antibiotics (Figure 7B). Although considerable work needs to be done to fully elucidate the mechanism underlying these changes in cecal glucose levels, it is possible that the divergent effects on cecal glucose concentration might result from the different microbiome compositions induced by amoxicillin and ciprofloxacin (Figure 1B). Regardless, the observed collapse in cecal glucose amounts could partially explain the detected expansion of B. thetaiotaomicron during amoxicillin therapy in vivo. Because B. thetaiotaomicron is sensitized to amoxicillin with increasing concentrations of glucose (Figure 6F), it is possible that the reduced glucose concentration in the cecum might provide a metabolic environment that promotes tolerance of this bacterium to amoxicillin.

In combination with our earlier in vitro results, these findings demonstrate that glucose availability plays an important role in the response of the microbiome to amoxicillin. Thus, we hypothesized that adding additional glucose to the murine diet would change the response of the total microbiome, and B. thetaiotaomicron, to amoxicillin. To test this, we performed an experiment in which mice were fed a standard rodent diet (Laboratory Rodent Diet 5001, LabDiet, St. Louis, MO, USA) alone or supplemented with 30% (w/w) glucose for 24 h and then treated with either amoxicillin or vehicle control. Microbiome composition was subsequently analyzed via metagenomics, and universal bacterial- and B.-thetaiotaomicron-specific qPCR (Figure 7C). After amoxicillin treatment, we observed a trend toward increased cecal glucose amounts in the mice supplemented with glucose (p = 0.09) (Figure 7B). However, we did not observe a change in cecal glucose amounts in mice receiving vehicle controls (Figure 7B). Overall, compared with normal chow, glucose supplementation had a detrimental effect on the community, reducing total bacterial loads of both vehicle- and amoxicillin-treated mice (Figure 7D). Furthermore, although amoxicillin induced a significant increase in the abundance of B. thetaiotaomicron in the normal chow, we did not observe a significant expansion in the normal chow + 30% glucose diet, although the baseline level of this bacterium was elevated in the absence of antibiotic (Figure 7E).

On the basis of these results, it is apparent that the metabolic environment within the gut plays an important role in the response of the microbiome to amoxicillin therapy, and that altering host diet can modulate this environment. However, the normal chow used in these experiments is not fully defined in composition and consists of natural ingredients such as corn and alfalfa. To further test the effect of host diet on antibiotic responses, we repeated our dietary modulation experiments by feeding mice a purified, fully defined diet (Envigo Teklad, TD.180901) either alone or supplemented with 30% glucose or dextrin (Figure S4A). Unlike the normal chow, which is high in host-indigestible fiber, the purified diet contains large quantities of carbohydrates that are host digestible. Overall, we found that mice on the purified diets had an increased relative abundance of Verrucomicrobia and reduced amounts of Bacteroidetes compared with mice on the normal chow (Figure S4B). Interestingly, dietary composition appeared to have a major effect on the community composition after antibiotic treatment. Bacteroidetes expanded in relative abundance during amoxicillin treatment in all conditions; however, the extent of this expansion differed considerably depending on the diet. Specifically, the amoxicillin-induced expansion was significantly lower on the purified diets compared with in the normal chow (p = 0.016) (Figure S4B). Mice on the purified diet displayed significantly higher levels of alpha diversity after amoxicillin treatment than did mice on normal chow (Figure S4C), possibly resulting from a reduced abundance of Bacteroidetes and Proteobacteria (p = 0.0396) and increased abundance of Verrucomicrobia (p = 0.004) and Firmicutes (p < 0.0001) (Figure S4B). In addition, we found that dietary composition affected amoxicillin-induced changes in beta diversity. Though amoxicillin induced a shift in community structure in both diets, we found that average Bray-Curtis dissimilarity between amoxicillin and control samples was significantly higher on the normal chow than the purified diet (Figures S4D and S4E). This suggests that the extent of microbiome disruption during amoxicillin treatment is modulated by diet composition.

Although we detected significant differences in the response of the microbiome to amoxicillin between the base normal and purified diets, we did not observe significant differences in community composition between purified diet mice with glucose or dextrin supplementation (Figure S4B). Unlike in the normal chow, we found that glucose supplementation did not elevate cecal glucose amounts when added to the purified diet (Figure 7F). It is known that dietary fiber intake reduces the absorption of glucose in the gut by the host, which in turn would increase its availability to the microbiota (Schwartz and Levine, 1980). Thus, we hypothesize that the absence of host-indigestible fiber in the purified diet resulted in increased absorption of sugar in the upper digestive tract and reduced glucose levels in the cecum. We did observe statistically significant reductions in cecal glucose concentrations in mice receiving amoxicillin on each of the three purified diet formulations (Figure 7F). In fact, these reductions were more robust than those observed on the normal chow (Figure 7B).

Altogether, these data clearly demonstrate that dietary composition has a major effect on the response of the microbiome to antibiotics. However, due to the extensive differences in composition between the normal and purified diet, it is hard to definitively conclude which nutrients are responsible for the observed changes (Table S9). Although considerable work is needed to fully elucidate how host dietary composition affects the response of the microbiota to antibiotic treatment, these initial results illustrate a currently unappreciated role for host diet in the response of the microbiota to antibiotic therapy. Furthermore, it demonstrates that the choice of diet should be an important consideration in the design and interpretation of microbiome studies, particularly those focused on antibiotics.

DISCUSSION

Understanding how and why bacteria within the microbiome die during antibiotic treatment is essential to reducing the risk of dysbiosis during therapy. To address this knowledge gap, we utilized a combined metagenomic and metatranscriptomic approach to profile taxonomic shifts during antibiotic therapy and then link them to transcriptional responses. We found that each antibiotic has diverging effects on the composition of the murine gut microbiome: amoxicillin enriched for Bacteroidetes species, whereas both ciprofloxacin and doxycycline increased the proportion of Firmicutes. Using metatranscriptomics, we detected that each of the antibiotics elicited profound changes in transcript abundance, including a reduction in many metabolic genes and pathways. Most notably, amoxicillin reduced the relative abundance of central carbon metabolism transcripts and induced the production of CAZymes that utilize polysaccharides as their primary substrates. One species in particular, B. thetaiotaomicron, increased the expression of polysaccharide utilization genes in response to amoxicillin and was able to survive this treatment in vivo. In vitro, we found that although B. thetaiotaomicron is sensitive to amoxicillin in the presence of glucose, it is far more tolerant when supplemented with polysaccharides. Furthermore, we found that amoxicillin induced a collapse in glucose availability within the murine cecum in vivo. As glucose concentration appears to be a major determinant of amoxicillin susceptibility in B. thetaiotaomicron in vitro, reduced cecal glucose amounts might provide a metabolic environment that promotes tolerance and enables the bloom of this bacterium in vivo. Furthermore, this reduction in glucose might contribute to many of the transcriptional changes observed. We also found that dietary supplementation with glucose affected amoxicillin-induced microbiome perturbation with normal chow. Together, our results demonstrate that microbial metabolism and host diet play a role in the response of the microbiota to antibiotic treatment.

Metagenomics and Metatranscriptomics—Advantages and Limitations

The current understanding of antibiotic efficacy and spectrum is shaped by in vitro assays that are largely unreflective of the complex ecosystem that bacteria experience within the gut. Thus, shotgun metagenomics provides an unparalleled ability to obtain species-level resolution of microbiome composition and provides a unique opportunity to study antibiotic spectrum within the gut microbiome. However, it should be noted that these taxonomic shifts are representative of compositional changes within this community and might not be reflective of changes in absolute abundance. In general, our species-level metagenomics are in strong agreement with the expected spectrum of activity for each antibiotic. Amoxicillin, which typically has good activity against Gram-positive bacteria, drastically reduced the relative abundance of Gram-positive Firmicutes (Akhavan and Vijhani, 2019). Conversely, doxycycline and ciprofloxacin, which have broad activity against both Gram-negative and Gram-positive bacteria, reduced the relative abundance of Bacteroidetes, though both drugs had less dramatic effects on the composition than amoxicillin (Chopra and Roberts, 2001; LeBel, 1988). We hypothesize that a number of different factors could explain this observed difference. For example, environmental variables such as iron availability might reduce the efficacy or bioavailability of both ciprofloxacin and doxycycline because of their known chelating properties (Campbell and Has-inoff, 1991; Grenier et al., 2000). Additionally, dosage and route of delivery might affect the length and concentration of antibiotic exposure to bacteria within the gut. Although future studies might be able to elucidate the effect of such factors, this work demonstrates the robust potential of shotgun metagenomics to explore antibiotic spectrum within the gut.

Here, we present a high-depth metatranscriptomic analysis of the response of the gut microbiota to antibiotic therapy in vivo. The application of this method has enabled the discovery of previously unknown antibiotic response mechanisms among individual members of the microbiota. Although traditional 16S rRNA amplicon and metagenomic sequencing yields information regarding community composition and gene content, metatranscriptomics provides novel insights into the functional response of the microbiota to perturbation (Gosalbes et al., 2012; Thomas et al., 2012). The combination of species-level metatranscriptomics with metagenomics provides a robust platform that can link transcriptional responses to susceptibility and/or growth in the microbiome. Combining these techniques with in vitro validation and in vivo dietary supplementation experiments creates an integrated and comprehensive approach to identify factors that shape antibiotic responses within the gut microbiome.

Despite the benefits of this technique, it does have several key limitations. Metatranscriptomic analyses require tremendous sequencing depth of at least 50 million paired-end reads for accurate transcriptional profiling of stool samples, making such studies costly and computationally intensive (Westreich et al., 2016). Additionally, the effectiveness of metatranscriptomics is inherently limited by the databases and computational tools utilized for mapping (Pasolli et al., 2019). It has been estimated that nearly 50% of genes within the human gut microbiome lack proper functional annotation (Human Microbiome Project Consortium, 2012). As a result, many metatranscriptomic reads might map to genes that have not been functionally annotated, be misassigned to closely related but better annotated species, or fail to map to a particular database entirely. However, as the cost of sequencing continues to decline and additional bacterial genomes are annotated, metatranscriptomic analyses will become more cost-effective and informative. Fortunately, many of the most abundant species within the microbiome of our mice have been fully sequenced and annotated.

Similarly, the interpretation of metatranscriptomic data were complicated by the limitations of the computational tools utilized for analysis. In this study, we employed two commonly used tools, SAMSA2 and HUMAnN2, in parallel to profile the transcriptional response of the gut microbiome to antibiotics. Although both SAMSA2 and HUMAnN2 can identify gene families and pathways that shift in response to a perturbation, they have several important differences that affect their subsequent interpretation. Most notably, HUMAnN2 facilitates the normalization of metatranscriptomic data with corresponding metagenomic reads. Thus, it enables the identification of genes or pathways that change in abundance in relation to the underlying community structure. Conversely, SAMSA2 does not normalize for community structure when identifying differentially abundant features. Thus, although this tool is useful for identifying changes in transcript abundance between treatments, it is possible that many of these shifts are driven by changes in the composition of the underlying community in response to antibiotic treatment. Because of the differences between individual tools, it is advantageous to utilize several in parallel for a comprehensive analysis of metatranscriptomic data.

Another challenge inherent to all microbiome analyses (and particularly metatranscriptomics) is determining the biological significance of results. Though we observed robust transcriptional changes in response to each antibiotic treatment, we cannot definitively conclude that shifts in gene expression result in altered metabolic activity without additional supporting data (such as metabolomics and proteomics). Additionally, it is widely known that members of the microbiota form complex ecological relationships (Faust et al., 2012; Rowan-Nash et al., 2019). Because of the highly interconnected nature of these communities, it is plausible that some of the changes observed in this study could be the result of secondary, rather than direct, effects of antibiotics. For example, the observed increase in expression of sus genes within B. thetaiotaomicron could be a response to altered nutrient availability within the gut. We observed that the concentration of glucose within the murine cecum was significantly reduced during treatment with amoxicillin, but not ciprofloxacin. Thus, it is possible that some of the changes in gene expression could be reflective of altered carbon source utilization elicited by changes in the metabolic environment within the gut after amoxicillin treatment. However, our subsequent in vitro and in vivo experiments demonstrate that utilization of glucose increases sensitivity to amoxicillin, supporting the interpretation that polysaccharide utilization might be a component of a tolerance mechanism to beta-lactam-induced stress. Alternatively, it is possible that the increased expression of polysaccharide utilization genes might be a component of a larger stress response rather than a stand-alone tolerance mechanism. For example, B. thetaiotaomicron is known to upregulate genes involved in glycan degradation during the stringent response, which has been previously shown to play a role during antibiotic stress and might result from carbon source limitation (Aedo and Tomasz, 2016; Belenky and Collins, 2011; Cabral et al., 2018; Gaca et al., 2013; Schofield et al., 2018).

Our findings suggest that nutrient availability and bacterial metabolism play important roles in shaping the response of the gut microbiota to antibiotic treatment. However, the contribution of other factors should not be overlooked. In particular, the expression of antibiotic resistance genes likely plays a major role in shaping the community dynamics during antibiotic treatsment. It is known that clinical isolates of B. thetaiotaomicron and other members of the Bacteroides genus have been found to have varying levels of beta-lactamase activity that confer resistance to this class of antibiotics (Fang et al., 2002; Nguyen et al., 2000; Sadarangani et al., 2015; Ulger Toprak et al., 2004). Furthermore, the production of beta-lactamases by clinical isolates of Bacteroides has been demonstrated to provide protection to other members of the microbiome (Stiefel et al., 2015). Our single-species transcriptomic analysis indicated that B. thetaiotaomicron induced the expression of a metallo-beta-lactamase gene in response to amoxicillin. Thus, it is likely that beta-lactamase expression plays some role in the observed expansion of B. thetaiotaomicron to amoxicillin treatment. However, it is currently unclear what the relative contribution of elevated metallo-beta-lactamase expression or altered metabolism is to the expansion of this bacterium during treatment. Further complicating the relationship between the beta-lactamase expression and resistance to amoxicillin in vivo is the possibility that increased expression of such genes over endogenous levels might not be necessary or sufficient to confer resistance, particularly because metallo-beta-lactamases require metal ions for activity that might be present at variable concentrations within the gut (Palzkill, 2013). Though we did not detect the overexpression of resistance genes in other bacteria within our single-species-level analysis, it is highly likely that such genes play an important role in community dynamics during treatment.

Host Diet and Bacterial Metabolism

By understanding the effect of microbial metabolism on antibiotic responses within the gut microbiome, it might be possible to develop strategies to protect commensal bacteria during treatment. One such strategy that has potentially major clinical implications could be the modulation of diet during treatment. It is widely known that host diet and antibiotic usage are two of the most important factors that shape microbiota composition, often inducing rapid and profound changes (Behr et al., 2018; Bokulich et al., 2016; Carmody et al., 2015; Dethlefsen and Relman, 2011; Gomez-Arango et al., 2017; Kisuse et al., 2018; Nakayama et al., 2017; Turnbaugh et al., 2008, 2009; Zaura et al., 2015). However, little work has been done to explore the relationship between host diet and bacterial responses to antibiotics within the microbiota. In vitro, we found that carbohydrate utilization played a major role in amoxicillin sensitivity in B. thetaiotaomicron, with added glucose increasing susceptibility to this antibiotic. This effect of added glucose could have public health implications regarding the role of diet composition in antibiotic responses within the gut microbiome. In the United States, it is estimated that the average adult obtains nearly 17% of their daily caloric intake from added sugars, such as high-fructose corn syrup (Powell et al., 2016). We found that modulating the diet with added glucose for as little as a day was sufficient to negatively affect the microbiota after amoxicillin treatment, resulting in decreased amounts of Bacteroidetes and increased amounts of Proteobacteria. In humans, such changes are considered to be hallmark signatures of microbial dysbiosis within the gut and can lead to numerous long-term side effects such as inflammatory bowel disease, obesity, and diabetes mellitus (DeGruttola et al., 2016; Shin et al., 2015). Therefore, even a relatively minor and short-term dietary shift might be sufficient to modulate the response of the gut microbiota to antibiotics. At this time, we cannot fully elucidate or predict the impact of a particular supplement on antibiotic-induced microbiome disruption. However, in the future it might be possible to design dietary interventions to reduce the risk of dysbiosis during antibiotic therapy.

Despite these promising preliminary findings, it should be noted that a number of factors limit their immediate clinical relevance to humans. In addition to the widely known physiological and immunological differences between humans and mice that might affect microbial community dynamics, typical murine and human diets are highly dissimilar in composition (Nguyen et al., 2015). Specifically, standard rodent diets are typically rich in fiber with little added sugar, whereas the diets of Westernized societies are relatively low in fiber and high in added sugar and fat (Martinez et al., 2017; Turnbaugh et al., 2008, 2009). Thus, although our in vitro and in vivo results demonstrate the potential of diet modulation as a potent regulator of antibiotic responses within the microbiome, the response in humans might be broader and harder to predict. Additionally, although our results demonstrate a broad role for diet in the response of the microbiome to antibiotic therapy, it is currently unclear which nutrients are responsible for the observed changes due to the differences in composition between the two dietary formulations. Thus, future studies will be needed to test the effect of the addition or subtraction of specific nutrients from the diet on microbiome community dynamics. Lastly, studies involving dietary modifications are inherently confounded by their potential effects on host metabolism. Thus, it is often difficult to disentangle the direct effects of dietary modulation on the microbiota from those induced indirectly by changes in host physiology.

Although our study examines the acute effects of antibiotic treatment and dietary modulation on microbiota composition, it does not investigate the long-term effects that these interventions have on this community. In particular, it is unclear how quickly the community composition and function recover after cessation of antibiotic treatment. Thus, future work is needed to determine the long-term effects of antibiotics on community dynamics. Furthermore, dietary intervention and antibiotic treatment might have potentially detrimental effects on the host. Previous studies have demonstrated that B. thetaiotaomicron can enhance the expression of virulence genes within enterohemorrhagic E. coli (EHEC) during enteric infection (Curtis et al., 2014). Furthermore, EHEC colonization and virulence is known to be responsive to sugar concentrations via the Cra transcription factor (Curtis et al., 2014; Njoroge et al., 2012a, 2012b; Pacheco et al., 2012). Therefore, it is possible that the expansion of B. thetaiotaomicron during amoxicillin treatment (and its modulation via dietary sugar supplementation) could exacerbate infection with enteric pathogens. Due to the low abundance of E. coli in our dataset, we could not detect any changes in the expression of the Cra transcription factor during antibiotic treatment. However, we found that each antibiotic induced the expression of a number of virulence genes within the gut microbiome (Table S5). For example, amoxicillin treatment increased the expression of superoxide dismutase (SOD) within the microbiome, which has been shown to play a role in the virulence of known enteric pathogens such as Salmonella typhimurium (Broxton and Culotta, 2016; De Groote et al., 1997; Poyart et al., 2001; Roggenkamp et al., 1997). Additionally, we found that hemolysin, a virulence factor shown to play a role in the pathogenicity of several organisms, was increased in expression within B. thetaiotaomicron and the microbiome as a whole after amoxicillin treatment (Arthur et al., 1989; Lobo et al., 2013; Robertson et al., 2006; Vergis et al., 2002). Thus, it is possible that some of the acute changes observed in this study could have negative long-term effects on the host, particularly with respect to enteric infection susceptibility and severity. Therefore, additional work is needed to elucidate the effect that antibiotic- and diet-induced changes to both microbiome community structure and nutrient availability have on the host.

Limitations of study

This study has intrinsic limitations related to methodology, the complexity of diet, and the interaction between microbes and their host. Although our metagenomic and metatranscriptomic pipelines enable compositional and transcriptional profiling of microbial communities, they are dependent on the currently available databases and can only measure relative abundance rather than provide an absolute quantification. In addition, it is not possible to definitively conclude that changes in gene expression translate to altered enzymatic or metabolic activity. Our findings demonstrate a broad role for host diet in shaping microbial community dynamics during antibiotic treatment. However, it is difficult to ascertain whether the observed changes in community dynamics during amoxicillin treatment are the result of a particular nutrient because of the complex relationship between host and inter-microbial metabolism. Thus, the observed changes could be the direct result of nutrient availability, or indirect changes reflective of host physiology or altered microbiome composition. Lastly, though we have demonstrated that antibiotics induce changes in both microbiome composition and the metabolic environment within the gut, the relationship between these two factors has not been fully elucidated. Specifically, it is unclear whether these changes occur simultaneously or whether changes in microbiome composition result in metabolic shifts that ultimately favor an expansion of B. thetaiotaomicron. Therefore, additional work is needed to fully characterize the relationship between microbiome composition and the local metabolic environment within the gut.

STAR★METHODS

LEAD CONTACT AND MATERIALS AVAILABILITY

The only new, unique reagents generated in this study are murine-derived isolates of B. thetaiotaomicron DC20 and DC21. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Peter Belenky (peter_belenky@brown.edu).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

Four-week-old female C57BL/6J mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA) and were subsequently allowed to acclimate to the Brown University mouse facility for two weeks before handling. All mice were housed in a temperature-controlled (21±1.1 °C), specific-pathogen free (SPF) facility on a 12 h light/dark cycle. All mice were fed a standard chow (Laboratory Rodent Diet 5001, LabDiet, St. Louis, MO, USA) unless otherwise noted and were cohoused throughout all experiments. However, each experimental condition contains mice originating from at least two different cages to account for possible cage effects. All experimental procedures involving mice were approved by the Institutional Animal Care and Use Committee of Brown University.

Bacterial Strains

B thetaiotaomicron (VPI-5482) and B. fragilis (VPI-2553) were purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). All strains of Bacteroides were cultured in modified Gifu Anaerobic Media (mGAM, HyServe), brain-heart infusion broth (BHI, Remel, San Diego, CA, USA), or Bacteroides minimal media supplemented with 1 mg/L resazurin (Martens et al., 2008). C. saccharolyticum WM1 (DSM-2544) was purchased from the Leibniz Institute – DSMZ (Braunschweig, Germany) and was routinely grown in BHI supplemented with 0.5 mg/L hemin and 2 mg/L β-NAD (BHI++) (Tramontano et al., 2018). All bacteria were grown at 37°C in a BactronEZ anaerobic chamber (Sheldon Manufacturing, Cornelius, OR, USA) under an atmosphere of 5% H₂, 5% N₂, and 90% CO₂.

METHOD DETAILS

Animal Experiments

To generate samples for microbiome analysis, six-week old female C57BL/6J mice fed standard mouse chow were treated with amoxicillin (0.1667 mg/mL), ciprofloxacin (0.0833 mg/mL) or doxycycline hydrochloride (0.067 mg/mL) ad libitum in drinking water. At the time of treatment, mice weighed an average of 18.3 ± 0.94 g. Using the estimate that a healthy mouse drinks 150 mL/kg of water per day, these concentrations were selected to administer a dosage of 25 (amoxicillin), 12.5 (ciprofloxacin), or 10 (doxycycline) mg/kg/day (Yang et al., 2017). Dosages for each antibiotic were based on those used in previous studies with mice (Abgueguen et al., 2007; Marx et al., 2014; Yang et al., 2017). Amoxicillin was administered for 12 h, while ciprofloxacin and doxycycline were administered for 24 h. Controls received water that was pH-matched to the corresponding antibiotic water. All drinking water was filter-sterilized prior to administration. After the completion of treatment, mice were sacrificed and dissected to isolate the cecum. Cecal contents were extracted and transferred immediately into a DNA/RNA Shield Collection and Lysis Tube from Zymo Research (Irvine, CA, USA) and stored on ice until samples could be transferred to −80°C for permanent storage.

For the glucose supplementation experiment, six-week old female C57BL/6J mice were fed standard mouse chow (“Normal Chow,” Laboratory Rodent Diet 5001, LabDiet, St. Louis, MO, USA) alone or supplemented with 30% (w/w) glucose for 24 h before antibiotic treatment. For purified diet experiments, six-week old female C57BL/6J mice were fed a basal purified diet (TD.180901) from Envigo Teklad (Madison, WI, USA) for one week. A comparison of the macronutrient composition of the purified and normal chow can be found in Table S9. Following this acclimation period, diets were subsequently supplemented with 30% (w/w) glucose or dextrin for 24 h prior to treatment. Mice were then treated with amoxicillin for 12 h and sacrificed as described above.

Nucleic Acid Extraction and Quantification

Total DNA and RNA were simultaneously extracted from cecal contents using the ZymoBIOMICS DNA/RNA Miniprep Kit from Zymo Research (Irvine, CA, USA) following the manufacturer’s instructions. DNA and RNA were eluted in nuclease-free water and stored at −80°C until further use. Total DNA and RNA were quantified using the dsDNA-BR and RNA-HS kits on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, U.S.).

Metagenomic and Metatranscriptomic Library Preparation

Metagenomic libraries for the glucose supplementation experiment were prepared from total DNA using the NEBNext® Ultra II FS DNA Library Prep Kit for Illumina from New England Biolabs (Ipswich, MA, USA). All other metagenomic libraries were prepared using the Ovation® Ultralow System V2 kit from NuGEN (San Carlos, CA, USA). Libraries made using the Ultra II FS DNA Library Prep Kit were prepared according to the manufacturer’s instructions to obtain fragments between 200–450 bp. For libraries prepared using the Ovation® Ultralow System V2 kit, DNA was first sheared to a median fragment size of 300 bp using a Covaris S220 High Performance Ultrasonicator (Woburn, MA, USA) using the manufacturer’s recommended settings. Fragmented DNA was subsequently prepared into sequencing libraries according to instructions provided by NuGEN. Prior to library preparation, total RNA was treated with rDNase I and subsequently depleted of host mRNA and bacterial ribosomal RNA using the MICROBEnrich and MICROBExpress kits from Invitrogen (Carlsbad, CA, USA). RNA (100 ng) was subsequently used to prepare sequencing libraries using the Ovation® Complete Prokaryotic RNA-Seq Library System from NuGEN according to the manufacturer’s instructions. This kit utilizes a proprietary technology known as AnyDeplete to further deplete host mRNA and bacterial ribosomal RNAs. Additionally, a trial experiment suggested that a significant portion of reads in the murine gut metatranscriptome were derived from murine osteosarcoma virus, a retrovirus known to infect mice (Finkel et al., 1966). To address this, we requested custom AnyDeplete probes designed to specifically remove fragments originating from murine osteosarcoma virus. The sequences of these probes can be found in Table S10. Following library preparation, metagenomic, and metatranscriptomic libraries were sequenced on an Illumina HiSeqX using paired-end, 150 bp reads. We obtained an average of 26,113,145 (±11,436,616) raw reads per metagenomic sample and 85,599,941 (±11,674,614) raw reads per metatranscriptomic sample.

Preprocessing of Raw Sequencing Reads

Preprocessing of all sequencing reads was done using the kneadData wrapper script (McIver et al., 2018). Using this program, reads were first trimmed using Trimmomatic (version 0.36) using a SLIDINGWINDOW value of 4:20 (Bolger et al., 2014). Surviving reads were subsequently filtered of contaminating sequences from the C57BL/6NJ mouse genome and two murine retroviruses found in our dataset (murine osteosarcoma virus and murine mammary tumor virus) using Bowtie2 (Langmead and Salzberg, 2012). Metatranscriptomic reads were processed in a similar fashion but were also depleted of bacterial ribosomal reads using the SILVA 128 database containing LSU and SSU ribosomal RNA sequences (Pruesse et al., 2007).

Analysis of Metagenomic Reads

Cleaned metagenomic reads were classified against a database containing all prokaryotic genomes downloaded from NCBI RefSeq using Kraken2 (version 2.0.7-beta) with default parameters and a k-mer length of 35 (Wood and Salzberg, 2014). Because Kraken2 utilizes the lowest common ancestor (LCA) algorithm to assign taxonomy to sequences, reads are often not fully assigned down to the species-level. Therefore, species-level abundances were estimated from the preliminary Kraken2 output with Bracken (version 2.0.0) (Lu et al., 2017). The resulting output was subsequently imported into R (version 3.5.0) and analyzed with the phyloseq package (version 1.24.2) to calculate alpha and beta diversity metrics (McMurdie and Holmes, 2013). Metagenomic data were neither filtered nor subsampled in our analysis. Principle coordinate analysis (PCoA) was performed using the Bray-Curtis Dissimilarity metric (Bray and Curtis, 1957).

Assembly of B. thetaiotaomicron Genome from Metagenomic Reads

To confirm the identity of B. thetaiotaomicron in our dataset, metagenomic reads from amoxicillin-treated samples were pooled and subsequently assembled using the metaSPAdes utility within SPAdes (version 3.13.0) using default settings (Bankevich et al., 2012; Nurk et al., 2017). Contigs were then binned and functionally annotated using the metagenomic binning tool on the PATRIC webs-server (3.5.38) (Wattam et al., 2014). The resulting bin identified as B. thetaiotaomicron was found to have 100% completeness, and an N50 value of 486,061 bp.

Metatranscriptomic Analysis Using SAMSA2

Cleaned metatranscriptomic reads were analyzed using a modified version of the SAMSA2 pipeline (Westreich et al., 2018). In brief, reads were merged using the Paired-End Read Merger (PEAR) program (version 0.9.10) and subsequently aligned to the RefSeq and SEED subsystems databases using DIAMOND (version 0.9.11) for whole-community transcriptional analysis (Buchfink et al., 2015; Overbeek et al., 2014). For the analysis of carbohydrate-active enzyme and virulence factor expression, metatranscriptomic reads were aligned to the CAZy dbCAN2 database (from 07/20/2018) and the core dataset from the Virulence Factor Database (VFDB; from 04/2019), respectively (Chen et al., 2005; Huang et al., 2018a; Zhang et al., 2018). Subsequent analyses were performed in DESeq2 (version 1.20.0) as described in Westreich et al. using the Benjamini-Hochberg method to correct for multiple hypothesis testing (Benjamini and Hochberg, 1995; Westreich et al., 2018).

HUMANn2 Analysis

The analysis for paired metagenomic and metatranscriptomic data were conducted following the HUMAnN2 pipeline (Franzosa et al., 2018; 2014). MetaPhlan2 (version 2.6.0) was used to generate a taxonomic profile from filtered metagenomic and metatranscriptomic reads (Segata et al., 2012). Functional profiling of transcripts was subsequently performed using HUMAnN2 (version 0.11.1) for the metagenome and metatranscriptome of each sample to produce abundances of gene families and pathways (Franzosa et al., 2018). Profiling of metatranscriptomes was guided by the taxonomic profile generated from the corresponding metagenome to guarantee correct mapping of the RNA reads to the genomes of detected species. Gene families generated from metatranscriptomic reads were smoothed against those from metagenomes using the Witten-Bell method to produce smoothed RNA and DNA RPKMs and relative expression values (Witten and Bell, 1991). Smoothed RNA gene families were then collapsed into GO terms and KEGG Orthologs based on the KEGG database of gene families and pathways (Ashburner et al., 2000; Kanehisa et al., 2012). MetaCyc path- ways were normalized in a similar fashion. RPKM values were subsequently converted to relative abundances for statistical analysis with LEfSe (Segata et al., 2011).

Single-Species Metatranscriptomic Analysis

The gene expression profile of key species was obtained from metatranscriptomic reads using a modified pipeline from Deng et al. (Deng et al., 2018). First, metatranscriptomic reads were classified using Kraken2 (version 2.0.7-beta) with default parameters as described above. Using this output, we identified the fifty most abundant species present in all of our metatranscriptomic samples. We subsequently extracted the reads mapping to these fifty species using the BBSplit tool in the BBMap utility (version 37.96) (Bushnell, 2014). Reads were then aligned to their corresponding reference genome using BWA-MEM (version 0.7.15) (Li and Durbin, 2010) and counted using the featureCounts command in the subread program (version 1.6.2) (Liao et al., 2014). Lastly, counts were input into DESeq2 (version 1.20.0) for differential expression analysis with multiple hypothesis testing correction using the Benjamini- Hochberg method (Benjamini and Hochberg, 1995; Love et al., 2014). For B. thetaiotaomicron, gene set enrichment analysis (GSEA) was performed using the GAGE package (version 2.30.0) in R using default settings (Luo et al., 2009).

B. thetaiotaomicron Growth Rate Determination

Overnight cultures of B. thetaiotaomicron grown in mGAM were pelleted and resuspended in 1X PBS + 0.1% L-cysteine. Cells were subsequently diluted to an optical density at 600 nm (OD600) of approximately 0.05 into minimal media containing 0.5% (w/v) of a single carbohydrate (glucose, dextrin, levan, pectin, or pullulan). Cells were incubated at 37°C under anaerobic conditions and growth was monitored by taking OD600 readings at regular time intervals. To determine the doubling time in each carbohydrate, the growth curves were fitted to an exponential growth function using default settings in Prism (version 8.0).

Minimal Inhibitory Concentration Determination

Minimal inhibitory concentrations (MICs) were determined using the broth dilution method (Wiegand et al., 2008). In short, overnight cultures of Bacteroides or C. saccharolyticum strains grown in mGAM or BHI++, respectively, were diluted 100-fold into BHI, mGAM, or minimal media with different carbon sources. Amoxicillin, ciprofloxacin, or doxycycline hydrochloride (Sigma-Aldrich, St. Louis, MO, USA) were added at varying concentrations to cell culture media and serially diluted two-fold. Cells were then incubated anaerobically at 37o C until controls reached their maximum OD600 reading (~20 h).

cDNA Synthesis

Purified and DNased RNA was converted to cDNA in technical triplicates using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, CA, USA, Cat: 4368814) according to the manufacturer’s instructions.

Quantitative PCR (RT-qPCR)

To validate the increased transcript levels of the B. thetaiotaomicron sus genes observed in the amoxicillin-treated samples, we compared the expression of the susB, susC, and susD genes using qPCR. Triplicates of each cDNA sample were pooled and assessed for concentration using the dsDNA-HS kit for the Qubit 3.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, U.S.; Cat: Q32854). Equal concentrations were then combined with B. thetaiotaomicron-specific susB, susC, susD, or rpoB primers as well as FastStart Essential DNA Green Master Mix (Roche Diagnostics, Indianapolis, IN, USA; Cat: 06924204001) and run in technical triplicates with negative controls on the Roche Light Cycler 96 system. The fold-change of the sus genes were subsequently calculated using the ΔΔCt method using rpoB as the housekeeping gene (Livak and Schmittgen, 2001; Rao et al., 2013). Primer sequences can be found in Table S1.

B. thetaiotaomicron and Universal Bacterial 16S qPCR

Quantification of 16S rRNA copies of total bacteria within the murine cecum was performed via qPCR using universal Eubacterial 16S primers (Table S1) as described previously (Vaishnava et al., 2011). In short, a plasmid containing a cloned segment of the bacterial 16S rRNA gene was used to generate a standard curve to calculate the number of 16S rRNA gene copies present. The absolute abundance of B. thetaiotaomicron was determined as described previously (Kovatcheva-Datchary et al., 2019; Samuel and Gordon, 2006). Briefly, a segment of the 16S rRNA gene from B. thetaiotaomicron (VPI-5482) was amplified using universal primers 27F and 1492R (Table S1). This PCR product was subsequently serially diluted to generate a standard curve and amplified using B. thetaiotaomicron-specific qPCR primers BT1F and BT1R (Table S1). Due to variation in 16S rRNA gene copy number between bacteria, these data do not represent actual bacterial numbers or viable cells and thus are only indicative of the abundance of 16S rRNA gene copies. For both methods, data are reported as log10 16S rRNA gene copies per gram of cecal content.

Biolog Metabolic Assay

Six-week old female C57BL/6J mice were treated with 25 mg/kg/day amoxicillin or vehicle control ad libitum in drinking water for 12 h as described above. Following the completion of treatment, mice were sacrificed, and total cecal contents were collected in sterile PBS + 0.1% L-cysteine inside an anaerobic chamber. Cecal contents were subsequently filtered through a 70 μm cell strainer to remove particulate matter and washed twice with PBS + 0.1% L-cysteine. Cells were resuspended in AN inoculating fluid from Biolog (Hayward, CA, USA) and diluted to a final optical density (OD600) of 0.13 (Garland and Mills, 1991; Huang et al., 2018b; Röling et al., 2000; Zhang et al., 2014). Next, 100 μL of the resulting dilution was inoculated into each well of a Biolog AN MicroPlate. Inoculated AN MicroPlates were incubated at 37°C in an anaerobic environment using AnaeroPack gas generator packets from Thermo Fisher Scientific (Waltham, MA, USA). Following incubation, the resulting color development in each well was measured using the OD590 value. To compare the number of viable cells present in this assay, Biolog inoculum was serially diluted in PBS + 0.1% L-cysteine and plated on mGAM plates to count colony forming units (CFUs). To quantify bacteria using qPCR, total DNA was extracted from 200 μL of Biolog inoculum from each sample using the ZymoBIOMICS DNA MiniPrep Kit (Irvine, CA, USA) according to the manufacturer’s instructions. We subsequently quantified 16S rRNA copies in equal volumes of DNA template using Eubacterial 16S rRNA primers as described above.

C. saccharolyticum Sporulation Assay

C. saccharolyticum was grown in BHI++ for 36 h under anaerobic conditions. Cultures were diluted 1:2 in fresh BHI++ and treated with varying concentrations of amoxicillin (Sigma-Aldrich, St. Louis, MO, USA) or DMSO control for 3 h. Following amoxicillin treatment, cells were placed on agar pad slides for light microscopy. Spores were subsequently quantified using imageJ (version 1.52) to detect phase-bright spores.

Scanning Electron Microscopy

Log phase C. saccharolyticum was grown under anaerobic conditions for three hours in the presence of 500 ng/mL amoxicillin. Individual 12 mm circular coverslips were placed in the wells of a 12-well plate and stored in 70% ethanol overnight. The ethanol was then removed and coverslips were allowed to dry in a biosafety cabinet until no traces of ethanol remained. Once dry, sufficient poly-D-lysine (Trevigen, Inc., Gaithersburg, MD, USA) was added to wells to cover the slips, which were then stored at 37°C for 90 min. The excess poly-D-lysine was then removed and coverslips were stored at 37°C until dry. 150 μL of a dense culture of C. saccharolyticum was then added to the coverslips and allowed to adhere for 30 min at room temperature. Excess culture was then removed and cells were fixed by adding 300 μL of fixative (5% glutaraldehyde/4% paraformaldehyde/0.1M sodium cacodylate) to each coverslip for 3.5 min. The coverslips were then removed from the fixative and transferred to new wells where they were washed four times with 0.1M sodium cacodylate for one minute each wash. The coverslips were then serially washed with ethanol (30%, 50%, 70%, 95%, 100%, 100%, and 100%) and stored in 100% ethanol. The samples then underwent CO₂ critical point drying, during which they were exposed to two rounds of infiltration using a Ladd Research Industries critical point dryer (Ladd Research, Williston, VT, USA). All samples were sputter coated in gold using the Emitech K550 sputter coater (Quorum Technologies Ltd, Kent, UK) with a two-minute sputter cycle at 2 mA, 45 mm. Images were taken using the Thermo Scientific Apreo scanning electron microscope (Thermo Fisher Scientific, CA, USA) collecting secondary electrons under the immersion mode at 2.00 kV and 25 pA.

Isolation and Analysis of B. thetaiotaomicron Strains from Murine Cecum Samples

Six-week old female C57BL/6J mice were treated with amoxicillin and sacrificed as described above. Cecal contents were isolated, serially diluted in 1X PBS + 0.1% L-cysteine, and plated on Bacteroides minimal media containing 0.5% glucose. Individual colonies were picked and the 16S rRNA gene was amplified from boiled cells using universal 27F and 1046R primers (Table S1). Amplicons were subsequently Sanger sequenced and aligned to the 16S rRNA gene sequences of several known Bacteroides species using MUSCLE within the Unipro UGENE software (version 1.32) (Edgar, 2004; Okonechnikov et al., 2012). Phylogenetic trees were generated using the iTOL webserver (Letunic and Bork, 2019).

Whole-Genome Sequencing and Phylogenetic Analysis of B. thetaiotaomicron Isolates

Genomic DNA extracted from B. thetaiotaomicron isolates DC20 and DC21 was extracted using the ZymoBIOMICS DNA Kit (Irvine, CA, USA) and prepared into sequencing libraries using the Ultra II FS DNA Library Prep Kit from New England Biolabs (Ipswich, MA, USA) to obtain fragments between 200–450 bp. Libraries were subsequently sequenced on an Illumina HiSeqX using paired-end, 150 bp reads and assembled into contigs using SPAdes (version 3.13.0). Phylogenetic comparison of B. thetaiotaomicron isolates with other members of the Bacteroides genus was performed using the codon tree method in the “Phylogenetic Tree” utility of the PATRIC webserver (3.5.38) (Wattam et al., 2014).

Quantitation of Cecal Glucose Levels

Six-week-old female C57BL/6J mice were treated with antibiotics and sacrificed as described above. Cecal contents were isolated and flash frozen in liquid nitrogen until further processing. Cecal glucose levels were quantified using the Glucose Assay Kit - reducing agent compatible (ab102517) from Abcam (Cambridge, MA, USA) as described previously (Sanchez et al., 2018). In brief, cecal contents were resuspended in glucose assay buffer, filtered using a 0.22 μm spin column, and deproteinized using a 10 kD MWCO PES spin filter from Corning (Corning, NY, USA). Glucose concentration in the resulting filtrate was quantified with the Glucose Assay Kit according to the manufacturer’s instructions.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical details of all experiments can be found in the figure legends and Results section. Unless otherwise noted, all sample numbers denote biological replicates. MetaCyc pathway abundances generated by HUMAnN2 were analyzed using LEfSe (version 1.0) on the Galaxy web server using default settings (http://huttenhower.sph.harvard.edu/galaxy). Differential abundance testing using SAMSA2 and single-species metatranscriptome output was performed using the DESeq2 package (version 1.20.0) in R (version 3.5.0) under default parameters. All p values generated by DESeq2 were corrected for multiple hypothesis testing using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995). PERMANOVA calculations for beta diversity metrics from metagenomics were performed using the vegan package with default settings (version 2.5 – 2) in R. Unpaired t tests and Mann-Whitney U tests were performed in Prism (version 8.0). Sample size estimation was not used.

Supplementary Material

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KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Bacterial and Virus Strains
Bacteroides thetaiotaomicron	ATCC	VPI-5482
Clostridium saccharolyticum WM1	DSMZ	DSM-2544
Bacteroides fragilis	ATCC	VPI-2553
Chemicals, Peptides, and Recombinant Proteins
Levan	Real Biotech Co., Ltd (Gongju-si, South Korea)	N/A
Agencourt AMPure XP beads	Beckman Coulter	A63880
Ammonium sulfate	Fisher Scientific	A702-500
Amoxicillin	Sigma-Aldrich	A8523-5G
AnaeroPack Anaerobic Gas Generator	Thermo Fisher Scientific	R681001
Brain-heart infusion (BHI) broth	Remel	R452472
Calcium chloride, 96% extra pure, anhydrous	Acros Organics	349610250
Ciprofloxacin	Sigma-Aldrich	17850-5G-F
Deoxynucleotide (dNTP) Solution Mix	New England Biolabs	N0447S
Dextrin, Type I	Sigma-Aldrich	D2006-100G
Dextrose (D-Glucose) Anhydrous	Fisher Scientific	D16-3
Doxycycline hydrochloride	Sigma-Aldrich	D3447-500MG
Ethanol, 200 proof, molecular biology grade	Fisher Scientific	07-678-003
Glutaraldehyde, 25% (aqueous)	Fisher Scientific	50-262-19
Hemin	Sigma-Aldrich	51280-1G
Hydrochloric acid	Fisher Scientific	A144-500
Inulin (from chicory)	Chem-Impex Int’l, Inc.	Cat#01977
Iron (II) sulfate heptahydrate	Sigma-Aldrich	F8633-250G
L-cysteine	Sigma-Aldrich	C7352-25G
L-histidine free base	MP Biomedicals	101954
Magnesium chloride hexahydrate	Fisher Scientific	M35-500
Menadione	Sigma-Aldrich	M5625-25G
Modified Gifu Anaerobic Media (mGAM)	HyServe	5433
Paraformaldehyde, 32%	Fisher Scientific	50-980-494
Pectin	MP Biomedicals	102587
Phosphate buffered saline, 10X	Fisher Scientific	BP399
Phusion High-Fidelity DNA Polymerase	New England Biolabs	M0530S
Poly-D-Lysine	Trevigen, Inc.	3439-100-01
Potassium phosphate monobasic	Fisher Scientific	P284-500
Pullulan	Alfa Aesar	J66961
Purified Diet (Basal Mix)	Envigo Teklad	TD.180901
RNase AWAY	Thermo Scientific	7000TS1
Roche DNase I recombinant, RNase-free	Sigma-Aldrich	4716728001
Sodium cacodylate (0.2M)	Fisher Scientific	50-980-232
Sodium chloride	Fisher Scientific	S642-500
Sodium hydroxide	Fisher Scientific	S318-100
β-nicotinamide adenine dinucleotide hydrate	Sigma-Aldrich	N1511
TE Buffer, 1X solution pH 8.0, low EDTA	Fisher Scientific	AAJ75793AE
Vitamin B₁₂	Sigma-Aldrich	V2876-1G
Water, Molecular Biology Grade	Fisher Scientific	BP2819-1
ZymoBIOMICS Microbial Community Standard	Zymo Research	D6300
Critical Commercial Assays
AN Inoculating fluid	Biolog	72007
AN MicroPlate for anaerobes	Biolog	1007
DNA/RNA Shield Collection and Lysis Tube	Zymo Research	R1102
FastStart Essential DNA Green Master Mix	Roche Diagnostics	6924204001
Glucose Assay Kit - Reducing agent compatible	Abcam	Ab102517
High Capacity cDNA Reverse Transcription Kit	Thermo Fisher Scientific	4368814
MICROBEnrich Kit	Thermo Fisher Scientific	AM1901
MICROBExpress Bacterial mRNA Enrichment Kit	Thermo Fisher Scientific	AM1905
MiSeq Reagent Kit v2 (500-cycles)	Illumina	MS-102-2003
NEBNext Ultra II FS DNA Library Prep Kit for Illumina	New England Biolabs	E7805S
NucleoSpin® Gel and PCR Clean-up kit	Machery-Nagel GmbH & Co	740609
Ovation Complete Prokaryotic RNA-seq Library System	NuGEN	0326-32
Ovation Ultralow V2 DNA-seq Library Preparation Kit	NuGEN	0344NB-08
Qubit dsDNA BR Assay Kit	Thermo Fisher Scientific	Q32850
Qubit RNA HS Assay Kit	Thermo Fisher Scientific	Q32852
Spin-X Centrifuge Tube Filter (0.22 um)	Costar	8160
Spin-X UF 500 10k MWCO PES Spin Filter	Corning	431478
ZymoBIOMICS DNA Kit	Zymo Research	D4301
ZymoBIOMICS DNA/RNA Mini Kit	Zymo Research	R2002
Deposited Data
Bacteroides thetaiotaomicron genome assembly from metagenomic reads	NCBI BioProject ID	PRJNA543857
Raw Metagenomic and metatranscriptomic reads	NCBI BioProject ID	PRJNA504846
Raw metagenomic reads from glucose supplementation experiment	NCBI BioProject ID	PRJNA515074
Raw metagenomic reads from purified diet experiments	NCBI BioProject ID	PRJNA549182
Genomic assembly of murine isolates of B. thetaiotaomicron	NCBI BioProject ID	PRJNA548111
Oligonucleotides
See Table S1	Thermo Fisher Scientific
Software and Algorithms
BBMap (version 37.96)	Bushnell, 2014	https://sourceforge.net/projects/bbmap/
Bowtie2 (version 2.3.0)	Langmead and Salzberg, 2012	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
Bracken (version 2.0.0)	Lu et al., 2017	https://ccb.jhu.edu/software/bracken/index.shtml?t=manual
BWA-Mem (version 0.7.15)	Li and Durbin, 2010	http://bio-bwa.sourceforge.net/
DIAMOND (version 0.9.11)	Buchfink et al., 2015	https://github.com/bbuchfink/diamond
HUMAnN2 (version 0.11.1)	Franzosa et al., 2018	https://bitbucket.org/biobakery/humann2/wiki/Home
imageJ (version 1.52)	Schneider et al., 2012	https://imagej.nih.gov/ij/
iTOL (version 4)	Letunic and Bork, 2019	https://itol.embl.de/
Kneaddata (version 0.6.1)	McIver et al., 2018	https://bitbucket.org/biobakery/kneaddata/wiki/Home
Kraken2 (version 2.0.7-beta)	Wood and Salzberg, 2014	https://ccb.jhu.edu/software/kraken/
LDA Effect Size (LEfSe, version 1.0)	Segata et al., 2011	http://huttenhower.sph.harvard.edu/galaxy/
MetaPhlan2 (version 2.6.0)	Truong et al., 2015	https://bitbucket.org/biobakery/metaphlan2
metaSPAdes (version 3.13.0)	Nurk et al., 2017	http://cab.spbu.ru/software/meta-spades/
Paired-End Read Merger (PEAR; version 0.9.10)	Zhang et al., 2014	https://cme.h-its.org/exelixis/web/software/pear/
PATRIC (version 3.5.38)	Wattam et al., 2014	https://www.patricbrc.org/
Primer-BLAST	Ye et al., 2012	https://www.ncbi.nlm.nih.gov/tools/primer-blast/
Prism (version 8.0)	GraphPad	https://www.graphpad.com/scientific-software/prism/
R (version 3.5)	The R Project for Statistical Computing	https://www.r-project.org/
R package: DESeq2 (version 1.20.0)	Love et al., 2014	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
R package: GAGE (version 2.30.0)	Luo et al., 2009	http://bioconductor.org/packages/release/bioc/html/gage.html
R package: phyloseq (1.24.2)	McMurdie and Holmes, 2013	https://joey711.github.io/phyloseq/
R package: vegan (version 2.5-2)		https://cran.r-project.org/web/packages/vegan/index.html
RStudio (version 1.1.419)	Rstudio	https://www.rstudio.com/
SAMSA2 (version 1.0)	Westreich et al., 2018	https://github.com/transcript/samsa2
SPAdes (version 3.13.0)	Bankevich et al., 2012	http://cab.spbu.ru/software/spades/
Subread (featureCounts) (version 1.6.2)	Liao et al., 2014	http://bioinf.wehi.edu.au/featureCounts/
Trimmomatic (version 0.36)	Bolger et al., 2014	http://www.usadellab.org/cms/?page=trimmomatic
Unipro UGENE (version 1.32.0)	Okonechnikov et al., 2012	http://ugene.net/
Other
Apreo scanning electron microscope	Thermo Fisher Scientific	N/A
BactronEZ Anaerobic Chamber	Sheldon Manufacturing	BAAEZ22
Critical point dryer	Ladd Research	N/A
Emitech K550 sputter coater	Quorum Technologies Ltd	K550
High Performance Ultrasonicator	Covaris	S220
Qubit 3.0 Fluorometer	Thermo Fisher Scientific	Q33216
Roche Light Cycler™ 96 system	Roche Diagnostics	N/A
SpectraMax M3 Multi-Mode Microplate Reader	Molecular Devices	89429-536

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Highlights.

Antibiotics perturb the metabolic capacity of the murine gut microbiome
Amoxicillin elevates expression of starch utilization genes in B. thetaiotaomicron
Fiber supplementation protects B. thetaiotaomicron from amoxicillin in vitro
Host diet has a major effect on the response of the microbiome to amoxicillin

Context and Significance.

Antibiotics are known to perturb the microbial flora and lead to numerous microbiome-related complications. Although microbial metabolism is known to be an important determinant of antibiotic susceptibility in vitro, its effects are less defined in the host. Using a combined omics approach, researchers at Brown University and their collaborators find that antibiotics change both the metabolic environment within the gut and the transcriptional response of the microbiome at the whole-community and species level. In turn, changing the metabolic environment alters the susceptibility of bacteria to antibiotics both in culture and in the mouse gut. Overall, this work suggests that host diet and metabolic state should be considered in efforts to safeguard the gut microbiome during antibiotic therapy.

ACKNOWLEDGMENTS

This work was supported by the U.S. Department of Defense through the Peer Reviewed Medical Research Program under award number W81XWH-18-1-0198; by the National Science Foundation through the Graduate Research Fellowship Program under award number 1644760 for D.J.C., B.J.K., S.P., and J.I.W.; and by the National Institutes of Health under institutional development awards P20GM121344 and P20GM109035 from the National Institute of General Medical Sciences, which fund the Center for Antimicrobial Resistance and Therapeutic Discovery and the COBRE Center for Computational Biology of Human Disease, respectively. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense, the National Science Foundation, or the National Institutes of Health. H.L. is supported by Glenn Foundation for Medical Research and Mayo Clinic Center for Biomedical Discovery. The Thermo Apreo VS scanning electron microscope used in this study was funded by an S10 award (NIH S10 OD023461) from the National Institutes of Health.

Footnotes

DATA AND CODE AVAILABILITY

The assembled B. thetaiotaomicron genomes have been deposited at DDBJ/ENA/GenBank under the accession numbers VEWO00000000 (DC20), VEWP00000000 (DC21), and VCHB00000000 (metagenomic assembly). Raw metagenomic and metatranscriptomic reads were deposited in the NCBI Short Read Archive under the following BioProject numbers: PRJNA504846 for metagenomic and metatranscriptomic reads from mice treated with amoxicillin, doxycycline, or ciprofloxacin; PRJNA515074 for metagenomic reads from glucose supplementation experiments; and PRJNA549182 for metagenomic reads from purified diet experiments.

SUPPLEMENTAL INFORMATION

Supplemental Information can be found online at https://doi.org/10.1016/j.cmet.2019.08.020.

DECLARATION OF INTERESTS

The authors declare no competing interests.

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PERMALINK

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

Damien J Cabral

Swathi Penumutchu

Elizabeth M Reinhart

Cheng Zhang

Benjamin J Korry

Jenna I Wurster

Rachael Nilson

August Guang

William H Sano

Aislinn D Rowan-Nash

Hu Li

Peter Belenky

SUMMARY

In Brief

Graphical Abstract

INTRODUCTION

RESULTS

Figure 1. Amoxicillin, Doxycycline, and Ciprofloxacin Elicit Unique Changes in Microbiome Structure.

Amoxicillin, Doxycycline, and Ciprofloxacin Elicit Unique Changes in Microbiome Structure

Antibiotic Treatment Is Associated with Broad Reductions in Metabolic Gene Expression within the Gut Microbiota at the Whole-Community Level

Figure 2. Antibiotic Treatment Is Associated with Broad Reductions in Metabolic Gene Expression within the Gut Microbiota at the Whole- Community Level.

Amoxicillin Treatment Alters CAZyme Expression within the Gut Microbiome

Figure 3. Amoxicillin Treatment Alters CAZyme Expression within the Gut Microbiome.

Amoxicillin Therapy Changes the Expression of Key Metabolic Genes within Individual Members of the Microbiota

Figure 4. Amoxicillin Therapy Changes the Expression of Key Metabolic Genes within Individual Members of the Microbiota.

B. thetaiotaomicron Increases the Expression of Polysaccharide Utilization Genes in Response to Amoxicillin Treatment

Figure 5. B. thetaiotaomicron Increases the Expression of Polysaccharide Utilization Genes in Response to Amoxicillin Treatment.

Polysaccharide Utilization Decreases Susceptibility of B. thetaiotaomicron In vitro

Figure 6. Polysaccharide Utilization Decreases Susceptibility of B. thetaiotaomicron In vitro.

Host Diet Modulates the Response of the Microbiome to Amoxicillin in Mice

Figure 7. Host Diet Modulates the Response of the Microbiome to Amoxicillin in Mice.

DISCUSSION

Metagenomics and Metatranscriptomics—Advantages and Limitations

Host Diet and Bacterial Metabolism

Limitations of study

STAR★METHODS

LEAD CONTACT AND MATERIALS AVAILABILITY

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

Bacterial Strains

METHOD DETAILS

Animal Experiments

Nucleic Acid Extraction and Quantification

Metagenomic and Metatranscriptomic Library Preparation

Preprocessing of Raw Sequencing Reads

Analysis of Metagenomic Reads

Assembly of B. thetaiotaomicron Genome from Metagenomic Reads

Metatranscriptomic Analysis Using SAMSA2

HUMANn2 Analysis

Single-Species Metatranscriptomic Analysis

B. thetaiotaomicron Growth Rate Determination

Minimal Inhibitory Concentration Determination

cDNA Synthesis

Quantitative PCR (RT-qPCR)

B. thetaiotaomicron and Universal Bacterial 16S qPCR

Biolog Metabolic Assay

C. saccharolyticum Sporulation Assay

Scanning Electron Microscopy

Isolation and Analysis of B. thetaiotaomicron Strains from Murine Cecum Samples

Whole-Genome Sequencing and Phylogenetic Analysis of B. thetaiotaomicron Isolates

Quantitation of Cecal Glucose Levels

QUANTIFICATION AND STATISTICAL ANALYSIS

Supplementary Material

Highlights.

Context and Significance.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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