Clostridioides difficile-Associated Antibiotics Alter Human Mucosal Barrier Functions by Microbiome-Independent Mechanisms

Based on reported meta-analyses of community-associated C. difficile infection and antibiotics (26, 27) we selected 3 antibiotics representing the odds ratio spectrum from less than 1 (tigecycline, no risk) to 6 (ciprofloxacin, medium risk) to 20 (clindamycin, highest risk) in order to achieve complete coverage of the CDAD risk landscape. Tigecycline, a derivative of tetracycline that is delivered intravenously, is currently used to treat CDAD (28) and hence is an attractive antibiotic for this study. Although there are currently no epidemiological studies giving an odds ratio to tigecycline, we assigned it a low risk based on its similarity to tetracycline, an antibiotic which showed no increased risk of CDAD in the meta-analyses (27), and its strong safety profile (29) in clinical use, including for CDAD treatment.

We used clindamycin and ciprofloxacin for CDAD-associated antibiotics, as they have the highest risk of CDAD among commonly used antibiotics (26, 27). Although all three are considered broad-spectrum antibiotics, they differ in their mechanisms of action and species specificity. We considered mechanism of action as part of the study design. Tigecycline (low risk) and clindamycin (high risk) share a similar mechanism of action, both targeting bacterial translation machinery. Conversely, ciprofloxacin (intermediate risk) inhibits bacterial DNA replication. Notably, ciprofloxacin has an FDA black-box label arising from adverse human side effects on the nervous and musculoskeletal systems (30), yet it has been used to treat Crohn’s disease, a chronic gastrointestinal disorder, with a reasonable safety profile on the gastrointestinal tract although unclear efficacy (31). The mechanisms for effects of ciprofloxacin on human cells have been implicated in damage to mitochondrial DNA and in alterations in DNA-modifying enzymes, but a clear picture has not emerged (30, 32). Gene expression and phenotypic effects of these antibiotics on human colon mucosal barrier cells will thus help illuminate whether additional off-target effects of these drugs exist and should be further studied.

To test the commensal-independent effects of antibiotics on human tissue, we used a transwell-based in vitro epithelial barrier without bacteria to model a germfree human gut (33, 34). We treated mature mucosal barriers with antibiotics dosed from the basal side, using clinically relevant dose ranges and estimating the maximum concentration of drug in serum (C_max) in each case from a combination of the FDA inserts (Table 1) and published literature on pharmacokinetics for each drug, which provide C_max values comparable to those in Table 1 (29, 35–40). The standard dosing for intravenous tigecycline includes a 100-mg loading dose followed by maintenance doses, resulting in C_max values comparable to those shown in Table 1 (29, 39); however, protocols involving higher daily doses (400 mg every 24 h [q24h]) are also used (41), motivating exploration of high-end dosing ranges.

Basal dosing of colonic epithelial monolayers at low doses near the reported plasma C_max is expected to replicate some but not all features of colonic epithelial exposure. The lack of significant metabolism in vitro results in exposure to C_max throughout the experiment, rather than episodically, as in vivo. Furthermore, although the orally dosed antibiotics are absorbed in the intestine, with subsequent strong partitioning into tissue compartments, all 3 antibiotics considered in this study are also known to be partially excreted in feces in humans (35, 36, 42), resulting in apical exposure in vivo. Tigecycline is given intravenously and is partly eliminated via biliary export into the intestine and passage to feces (40). Tigecycline also strongly partitions into tissue compartments (40, 42), with concentrations in the colon tissue exceeding that in plasma (36, 42); whether it exhibits basal-to-apical transport in the colon is not reported in the literature, but the relatively high in vivo basal concentrations suggest that our basal dosing scheme is reasonable for tigecycline. Orally administered ciprofloxacin has variable absorption in the intestine (38) and, with a relatively long half-life, builds up substantial concentrations in blood over several days of treatment. Ciprofloxacin is partly excreted in urine, but also in feces, where its presence may arise in part from systemic circulation via a known basal-to-apical transport route in colon epithelia (35, 38). Thus, for ciprofloxacin, over the course of the 24- to 48-h experiment, we likely exposed the cells in the ciprofloxacin case to both basal and apical antibiotic due to basal-apical transport. Clindamycin is administered orally or systemically and in both cases partitions strongly into tissues; it is also excreted into the intestine via biliary transport (43). Whether it is transported basal-apically in colonic epithelia is unknown, but the basal dosing used here is reasonable given the strong tissue partitioning.

In order to capture extremes of response, Caco2/HT29-MTX monolayers were exposed for 24 h at either standard dose concentrations (equivalent to C_max) or higher doses, as listed in Table 1, then harvested and lysed. Transcriptome sequencing (RNA-seq) identified gene expression changes under high- and low-exposure conditions, with the largest number of transcriptional changes being driven by ciprofloxacin exposure (Fig. 1B to E). Tigecycline was insoluble in water and was therefore dosed in dimethyl sulfoxide (DMSO). All other antibiotics were dosed in water and compared back to water vehicle. We found DMSO exposure at high concentration had a significant effect on gene expression (Fig. S1), and we therefore performed subsequent analysis using the lower concentrations of both DMSO and tigecycline, listed as “standard” dose type in Table 1.

Unsupervised hierarchical clustering of the 606 genes with statistically significant gene expression changes in at least two exposure groups, using the high concentrations for ciprofloxacin and clindamycin but the low concentration for tigecycline, revealed antibiotic-specific alterations of the gut transcriptome (Fig. S2). This clustering showed various patterns of transcriptional response to exposure. Several transcripts shared similar expression configurations across experimental conditions, some had dose-dependent effects correlating to increasing or decreasing CDAD risk, and still others exhibited more complex behaviors not apparent from the initial clustering. However, the ciprofloxacin-driven expression changes dominated the clustering, highlighting the need for more nuanced computational analysis.

In order to identify genes with transcriptional changes shared between both CDAD-associated antibiotics, we used a self-organizing map (SOM). An SOM is a neural network-based unsupervised clustering technique that groups similar observations together on the SOM neurons. Here, we used the SOM to cluster gene transcript fold changes across antibiotic exposures to identify genes with expression changes associated with CDAD risk. Similarly to other dimensionality-reduction techniques, such as principal-component analysis (PCA), SOMs produce a low-dimensional projection of high-dimensional data that facilitates visualization of patterns. However, unlike PCA or our previous hierarchical clustering, the SOM analysis simultaneously merges two important features, as follows: (i) it incorporates information about the expected number of clusters in the data by defining the number of SOM neurons based on experimental design (number of conditions), and (ii) it allows the data to drive identification of the most informative groups among those clusters (i.e., SOM neurons).

The architecture of the SOM employed here to map the 606 significant genes is based on increased or decreased gene expression (2 directions) in each of three experimental conditions (i.e., clindamycin, ciprofloxacin, or tigecycline, each compared to its respective control), with an extra neuron for noisy profiles (2³ + 1 = 9 neurons). Genes with similar expression patterns cluster in a node, with the number of genes per node indicated (Fig. 2A). Plotting neighbor weight distances allows for the visualization of similarities between nodes (Fig. 2B).

Each neuron of the SOM captured gene expression responses to antibiotic exposure that grouped according to changing CDAD risk ratios. These patterns could then be investigated by plotting line graphs of the gene fold changes across increasing CDAD risk for each node (Fig. 2C). Two nodes identified gene expression responses that were specifically elevated (node 3) or repressed (node 7) in response to CDAD-associated antibiotic exposure. Another two nodes (4 and 6) captured risk ratio-dependent changes in gene expression responses to CDAD-associated antibiotic exposure, with genes on node 4 being more downregulated and genes on node 6 being more upregulated in antibiotics with higher CDAD risk ratios. Altogether, nodes 3, 4, 6, and 7 capture a set of 261 genes with expression patterns common among ciprofloxacin and clindamycin that indicated a shared pattern of expression unique to the CDAD-associated antibiotics (Fig. 2C), despite different mechanisms of action between ciprofloxacin and clindamycin and similar mechanisms for tigecycline and clindamycin.

In order to identify the biological functions associated with CDAD-associated antibiotic exposure, we performed gene ontology (GO) enrichment analysis (GOEA) of each node (Fig. 2D, Table S1). We would expect nodes that cluster by mechanism of action to be enriched in related GO terms. For instance, the gene expression responses common to tigecycline and clindamycin (nodes 2 and 8) were enriched for the cellular targets of those drugs, translation machinery and chromosome maintenance (Fig. 2D). It is important to note that these targets are considered bacterial cellular components, yet we found they impacted mammalian cells. This finding from the SOM clustering that grouped known target-associated gene expression responses to tigecycline and clindamycin provided an important positive control for interpreting the biological functions associated with the other SOM neurons.

We then analyzed the SOM clusters that captured genes with shared patterns of expression response to CDAD-associated antibiotics (nodes 3, 4, 6, and 7), but distinct from unassociated antibiotics, to generate mechanistic hypotheses of host-dependent mechanisms of CDAD. The GOEA functional annotations of CDAD-associated antibiotic exposure showed an accumulation of cellular toxins in the cell via retrograde secretion (node 3, toxin transport) coupled with a decrease in secretion out of the cell (node 7). We found that as antibiotic-CDAD risk ratios increased, genes associated with immune signaling GO terms were suppressed (node 4), and expression of genes associated with cell-cell and cell-extracellular matrix (ECM) connections was increased (nodes 6 and 7), both in a dose-dependent manner. Node 6 captured genes related to focal adhesion and anchoring junctions, pathways reported to be enhanced in gut wound-healing responses (44), suggesting CDAD-associated antibiotics might result in greater cell stress or death, a hypothesis that can be tested experimentally. The suppression in immune signaling may also alter important barrier functions critical in host-pathogen response (reviewed in references 45), and likewise can be probed in subsequent experiments. Overall, GOEA of these SOMs suggested that exposure to CDAD-associated antibiotics resulted in alterations to transport of extracellular components out of the cell and toxins into the cell and in a reduced immune capacity after only 24 h of exposure.

Based on the results of the SOM analysis, we hypothesized that CDAD-associated antibiotic exposure would result in acute and possibly persisting effects of impaired epithelial barrier and impaired innate immune cell function. We tested these SOM predictions experimentally using three complementary levels of in vitro models, as follows: (i) acute effects on epithelial barrier survival and function exposed for 24 h to standard doses of antibiotics listed in Table 1; (ii) chronic (3-day) effect on epithelial exposure to antibiotics and toxin; and (iii) acute effects on innate immune cell function exposed to standard concentrations of the antibiotics listed in Table 1.

We assessed the barrier function of Caco2/HT29-MTX mucosal barriers following 24 h of exposure to standard concentrations of antibiotics (Table 1) dosed in the basal compartment. We found a small but significant increase in cell death in monolayers with ciprofloxacin exposure, which agrees with results of previous work using significantly higher concentrations (46), and the same result with clindamycin, which has not been demonstrated previously (Fig. S3). It is unclear whether this increase is biologically significant, but the upregulation in adhesion-related genes is consistent with a wound-healing response (44). Despite the increase in cell death, none of the antibiotics used affected the physical integrity of the barrier as determined by transepithelial electrical resistance 24 h post exposure (Fig. S4).

To better mimic the 3- to 7-day course of antibiotics in routine in vivo human treatment patterns, we extended the exposure period to a 3-day basal dose, again using the standard concentrations listed in Table 1. We quantified both cell-associated and secreted mucin production, as these strongly influence microbial interactions with the mucosal barrier. Total cell-bound (Fig. 3A) mucin of Caco2/HT29-MTX monolayers was reduced robustly following exposure to both CDAD-associated antibiotics, while mucins in low-risk CDAD exposure groups remained unchanged (Fig. 3A and B). These cell-bound mucins, including MUC1 and MUC4, are critical for protection from intestinal pathogens, as they alert the cell to the presence of invading pathogens through intracellular signal transduction (reviewed in reference 47). Secreted mucins, which provide both a niche for beneficial microbes and a physical obstacle preventing pathogens from accessing the epithelial surface, were reduced with both CDAD-associate antibiotics, although the clindamycin exposure group does not reach statistical significance (Fig. 3B). Together, this reduction in secreted and cell-bound mucins with CDAD-associated antibiotics could provide increased access to the epithelia for C. difficile and its toxins.

To assess the effect of our antibiotic panel on primary tissue, we repeated this experiment using primary cell-derived 2-dimensional (2D) enteroids. We assayed for one of the main transmembrane mucin genes in the colon (48), muc17, as it was highly expressed in this donor and was one of the mucin genes that was significantly downregulated in our RNA-seq analysis. We found that expression of muc17 was reduced with ciprofloxacin exposure but not with clindamycin or tigecycline exposure (Fig. 3C); however, we did not assay for other mucin genes in this donor. The inconsistency between cell lines and primary tissue could suggest a reduced role for mucin changes in clindamycin exposure or a donor-specific reduced effect of clindamycin. Other cell-associated mucins (e.g., Muc1 or Muc4) may be contributing to the changes in this primary cell donor shown in Fig. 3A.

To assess the effect of extended, low-dose antibiotic exposure on immune function, we treated an immunocompetent mucosal barrier (Caco2/HT29-MTX monolayers with monocyte-derived dendritic cells added to the basal compartment) for 3 days with each antibiotic, again dosing from the basal side. Interleukin 8 (IL-8) secretion is the primary chemokine implicated in CDAD (5). IL-8 is required for neutrophil recruitment to contain the infection, yet neutrophils are also implicated in progression of disease (49). Thus, a delicate control over dissemination and clearance of neutrophils is likely required for resolution of infection.

We therefore assessed the effect of antibiotics on the ability of immunocompetent mucosal barriers to induce il8 expression and IL-8 secretion following lipopolysaccharide (LPS) stimulation. LPS signals through TLR4 and tlr4 gene expression should increase following its activation, yet tlr4 expression did not increase with LPS stimulation following ciprofloxacin exposure (Fig. 3D). Clindamycin-treated barriers had lower levels of tlr4 relative to those of the vehicle, although this was not significantly lower by Student’s t test than those for tigecycline (Fig. 3D). We found that il8 gene expression (Fig. 3E) is reduced following ciprofloxacin and clindamycin exposure but unchanged with tigecycline in LPS-treated barriers. IL-8 secretion (Fig. 3F) was reduced to a statistically significant extent in all exposure groups. It is likely that the magnitude of change is important in the case of CDAD-associated antibiotics.

To test whether the immune cells are impaired in function, we performed phagocytosis and killing assays using green fluorescent protein-positive (GFP⁺) Escherichia coli. We found that pretreating macrophages with CDAD-associated antibiotics reduced both the phagocytosis of E. coli (Fig. 3G) and the subsequent killing of phagocytosed E. coli (Fig. 3H). Together, these data support the hypothesis that CDAD-associated antibiotics lead to a loss of immune responsiveness, which one can imagine might contribute to outgrowth of C. difficile.

Previous work has shown the importance of mucus—primarily made up of mucins—in preventing C. difficile toxins from entering gut cell lines in culture (50). CDAD pathology is driven by the cytotoxic effects of toxins, primarily those of TcdB (5). TcdB enters epithelial cells through receptor-mediated endocytosis (51), although none of the known toxin receptors (NECTIN3, CSPG4, or FZD1/2/7) had significantly altered expression following exposure to any antibiotic we tested. Following acidification of the vacuole, the toxin enters the cytosol, where it glucosylates its GTPase targets, Rho, Rac, and Cdc42. This leads to actin depolymerization, characterized by visible rounding of the cell, and eventually to cell death. The in vitro human mucosal barrier used in this work is affected by TcdB as expected, by cell rounding and areas of cell death, as determined by holes in the monolayer after 48 h of exposure to C. difficile spent medium (Fig. S5), demonstrating the utility of the system for studying TcdB effects.

To understand the translation of these altered barrier properties to potential impact in CDAD, we apically exposed mucosal barriers with TcdB (following pretreatment with standard concentrations of antibiotics [Table 1] in the basal compartment) and measured its action by the loss of a cellular target, activated Rac-1, by Western blot analysis. Both CDAD-associated antibiotics had deactivation of Rac1 at 24 h, while tigecycline and controls were still active by quantitative Western blot (Fig. 4A and B), implicating a shared sensitization to C. difficile toxin from CDAD-associated antibiotics with a mechanism independent of commensals.

Caco2 (clone C2BBe1, passage 48 to 58; ATCC, Manassas, VA) and HT29-MTX (passage 20 to 30; Sigma-Aldrich, St. Louis, MO) were maintained in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Gaithersburg, MD) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Atlanta Biologicals, Flowery Branch, GA), 1% GlutaMax (Gibco), 1% nonessential amino acids (NEAA, Gibco), and 1% penicillin-streptomycin (Pen-Strep). Both cell lines were passaged twice postthawing before their use for transwell seeding. Briefly, the apical side of a transwell membrane was coated with 50 mg/ml collagen type I (Corning Inc., Corning, NY) overnight at 4°C. Caco2 at 80 to 90% confluence and HT29-MTX at 90 to 95% confluence were harvested using 0.25% trypsin-EDTA and mechanically broken up into single cells. A 9:1 ratio of C2BBe1 to HT29-MTX was seeded onto 12-well 0.4-mm-pore polyester transwell inserts (Corning, Tewksbury, MA) at a density of 10⁵ cells/cm². Seeding medium contained 10% heat-inactivated FBS, 1× GlutaMax, and 1% P-S in advanced DMEM (Gibco). Seven days postseeding, the medium was switched to a serum-free gut medium by replacing FBS with insulin-transferrin-sodium selenite (ITS; Roche, Indianapolis, IN), and the epithelial cultures were matured for another 2 weeks. P-S was left out of the medium during experimental procedures.

Colon organoids (enteroids) used in this study were established and maintained as previously described (56, 57). Endoscopic tissue biopsy specimens were collected from the ascending colon of deidentified individuals at either Boston Children’s Hospital or Massachusetts General Hospital with the donors’ informed consent. Methods were carried out in accordance to the standards of the Institutional Review Board of Boston Children’s Hospital (protocol number IRB-P00000529) and the Koch Institute Institutional Review Board Committee, as well as those of the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects. Tissue was digested in 2 mg · ml⁻¹ collagenase I (catalog [cat.] no. 07416; Stemcell Technologies, Vancouver, BC, Canada) for 40 min at 37°C, followed by mechanical dissociation, and isolated crypts were resuspended in growth factor-reduced Matrigel (cat. no. 356237; Becton, Dickinson) and polymerized at 37°C. Organoids were grown in expansion medium (EM) consisting of advanced DMEM/F12 supplemented with L-WRN conditioned medium (65% vol/vol, cat. no. CRL-3276; ATCC,), 2 mM GlutaMax (cat. no. 35050-061; Thermo Fisher), 10 mM HEPES (cat. no. 15630-080; Thermo Fisher), penicillin-streptomycin (Pen-Strep) (cat. no. 15070063; Thermo Fisher), 50 ng · ml⁻¹ murine epidermal growth factor (EGF) (cat. no. PMG8041; Thermo Fisher), N2 supplement (cat. no. 17502-048; Thermo Fisher), B-27 supplement (cat. no. 17502-044; Thermo Fisher), 10 nM human [Leu15]-gastrin I (cat. no. G9145; Sigma), 500 μM N-acetyl cysteine (cat. no. A9165-5G; Sigma), 10 mM nicotinamide (cat. no. N0636; Sigma), 10 μM Y27632 (cat. no. 1293823; Peprotech), 500 nM A83-01 (cat. no. 2939; Tocris), 10 μM SB202190 (cat. no. 1523072; Peprotech), and 5 nM prostaglandin E2 (cat. no. 72192; Stemcell Technologies) at 37°C and 5% CO₂. Organoids were passaged every 7 days by incubating in Cell Recovery Solution (cat. no. 354253; Corning) for 40 min at 4°C, followed by mechanical dissociation and reconstitution in fresh Matrigel at a 1:4 ratio.

For 2D enteroid studies, at day 7 post passaging, colon organoids were collected, Matrigel was dissolved with Cell Recovery Solution for 40 min at 4°C, followed by incubation of Matrigel-free organoids in trypsin (cat. no. T4549; Sigma) at 37°C for 5 min. Organoids were mechanically dissociated into single cells, resuspended in EM without nicotinamide and with 2.5 μM thiazovivin (cat. no. 3845; Tocris) in the place of Y27632, and seeded onto 24-well 0.4-μm-pore polyester transwell inserts (cat. no. 3493; Corning) coated with a mixture of 200 μg/ml type 1 collagen and 1% Matrigel at a density of 1 × 10⁵ cells/transwell. After 3 to 4 days of incubation, monolayers were confluent and differentiation was initiated. For differentiation, apical medium was replaced with advanced DMEM/F12 plus HEPES, GlutaMax, and Pen-Strep, and basal medium was replaced with differentiation medium (DM), which is EM without L-WRN conditioned medium, nicotinamide, prostaglandin E2, and Y27632 but supplemented with 100 ng ml⁻¹ human recombinant noggin (cat. no. 120-10C; Peprotech) and 20% R-spondin-conditioned medium (cat. no. SCC111; Sigma). Transepithelial electrical resistance (TEER) measurements were performed using the EndOhm-12 chamber with an EVOM2 meter (World Precision Instruments). At day 8 post seeding, the 2D enteroids were washed to remove Pen-Strep and used for further experimentation.

Monocyte-derived dendritic cells were used as the immune component of the gut when indicated. Briefly, peripheral blood mononuclear cells (PBMCs) were processed from Leukopak (Stemcell Technologies). Monocytes were isolated from PBMCs using the EasySep human monocyte enrichment kit (cat. no. 19058; Stemcell Technologies) and were differentiated in RPMI medium (Gibco) supplemented with 10% heat-inactivated FBS (Gibco), 50 ng/ml granulocyte-macrophage colony-stimulating factor (GM-CSF; BioLegend, San Diego, CA), 35 ng/ml IL-4 (BioLegend), and 10 nM retinoic acid (Sigma). After 7 days of differentiation (at day 19 or 20 post epithelial cell seeding), immature dendritic cells were harvested using phosphate-buffered saline (PBS; Gibco) and seeded onto the basal side of the gut transwells in the absence of Pen-Strep 1 day prior to the start of experiment. Macrophages were derived similarly, but with macrophage colony-stimulating factor (M-CSF; BioLegend) at 500 ng/ml.

RNA was prepared using a PureLink RNA minikit (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. DNA removal was done on a column with PureLink DNase (Invitrogen). cDNA synthesis using a High-Capacity RNA-to-cDNA kit (Applied Biosystems, Foster City, CA) according to the product insert. Quantitative PCR (qPCR) was completed using TaqMan assays with Fast Advanced master mix (Applied Biosystems) per the manufacturer’s guidelines.

RNA samples were quantified and quality assessed using a Fragment Analyzer (Advanced Analytical). Total RNA (20 ng) was used for library preparation with ERCC spike-in control mix A (10⁻⁶ final dilution; Ambion). All steps were performed on a Tecan EVO 150 instrument. 3′-digital gene expression (DGE) custom primers 3V6NEXT-bmc#1 to 3V6NEXT-bmc#24 were added to a final concentration of 1.2 μM (5′-/5Biosg/ACACTCTTTCCCTACACGACGCTCTTCCGATCT[BC₆]N₁₀T₃₀VN-3′, where 5Biosg = 5′ biotin, [BC₆] = 6-bp barcode specific to each sample/well, and N₁₀ = unique molecular identifiers; Integrated DNA Technologies). After addition of the oligonucleotides, samples were denatured at 72°C for 2 min, followed by addition of SmartScribe reverse transcriptase (RT) per the manufacturer’s recommendations along with template-switching oligo5V6NEXT (12 μM, 5′-iCiGiCACACTCTTTCCCTACACGACGCrGrGrG-3′, where iC = iso-dC, iG = iso-dG, and rG = RNA G) and incubation at 42°C for 90 min, followed by inactivation at 72°C for 10 min. Following the template-switching reaction, cDNA from 24 wells containing unique well identifiers was pooled and cleaned using RNA AMPure beads at 1.0×. cDNA was eluted with 90 μl of water, followed by digestion with exonuclease I at 37°C for 45 min and inactivation at 80°C for 20 min. Single-stranded cDNA was then cleaned using RNA AMPure beads at 1.0× and eluted in 50 μl of water. Second-strand synthesis and PCR amplification were done using the Advantage 2 polymerase mix (Clontech) and the SINGV6 primer (10 pmol, 5′-/5Biosg/ACACTCTTTCCCTACACGACGC-3′; Integrated DNA Technologies). PCR was performed for 12 cycles, followed by cleanup using regular solid-phase reversible immobilization (SPRI) beads at 1.0×, and cDNA was eluted with 20 μl of elution buffer. Successful amplification of cDNA was confirmed using the Fragment Analyzer. Illumina libraries were then produced using standard Nextera tagmentation, substituting the P5NEXTPT5-bmc primer (25 μM, 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCG*A*T*C*T*-3′, where * indicates phosphorothioate bonds; Integrated DNA Technologies) in place of the normal N500 primer.

Final libraries were cleaned using SPRI beads at 1× and quantified using the Fragment Analyzer and qPCR before being loaded for paired-end sequencing using the Illumina NextSeq 500 platform.

Postsequencing quality control on each of the libraries was performed to assess coverage depth, enrichment for mRNA (exon/intron and exon/intergenic density ratios), fraction of rRNA reads, and the number of detected genes using bespoke scripts. The sequencing reads were mapped to the hg38 reference genome using star/2.5.3a. Gene expression counts were further estimated using ESAT version 1 (58).

Escherichia coli GFP (ATCC 25922GFP) was grown to the early log phase in LB medium plus ampicillin, then washed and resuspended in RPMI without antibiotic at the appropriate density. PBMC-derived macrophages were treated with low doses (Table 1) of indicated antibiotics for 3 days in RPMI with heat-inactivated FBS (Atlanta Biologicals). Antibiotic was removed, and antibiotic-free RPMI with GFP⁺ E. coli was added at a multiplicity of infection (MOI) of 10:1. Extracellular E. coli was washed off after 24 h. For phagocytosis, at 30 min postinfection, cells were fixed and permeabilized, stained with DAPI (4′,6-diamidino-2-phenylindole, Thermo Fisher) and ActinRed 555 ReadyProbes (Molecular Probes, Life Technologies, Carlsbad, CA) stained, and imaged. The percentage of macrophages with at least one GFP+ bacterium was calculated from fluorescence microscopy images. For the intracellular survival assay, macrophages were lysed in water, and supernatants were plated for CFU.

Gene expression fold changes between the controls and the antibiotic-treated cultures were considered a function of CDAD risk and were normalized to be between 0 and 1 across the 3 antibiotics used. Genes without expression fold changes across all 3 conditions were omitted. The map was initialized with a 2-dimensional 3 × 3 square grid and implemented using the MATLAB (MathWorks, Natick, MA) R2017b Neural Network Toolbox.

Gene ontology enrichment on all GO terms was performed using the free online PANTHER overrepresentation test (59–61). The false-discovery rate (FDR) was set to <0.05.

Prism 8 software (GraphPad Software, La Jolla, CA) was used to graph all data except that for SOMs. Statistical tests of measurements were used from the Prism suite as noted in the figure legends. Statistical significance is indicated as follows: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

Viability of monolayers post-antibiotic exposure was assessed using the Viability/Cytotoxicity Assay for Animal Live & Dead Cells kit (Biotium, Fremont, CA) according to the package insert. The ratio of red to green cells was measured using ImageJ.

Apical medium (secreted mucin) or supernatant from the lysed cell pellet (cell-bound mucin) was stained for 2 h at room temperature with 1% Alcian blue (Electron Microscopy Sciences) at a ratio of 1:4 with the sample. Dye-treated mucin was sedimented by a 30-min centrifugation, followed by two wash steps in wash buffer (290 ml 70% ethanol, 210 ml 0.1 M acetic acid, and 1.2 g MgCl₂). Dye-treated mucin was resuspended in 10% SDS, and absorbance at 620 nm was read on a plate reader. Calculations were made based on a known standard prepared in parallel.

Western blotting was performed under reducing conditions using iBlot 2 dry blotting system (Invitrogen) standard procedures. Primary antibodies were incubated at 4°C overnight and diluted as follows in Odyssey blocking buffer: rabbit monoclonal anti-Vinculin antibody (EPR8185; abcam, Cambridge, MA) at 1:3,000, mouse monoclonal anti-Rac1 antibody (23A8; abcam) at 1:750, and purified mouse Anti-Rac1 antibody (clone 102/Rac1; BD Transduction Laboratories, San Jose, CA) at 1:750. For detection, goat anti-rabbit or anti-mouse IR800-conjugated secondaries at 1:8,000 (LI-COR Lincoln, NE) were incubated for 30 min at room temperature in Odyssey blocking buffer (Tris-buffered saline [BS]) with 0.1% Tween 20. Imaging of the membrane used an LI-COR Odyssey imager with settings as follows: 24-μm resolution and a high-quality laser intensity of 2.0 on the 800 channel.

Secreted IL-8 was measured by Quantikine enzyme-linked immunosorbent assay (ELISA) human IL-8/CXCL8 immunoassay (R&D Systems, Minneapolis, MN) per the manufacturer’s guidelines.

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (62) and are accessible under GEO series accession number GSE135383.