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A Pathogen-Selective Antibiotic Minimizes Disturbance to the Microbiome 1

Jiangwei Yao‡, Robert A. Carter§, Grégoire Vuagniaux†, Maryse Barbier†, Jason W. Rosch‡, and 2 Charles O. Rock‡1 3

4 Departments of ‡Infectious Diseases and §Computational Biology, St. Jude Children’s Research 5

Hospital, Memphis, Tennessee 38105, and †Debiopharm International SA, Lausanne, 6 Switzerland 7

8 9

Running Title: FabI inhibitor and the microbiome 10

1To whom correspondence should be addressed: Charles O. Rock, Department of Infectious 11 Diseases, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 12 38105, USA. Tel.:901-945-3491; Fax: 901-495-3099; E-mail: charles.rock@stjude.org. 13 14

ABSTRACT 15

Broad-spectrum antibiotic therapy decimates the gut microbiome resulting in a variety of 16

negative health consequences. Debio 1452 is a staphylococci-selective enoyl-acyl carrier 17

protein reductase (FabI) inhibitor under clinical development, and was used to determine if 18

treatment with pathogen-selective antibiotics would minimize disturbance to the microbiome. 19

The effect of oral Debio 1452 on the microbiota of mice was compared to four commonly used 20

broad-spectrum oral antibiotics. During the 10 days of oral Debio 1452 treatment, there was 21

minimal disturbance to the gut bacterial abundance and composition with only the unclassified 22

S24-7 taxa reduced at days 6 and 10. In comparison, broad-spectrum oral antibiotics caused a 23

∼100-4,000 fold decrease in gut bacterial abundance and severely altered the microbial 24

composition. The gut bacterial abundance and composition of Debio 1452-treated mice was 25

indistinguishable from untreated mice 2 days after antibiotic treatment stopped. In contrast, the 26

bacterial abundance in broad-spectrum antibiotic-treated mice took up to 7 days to recover, the 27

gut composition of the broad-spectrum antibiotic-treated mice remained different from the 28

control group 20 days after the cessation of antibiotic treatment. These results illustrate that a 29

pathogen-selective approach to antibiotic development will minimize disturbance to the gut 30

microbiome. 31

32

AAC Accepted Manuscript Posted Online 9 May 2016Antimicrob. Agents Chemother. doi:10.1128/AAC.00535-16Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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INTRODUCTION 33

The discovery and commercialization of broad-spectrum antibiotics is one of the most 34

significant medical achievements in the first half of the 20th century. However, broad-spectrum 35

antibiotic use is linked to a variety of medical problems such as antibiotic treatment-associated 36

infections first recognized in the 1950’s (1, 2). Broad-spectrum antibiotic therapy is known to 37

devastate the gut microbiome and the repeated use of these antibiotics during early childhood is 38

linked to metabolic and autoimmune diseases later in life (3-10). Therefore, one challenge for 39

future antibacterial therapeutic development is to design drugs that minimize disturbances to the 40

microbiome. 41

One approach to minimize disturbance to the microbiome is to use narrow-spectrum, 42

pathogen-selective antibiotics. In principle, antibiotics optimized to target a single pathogen 43

would not impact the beneficial inhabitants of the gut. There are two ways to achieve pathogen 44

selectivity. First, design an inhibitor against an enzyme or process that is only found in the 45

targeted pathogen. This approach has the challenge of identifying a novel, essential target in 46

each pathogen. A second approach circumvents this issue and utilizes structure-based design 47

to build high-affinity inhibitors that are optimized against the specific version of the drug target 48

expressed in the pathogen. One antimicrobial target that has been exploited for the 49

development of a pathogen-selective inhibitor is enoyl-acyl carrier protein (ACP) reductase 50

(FabI) (11-16). Debio 1452, previously known as AFN-1252 (Fig. 1), is an example of a FabI 51

inhibitor with the properties of a pathogen-selective antibiotic based on both target distribution 52

and isozyme selectivity (17). Debio 1452 is specifically designed to target staphylococcal FabI, 53

a key component of bacterial fatty acid synthesis (18-20). FabI is essential in Staphylococcus 54

species (11), but is not found in many other groups of bacteria. Specifically, members of the 55

order Clostridiales are abundant residents of the gut, and free-living members of this group do 56

not encode a FabI, but rather utilize an unrelated enoyl-ACP reductase called FabK (21, 22). 57

Debio 1452 is also highly-selective for staphylococcal FabI compared to the essential FabIs 58

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expressed in other bacteria due to a specific drug interaction with an active site methionine that 59

is unique to staphylococcal FabI (12, 23). Consequently, Debio 1452 has potent activity against 60

S. aureus and other staphylococci spp. including multi-drug resistant isolates, but does not 61

inhibit the growth of many other Gram-positive or Gram-negative genera (24). These 62

considerations suggest that Debio 1452 therapy may have minimal impact on the gut 63

microbiome. However, experimental validation of this idea is essential because most of the gut 64

inhabitants are known only from their 16S rDNA sequences, and whether or not they express an 65

essential FabI with S. aureus-like active site is unknown. In this study, we examined the effects 66

of therapeutic doses of Debio 1452 on the gut microbiome of mice as compared to a panel of 67

broad-spectrum antibiotics. As anticipated (2, 25, 26), the broad-spectrum agents devastated 68

the gut microbiome. However, Debio 1452 treatment caused minimal effects on both the 69

bacterial abundance and composition of the gut microbiome illustrating that pathogen-selective 70

antibiotics can be developed to minimize disturbances to the microbiome. 71

72

MATERIALS and METHODS 73

Mouse husbandry. Animal experiments were approved by the St. Jude Children’s 74

Research Hospital Institutional Animal Care and Use Committee (protocol 538). Mice were 75

housed in the St. Jude Children’s Research Hospital Animal Research Center at 22 ± 2°C, 50 ± 76

10% humidity, and 14 h light/10 h dark cycle. The animals were fed Purina Diet 5 chow. Six-77

week-old female C57-BL6 mice were ordered from Jackson Laboratory. The mice were 78

handled, received abdominal massage, and were mock gavaged twice weekly during the 79

acclimation period. Stool samples were collected from each mouse via abdominal massage 80

before antibiotic treatment started, daily during a 10-day antibiotic treatment, and on days 12, 81

17, 23, 30, and 37 during the post antibiotic recovery. Stool samples were stored are −80°C 82

until extraction. Antibiotics were purchased from Santa Cruz Biotechnology, except Debio 1452, 83

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which was provided by Debiopharm International SA. The antibiotic stock solutions were 84

formulated in 1% pluronic F-127 (Sigma Aldrich), and at 29.4 mg/ml for Debio 1452 tosylate 85

monohydrate salt, 40 mg/ml for linezolid, 40 mg/ml for clindamycin, 13 mg/ml for moxifloxacin, 86

and 48 mg/ml for amoxicillin. Antibiotic treatments were given once daily via oral gavage with 87

the stock antibiotic during the antibiotic treatment phase. The dose level of broad spectrum 88

antibiotics was based on the conversion of the human doses to body surface area (27), and 89

were: linezolid, 200 mg/kg; clindamycin, 200 mg/kg; moxifloxacin 65 mg/kg; and amoxicillin, 240 90

mg/kg. Debio 1452 dosage 100 mg/kg was based on previous mouse studies (28). The drug 91

carrier only (1% pluronic F-127) was given to the control group. 92

DNA extraction. The total DNA was extracted from 50-200 mg of stool sample using the 93

QIAmp Fast DNA Stool Mini Kit. The InhibitEX buffer (1 ml) was added to the stool sample in a 94

MP FastPrep Tube. The tube was heated at 70°C for 10 minutes, and then homogenized via 95

shaking in the MP FastPrep-24 machine (4.0 m/s for 20 seconds 3 times). The tube was heated 96

at 70°C for 10 more minutes and centrifuged to pellet stool particles. The rest of the extraction 97

process followed manufacturer protocols. Stool samples from days -1, 2, 6, 10, 12, 17, 23, 30, 98

and 37 were extracted for DNA and analyzed. 99

16S rDNA abundance determination. Real time PCR using the Applied Biosystems SYBR 100

Green PCR Master Mix in the Applied Biosystems 7500 Real Time PCR System was used to 101

determine the relative abundance of 16S rDNA in extracted DNA samples. A 20 μl reaction 102

composed of 10 μl 2X SYBR Green PCR Master Mix, 150 nM of each forward and reverse DNA 103

primers, and 5 μl of different concentrations of extracted DNA was amplified for 40 cycles. The 104

16S PCR primers were 5′-TCCTACGGGAGGCAGCAGT and 5′-105

GGACTACCAGGGTATCTAATCCTGTT (29). A serial dilution of DNA extracted from the stool 106

of mice before antibiotic treatment was tested to determine the DNA dilution that gave the best 107

dynamic range (cycle threshold between 15 and 20). A 1 to 2000 dilution of the extracted DNA 108

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was determined to give the best dynamic range for 16S rDNA detection, and used to determine 109

the cycle threshold for each of the samples. The resulting data was plotted in log 2 scale as 110

relative abundance for each mouse in each treatment group. 111

The abundance of the mouse TNFα gene (primers 5′- GGCTTTCCGAATTCACTGGAG and 112

5′- CCCCGGCCTTCCAAATAAA) was also measured in the samples by real time PCR as a 113

control to monitor the efficiency of the extraction (30). A 1 to 10 dilution of the extracted DNA 114

was used to determine the cycle threshold for each sample. The cycle threshold values for the 115

TNFα had a bell shaped distribution with a mean of 30.14 CT, median of 29.96 CT, and a range 116

of 24 to 36 CT. This normal distribution was reflected a similar efficiency of DNA extraction 117

across the samples. 118

16S rDNA Sequencing. The V1-V3 16S rDNA amplicon library was generated via PCR 119

using the NEXTflex 16S V1-V3 Amplicon-Seq Kit (Bioo Scientific) following manufacturer 120

instructions. The DNA from the PCR was cleaned using the Ampure XP PCR purification kit 121

(Beckman Coulter). The PCR products were quantified using the Quant-iT PicoGreen assay 122

(Illumina), and normalized by DNA concentration for sequencing. The samples were analyzed 123

via paired end sequencing using the Illumina MiSeq platform following manufacturer protocols. 124

Broad-spectrum antibiotic treatment caused a severe reduction in the number of read counts 125

obtained from Next Generation Sequencing corresponding to the reduction in 16S rDNA 126

abundance. These samples were analyzed using the same methodology because the reduction 127

of counts represents real changes in the gut microbiome composition. Sequencing read counts 128

were 100-fold lower than the cohort for Debio 1452 treatment mouse 5 on day 6, clindamycin 129

treatment mouse 3 on day 2, and moxifloxacin treatment mouse 5 on day 37. These samples 130

were considered sequencing failures and excluded from the analysis. 131

Taxa Assignment. The 16S primers targeting the V1-V3 regions were aligned to the full set 132

of 16S rDNA sequences from the Greengenes database v13.5 (31) using exonerate (32). Each 133

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16S rDNA database sequence was truncated to include only the V1-V3 region, the primer-134

matching regions, and an additional 40 bases on either side. Duplicate V1-V3 regions were 135

removed from the dataset to create a unique V1-V3 representative reference set. All taxa labels 136

from the removed duplicates were associated with the matching remaining representative 137

sequences. Reads from each sample were aligned exhaustively to the non-redundant V1-V3 138

reference set using USEARCH (33) allowing a minimum sequence identity of 97%. All taxon 139

labels associated with all top-scoring sequences were used to determine the taxon assignment 140

of each read. The highest resolution non-conflicting taxon from all taxa associated with the top-141

scoring V1-V3 region(s) was assigned as the taxon for a read. 142

Composition Analysis. The phylogenetic composition was analyzed in R version 3.1.1 143

using the package phyloseq version 1.8.2 (34). The assigned taxa for each sample was 144

tabulated and converted to percent composition for each taxon. The tabulated count numbers 145

data for each sample is organized by experiment day and treatment group, and is included in 146

the supplemental data (S1). The resulting tables for all the samples were converted into a 147

phyloseq class. Distance plots for the samples were generated using the ordinate function with 148

multidimensional scaling (“MDS”) as method choice and betadiversity measure 1 (“w”) as the 149

distance choice. The resulting principal components 1 and 2 were plotted as 2 dimensional 150

distance plots grouped by the treatment day. Bar charts were generated showing the family 151

level taxa composition for each treatment group for each treatment day by averaging the 152

percent composition of the different samples in each treatment group. 153

154

RESULTS AND DISCUSSION 155

Gut bacterial abundance and composition. Groups of 5 mice were treated orally with 156

drug carrier only (Control), Debio 1452, linezolid, clindamycin, moxifloxacin, or amoxicillin for 10 157

days, and then allowed to recover for 27 additional days. Treatment with Debio 1452, linezolid, 158

moxifloxacin and amoxicillin were well-tolerated with no notable distress or weight loss. Four 159

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mice in the clindamycin treatment group were distressed during the first four days, two 160

recovered, but two mice died on days 4 and 5. Feces from the mice were collected 1 day 161

before treatment and over the course of the experiment, and the total DNA was extracted from 162

the stool samples (Figure 2). The relative bacteria abundance was determined by performing 163

real time PCR amplifying the 16S rDNA from the total DNA (Figs. 3 and 4). The gut microbiome 164

composition was determined through Next Generation Sequencing of the V1-V3 region of the 165

16S rDNA from the total DNA. The gut microbiome compositions of all the samples were 166

compared using beta-diversity and multidimensional scaling analysis, and plotted as 2-167

dimensional distance plots of the first two principal components (Figs. 3 and 4). In these 168

distance plots, samples that are clustered together are more similar, while samples that are 169

more spatially distant are more different. The gut composition was also plotted as bar charts at 170

the family taxonomic level (Figs. 5-7). Distance plots were used to provide a high level 171

visualization of the similarities and differences in complex samples, whereas the bar charts 172

identified the distribution of taxa. 173

The mice from different treatment groups had similar bacterial abundance before drug 174

treatment. The samples from the different treatment groups obtained prior to treatment 175

clustered closely together in distance analysis, showing that the bacterial composition in all the 176

mice was similar before treatment. In terms of composition, the Rikenellaceae and S24-7 177

families (both of the Bacteroidia class) make up approximately 80% of the microbiome 178

composition before treatment in all groups and in the untreated mice over the course of the 179

experiment. The order Clostridiales including Ruminococcaceae, Lachnospiraceae, 180

Dehalobacteriaceae, Clostridiaceae, Mogibacteriaceae and unclassified members, make up 5-181

15% of the microbiome composition. Akkermansia muciniphila of the Verrucomicrobia phylum 182

averaged 1% of the microbiome composition. The RF39, Anaeroplasmataceae, 183

Lactobacillaceae, Turicibacteraceae, Coriobacteriaceae, and Erysipelotrichaceae families are 184

minor taxa making up 0.01-1% of the composition. The percent composition of individual taxa in 185

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the control mice were observed to vary over the course of the experiment and between the 186

individual mice, but the overall pattern of the taxa in the gut of untreated mice was similar over 187

the entire course of the experiment. 188

Debio 1452 treatment caused minimal disturbance to the gut microbiome. Debio 1452 189

treatment did not cause a significant change in the bacteria abundance over the course of the 190

10-day treatment (Fig. 3) or the subsequent 27-day recovery (Fig. 4). Distance analysis showed 191

that Debio 1452-treated mice clustered with the untreated mice over the course of experiment, 192

consistent with minimal changes in composition of the gut microbiome (Figs. 3 & 4). Only one 193

taxon, the S24-7 family, decreased following Debio 1452 treatment. The S24-7 family 194

comprised 30-50% of the bacteria before treatment, was reduced to an average of 4% by day 6, 195

and constituted only 0.2% of the bacteria by day 10 (Fig. 5). No other taxa had compositional 196

decreases, and several taxa, including the Rikenellaceae family, the Clostridiales order, and A. 197

muciniphila (Verrucomicrobiales order) exhibited compensating compositional increases to 198

maintain the bacterial abundance during the decline in S24-7 (Fig. 5). The drug treatments 199

were stopped after day 10, and the microbiome composition in the Debio 1452-treated mice was 200

evaluated on day 12. By day 12, the S24-7 family had recovered to pre-treatment levels in the 201

Debio 1452-treated mice, and the gut composition of Debio 1452-treated mice was 202

indistinguishable from untreated mice during the entire recovery phase (day 12-37) showing that 203

this FabI inhibitor did not have a lasting effect on the microbiome (Figs. 4 & 6). 204

Linezolid, clindamycin, and amoxicillin severely perturbed the gut microbiome. 205

Linezolid, clindamycin, and amoxicillin treatment all caused a ∼4,000-fold decrease in the gut 206

bacterial abundance at the second day of treatment that persisted until the end of the treatment 207

(Fig. 3). Distance plot analyses showed that the composition of linezolid, clindamycin, and 208

amoxicillin-treated mice became significantly distant from the control group during therapy, 209

consistent with the microbiome composition becoming severely altered (Fig. 3). In each case, 210

the remaining bacterial taxa were unique to the antibiotic used and represented those bacteria 211

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that survived the antibiotic treatment (Fig. 5). However, the persistent low bacterial abundance 212

over the course of treatment means that remaining bacterial taxa did not repopulate the gut 213

microbiome (Fig. 3). The major taxa in amoxicillin-treated mice at days 6 and 10 was the 214

unclassified order Clostridiales, suggesting that the bacteria from this order are relatively more 215

resistant to amoxicillin treatment compared to the other bacteria in the gut microbiome (Fig. 5). 216

The major taxa in clindamycin-treated mice at days 6 and 10 were the Pseudomonadaceae 217

family, suggesting that this family was better equipped than others to withstand clindamycin 218

treatment (Fig. 5). The major taxa in linezolid-treated mice had a similar distribution to the 219

untreated mice, suggesting that linezolid was equally effective at eliminating all of the significant 220

taxa of the gut microbiome (Fig. 5). 221

Although the drug treatments were stopped at day 10, the bacterial abundance did not 222

recover until day 17 for the linezolid and amoxicillin treatment groups, and day 23 for the 223

clindamycin treatment group (Fig. 4). The distance plots show that the composition became 224

distally closer to the control group over the recovery phase, but the clindamycin and amoxicillin 225

treatment groups still did not cluster completely with the control group by day 37 (Fig. 4). For all 226

three treatment groups, the Enterococcaceae family became a major taxon at day 12 (Fig. 6). 227

On the first day when the bacterial abundance reached normal levels in each of these cases 228

(Fig. 4), the S24-7 family became the major compositional component of the gut microbiome, 229

making up 80-90% of the composition (Fig. 6). These data illustrate that the S24-7 family was 230

the first normally observed taxon to recover in the gut microbiome following broad-spectrum 231

antibiotic therapy in mice. By day 37, the major taxa had recovered to normal levels in all three 232

cases. However, the minor taxa were still significantly different from the untreated mice in the 233

clindamycin and amoxicillin treatment groups (Figs. 4, 6 & 7). 234

Moxifloxacin-specific microbiome alterations. Of the broad-spectrum antibiotics tested, 235

moxifloxacin treatment caused the least reduction in the abundance of bacteria in the gut 236

microbiome (Fig. 3). Gut bacterial abundance was still significantly reduced, but only 16-100-237

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fold over the course of the moxifloxacin treatment compared to ∼4,000-fold by the other broad-238

spectrum antibiotics. Distance plot analysis showed that the microbiome composition in the 239

moxifloxacin-treated mice became significantly distinct from the control group (Fig. 5). The 240

percent composition of A. muciniphila increased over the course of the moxifloxacin treatment 241

from 0.5%-2% before treatment to 30% during day 2, 40% at day 6, and 50% at day 10 of 242

treatment. The 100-fold increase in A. muciniphila at day 10 of moxifloxacin therapy was not 243

due to an increase in this species, but was rather due to the 100-fold decrease in the other 244

species. The normal taxa constituted the other 50% of the microbiome composition at day 10, 245

showing that moxifloxacin did not reduce the normal gut inhabitants as much as the other 246

broad-spectrum antibiotics tested. The gut bacterial abundance of moxifloxacin treated mice 247

recovered 2 days after the treatment stopped, faster than for the other broad-spectrum 248

antibiotics tested (Fig. 4). The pattern of compositional recovery was similar to other broad-249

spectrum antibiotics, with S24-7 recovering first, the other major taxa returning to normal by day 250

30, and the minor taxa remaining unstable and continuing to fluctuate at day 37. 251

Pathogen-selective antibiotic strategy minimizes disturbances to the microbiome. 252

One of the challenges of microbiome studies is applying mouse results to humans. The drug 253

dosages used in this study were based on the equivalent surface area dosage conversion 254

method to achieve similar plasma levels of drugs (27). Mice received 12 times the drug per 255

mass compared to humans under this established conversion. Therefore, the concentration of 256

the drug in the mouse gut was higher than would be found in the human gut facilitating a 257

rigorous test of Debio 1452 and the pathogen selective approach for antibiotic discovery. The 258

minimal perturbation of the gut microbiome by Debio 1452 therapy illustrates that a pathogen-259

selective antibiotic strategy is an effective approach to minimize disturbance to the gut 260

microbiome. Debio 1452 derives its pathogen selectivity by targeting an enzyme that is either 261

absent or nonessential in many important residents of the gut, and by targeting the 262

staphylococcal FabI selectively over other FabI homologues. Debio 1452 did not change the 263

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gut bacterial abundance over the course of the treatment. The only taxa reduced by 264

Debio 1452 was the S24-7 family, which was replaced by an increase in the remaining taxa. As 265

reported previously (2, 25, 26), the broad-spectrum antibiotics in our study caused a 100-4,000-266

fold reduction in gut bacteria abundance and severely altered the microbiome taxa composition. 267

The gut composition of Debio 1452-treated mice was indistinguishable from untreated mice two 268

days after treatment had ceased, while the gut composition of broad-spectrum antibiotic-treated 269

mice took over 27 days to recover. Developing therapeutic strategies that minimize 270

disturbances to the beneficial microbiome bacteria will become a more important criterion in 271

antibiotic drug design in light of the emerging understanding of how the gut microbiome 272

supports health by facilitating digestion, shielding us from invading microorganisms, and 273

providing vitamins (3). 274

The principle inhabitants of the gut microbiome can bypass FabI inhibition. The 275

results of this study provide insight into the bacterial microbiome taxa that encode an essential 276

FabI. The essentiality of FabI and de novo fatty acid synthesis is well characterized in a variety 277

of human pathogens (11, 35, 36), but is largely unknown for the bacterial constituents of the gut 278

microbiome. Known members of the Clostridiales order encode for a FabK rather than a FabI 279

(21, 22), and our results support the prediction that the Clostridiales in the gut are unaffected by 280

FabI inhibition. It is more difficult to predict the behavior of the Bacteroidia class of organisms 281

because FabI and FabK are both found in organisms belonging to this class. Based on 282

sequenced genomes of family members (NCBI Microbial Genomes Resources), the 283

Prevotellaceae family encode only a FabI, the Porphyromonadaceae family encode only a FabK 284

(37), and the Bacteroidaceae and Rikenellaceae families encode both FabI and FabK. The 285

observation that Debio 1452 does not reduce the abundance of the Rikenellaceae family, which 286

is predicted to encode both FabI and FabK, indicates that the FabK enoyl-ACP reductase 287

compensates for a Debio 1452-inactivated FabI. Alternately, Debio 1452 may be a poor 288

inhibitor of Rikenellaceae FabI or this family may have a pathway to bypass fatty acid synthesis 289

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by obtaining fatty acids from the gut. The major impact of Debio 1452 therapy was the slow 290

disappearance of the unclassified S24-7 family of the Bacteroidia class. This family was 291

replaced by the expansion of the Clostridiales, but S24-7 returned to normal levels on day 2 292

following the cessation of treatment. These data suggest that the S24-7 family has an essential 293

FabI that is not potently inhibited by Debio 1452. Genomic data is unavailable for the S24-7 294

family, but the weak sensitivity to Debio 1452 may indicate that some Bacteroidia families may 295

be affected by FabI inhibitors. S24-7 is not a component of the human gut. Instead, the major 296

Bacteroidia species of the human microbiome belong to the Prevotellaceae and Bacteroidaceae 297

families. Whether the Prevoltellaceae have a FabI that is sensitive to Debio 1452 remains to be 298

established. A. muciniphila of the Verrucomicrobia phylum is a minor, but key component of the 299

human gut microbiome (38). A. muciniphila is predicted to possess a FabI with high homology 300

to the S. aureus FabI (WP_012420677.1, e-value of 2e-71) and does not harbor any known 301

alternative enoyl-ACP reductases. However, the abundance of A. muciniphila is not reduced by 302

Debio 1452 therapy suggesting that this organism has mechanisms to prevent the intracellular 303

accumulation of Debio 1452, expresses an unknown, alternate enoyl-ACP reductase, or 304

bypasses fatty acid synthesis inhibition by incorporating gut fatty acids derived from the diet. 305

The rare taxa of the gut microbiome, such as the Fusobacteria, Actinobacteria, and Mollicutes, 306

did not occur with sufficient frequency to draw conclusions regarding their FabI status. 307

Understanding fatty acid metabolism in the gut bacteria would advance our knowledge of how 308

FabI therapeutics, and fatty acid synthesis inhibitors in general, may impact the microbiome. 309

310

ACKNOWLEDGEMENTS 311

We would like to acknowledge Amy R. Iverson and Lois B. Richmond for animal handling. The 312

animals were housed in the Animal Research Center of St. Jude Children’s Research Hospital. 313

Next Generation Sequencing was performed by the Hartwell Center Genome Sequencing 314

Facility of St. Jude Children’s Research Hospital. 315

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G.V. and M.B. were employees of Debiopharm International SA and the research was funded in 316

part by Debiopharm International SA. 317

FUNDING INFORMATION 318

This work was supported by National Institutes of Health grant GM034496, a sponsored 319

research agreement with Debiopharm International SA, Cancer Center Support Grant CA21765 320

and the American Lebanese Syrian Associated Charities. 321

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FIGURE LEGENDS 436

Fig 1 Structure of Debio 1452. AFN-1252 was recently acquired by Debiopharm International 437 SA and is referred to clinically as Debio 1452. 438 439 Fig 2 Timeline of stool samples collected for analysis. 440 441 Fig 3 Relative 16S rDNA abundance (top plots) and distance analysis of beta-diversity 442 measures between the different antibiotic treatment groups (bottom plots) during 10 days of 443 therapy. (A) Day -1. (B) Day 2. (C) Day 6. (D) Day 10. 444 445 Fig 4 Relative 16S rDNA abundance (top plot) and distance plot of beta-diversity measures 446 between the different antibiotic treatment groups (bottom plot) during the recovery from therapy. 447 (A) Day 12. (B) Day 17. (C) Day 23. (D) Day 30. (E) Day 37. 448 449 Fig 5 Family level distribution of bacterial taxa during the 10 days of therapy. Top plot in each 450 figure set contains the high abundance taxa and the lower plot in each figure set contains the 451 low abundance taxa. Some sequences could not be assigned at the family level, and were 452 assigned at the order level. These taxa are denoted with [] bracketing the assignment (i.e. 453 [Clostridiales]). (A) Composition on Day -1, prior to therapy. (B) Composition on Day 2. (C) 454 Composition on Day 6. (D) Composition on Day 10. 455 456 Fig. 6. Family level distribution of bacterial taxa during recovery from therapy. The top plot in 457 each figure set contains the high abundance taxa and the lower plot in each figure set contains 458 the low abundance taxa. Some sequences could not be assigned at the family level, and were 459 assigned at the order level. These taxa are denoted with [] bracketing the assignment (i.e. 460 [Clostridiales]). (A) Composition on Day 12. (B) Composition on Day 17. (C) Composition on 461 Day 23. (D) Composition on Day 30. 462 463 Fig 7 Family level distribution of bacterial taxa 37 days after cessation of therapy. The top plot 464 illustrates the composition of the high abundance taxa and the lower plot shows the distribution 465 of low abundance taxa at day 37 post-therapy. Some sequences could not be assigned at the 466 family level, and were assigned at the order level. These taxa are denoted with [] bracketing the 467 assignment (i.e. [Clostridiales]). 468 469

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