Gut Microbiome Dynamics Associated with Rifamycin Therapy for Latent Tuberculosis Infection: Findings from a Prospective Cohort Study

doi:10.21203/rs.3.rs-2313802/v1

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Gut Microbiome Dynamics Associated with Rifamycin Therapy for Latent Tuberculosis Infection: Findings from a Prospective Cohort Study

https://doi.org/10.21203/rs.3.rs-2313802/v1

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published 02 Nov, 2023

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Background: Latent tuberculosis infection (LTBI) treatment is an effective strategy to eliminate TB in low-incidence settings. Shorter LTBI regimens incorporating the antimicrobial class of rifamycins are designed to improve treatment completion rates. Recent evidence suggests that the rifamycins could induce irreversible gut microbiota changes that impact future anti-TB immunity.

Methods: To document the immediate effect of the rifamycins on the gut microbiota, we followed six patients with LTBI initiating four months of monotherapy with rifampin (4R; n=4) or three months of rifapentine in combination with isoniazid (3HP; n=2) and tracked recovery to baseline two months posttreatment completion. We collected stool samples parallel to the LTBI group from healthy volunteers (N=6) unexposed to the rifamycins. We used a questionnaire to collect diet, antibiotics, and lifestyle changes during follow-up. We profiled the gut microbiota using 16S rRNA amplicon sequencing (V1-V2 region).

Results: Rifamycin exposure resulted in a 4.24% decrease in alpha diversity, compared to a 3.27% decrease in the controls. While the change in alpha diversity was small and not statistically different from changes observed in controls, significant bacterial community dissimilarity correlated with treatment duration (R² = 0.269, P=0.041) and dose (R² =0.201, P = 0.001) were observed. This rifamycin-associated dysbiosis was characterized by a depletion of butyrate-producing taxa (Clostridium-XIVa and Roseburia) and expansion of potentially pathogenic taxa within the Firmicutes and Proteobacteria phyla. Recovery of the gut microbial composition was incomplete two months after treatment ended.

Conclusion: TB prophylaxis with the rifamycins induced minimal changes in the overall gut microbiota diversity but a significant shift in gut microbial composition. A larger clinical study with a longer follow-up time is necessary to confirm the extent to which the gut microbiota can recover from this rifamycin-induced dysbiosis to inform strategies to mitigate potential LTBI treatment sequelae.

Health sciences/Diseases/Infectious diseases/Tuberculosis

Health sciences/Medical research/Epidemiology

Biological sciences/Microbiology/Communities/Microbiome

Biological sciences/Microbiology/Antimicrobials

latent tuberculosis infection

3HP

rifamycins

gut microbiome

dysbiosis

Mycobacterium tuberculosis (Mtb) is a persistent pathogen that continues to cause significant disease burden. In 2019, worldwide an estimated 10 million people developed TB, and 1.4 million died from its consequences[1]. Upon exposure to Mtb, about 5% of recently infected individuals progress to primary TB disease[2]. Most people develop latent TB infection (LTBI), defined as clinically asymptomatic Mtb infection[3]. People with LTBI are estimated to have a lifetime risk of progression to TB disease of 5% − 15%[2]. However, this risk is higher for young children and immunocompromised individuals, such as people living with the human immunodeficiency virus (PLHIV)[3]. It is estimated that 25% of the world population has LTBI, a sizeable Mtb reservoir that continuously fuels TB disease globally[4]. In the absence of a vaccine that prevents Mtb infection or progression to TB disease if infected, protracted therapy with a combination of antimicrobials is currently our most effective TB control approach[1]. In low incidence settings such as the United States (US), targeted testing and treatment of LTBI is a cornerstone of the national strategy to eliminate TB, defined as one TB case per million population[5]. Shortening the duration of TB prophylaxis has been instrumental to meeting this TB elimination goal[6, 7]. By reducing treatment duration, we can improve treatment acceptability and completion rates[6]. Today we can treat non-HIV infected individuals with LTBI in 3–4 months with either a weekly course of a combination of isoniazid (INH) and rifapentine (RPT; 3HP) or daily rifampin for four months (RIF; 4R)[6]. A one-month regimen of INH and RPT is currently approved for people living with HIV[8]. The effectiveness of these shorter LTBI regimens is dependent on the use of the antimicrobial class, rifamycins (RIF and RPT)[7, 9].

The antimicrobials used to treat TB target mycobacterial species; however, the rifamycins inhibit transcription in most bacteria by binding to the beta subunit of RNA polymerases[10]. The rifamycins are preferably administered orally and metabolized in the liver to a microbiologically active 25-O-desacetyl metabolite that is eliminated in feces[11]. Emerging evidence suggests that anti-TB treatment (ATT) may trigger profound modifications to the gut microbiota (gut dysbiosis) that persists long after treatment is completed[12–14]. Nevertheless, we lack clinical studies documenting changes to the gut microbiota during treatment, compared to baseline, and track posttreatment recovery [14–16]. We describe a prospective study of LTBI patients initiating TB prophylaxis with 3HP or 4R. We also enrolled a community control group that we sampled parallel with the LTBI patients. We included this control group to isolate the independent effect of the rifamycins on the gut microbiota of the LTBI patients. To do this, we contrasted the longitudinal changes observed following exposure to the antimicrobial to gut microbiota changes resulting from the controls’ everyday lifestyle activities. We aimed to describe the immediate effect of the rifamycins on the gut microbiota over the three- to four-month course of the treatment and track recovery to baseline two months posttreatment completion.

Study setting and design

Our study was conducted at two Florida Department of Health TB clinics in Alachua and Miami-Dade Counties. Between February 2019 to October 2019, we enrolled 13 clients of the TB clinics diagnosed with LTBI who were candidates for LTBI treatment. The LTBI participants had tested positive for tuberculosis infection, using either the tuberculin skin test (TST) or interferon gamma release assay (IGRA) test. To determine eligibility for LTBI therapy, patients underwent a medical exam and chest radiography to rule out active TB disease³. The decision to offer treatment and the choice of regimen were per CDC guidelines for the treatment of LTBI¹⁸. Clients who accepted to initiate LTBI therapy with a rifamycin-based regimen (3HP or 4R) were eligible. Six healthy volunteers (controls) were purposively recruited in the Alachua County community during the same period. Healthy volunteers self-reported a negative TST or IGRA test or were born in the United States without a history of travel to a TB endemic country. All participants were 15–65 years of age, HIV-negative, and without a history of antibiotic use in the month before enrollment. We further excluded participants if they were diabetic, pregnant, or became pregnant during study follow-up, were breastfeeding, or on regular therapy for any chronic condition.

Study participation was for a maximum of six months and consisted of four visits [days 0, 30, 60, and 120] during treatment administered by Health Department TB clinics, with a final visit two months after treatment was completed [day 180]. Two participants treated with 3HP completed visits at days 0,30,60, and 90 while on therapy and a final visit on day 120. At enrollment, all participants received a kit that included a stool collection bucket, a 5ml screw top tube containing 3ml of RNAlater™ Stabilization Solution (AM7021), three ceramic beads (2.8 mm), a wooden stick, and instructions to collect about 1g of stool at home and ship to the University of Florida Emerging Pathogens Institute (EPI) in a prepaid bubble mailer, where they were immediately stored at -80⁰C. Both groups were asked to collect a baseline stool sample as soon as possible after enrollment and before taking the first dose of the LTBI therapy. Follow-up stool samples were collected at 30-day intervals, calculated from the date therapy was initiated for LTBI participants, or the date healthy volunteers collected their baseline stool sample, ± 7 days (Supplemental Table S1). The healthy volunteers were asked to provide follow-up stool samples at days 30, 60, 120, and 180.

DNA extraction and 16S amplicon sequencing

Stool samples were batched for DNA extraction. On extraction day, the samples were thawed at room temperature then kept on ice during processing. The color and consistency of each stool sample were recorded before proceeding with bacterial genomic DNA isolation using a modified Qiagen fecal DNA extraction protocol with initial mechanical lysis bead-beating step (Biospec Products), as previously described[17]. Procedures to profile the gut microbiota via amplification of the V1-V2 hypervariable regions of the bacterial 16S rRNA were performed at the EPI following standard protocols as previously described[18]. Equimolar amounts of the 16S libraries were pooled for paired-end sequencing (250 x 2) using the Illumina Mi-Seq platform (Illumina®, San Diego, CA).

Gut microbiota profiling

The paired-end sequencing reads were processed using the Callahan et al. analytic workflow[19] implemented in RStudio[20]. Briefly, after trimming, filtering, and dereplication, raw reads were clustered into amplicon sequence variants (ASVs) using DADA2[21] and assigned to a taxonomic class using the naïve Bayesian classifier method[22] and the Ribosomal Database Project (RDP) v16 training set[23]. We applied prevalence filtering to retain phyla if present in at least 5.0% of all samples, and all unclassified reads at the phylum level were filtered out.

Parent and Metabolite drug concentration measurement

The DNA extraction protocol was modified to recover the supernatant from the initial phosphate buffer wash of the stool samples. Briefly, all samples were homogenized by vortexing, and 0.75mL was transferred to a bead beater tube. We added 1mL of phosphate buffer and vortexed quickly to mix. The samples were then centrifuged for 5:00 minutes at 15.4 RCF. The supernatant from this initial wash step was transferred to labeled 2mL tubes and stored at -80^o C until drug concentration assays were performed using standard procedures developed in the UF Infectious Disease Pharmacokinetics Laboratory (IDPL). A modification of the IDPL assay was used to measure the concentrations of the parent rifampin, rifapentine, and their major metabolite, desacetylrifampicin and 25-O-desacetylrifapentine, respectively in PBS wash instead of plasma, using a validated liquid chromatography-tandem mass spectrometry (LC-MS-MS) assay on a Thermo Acella high-performance liquid chromatography system and a Thermo Ultra triple-quadrupole mass spectrometer with a Dell computer and the Thermo Xcaliburdata management system.

Statistical Analysis

All analyses were conducted using RStudio (version 1.1.463)[20]. ASVs randomly resampled to 33,582 sequence reads were used to describe the community composition and calculate each sample's alpha-diversity (Shannon diversity index). Paired non-parametric Wilcoxon tests were used to assess differences between sampling intervals (pretreatment vs. treatment, treatment vs. posttreatment, pretreatment vs. posttreatment) for both LTBI and control samples. Unpaired Wilcoxon tests were used to assess differences between LTBI and healthy volunteer samples across all sampling intervals. Paired Wilcoxon tests were conducted to identify differentially abundant ASVs between pretreatment, treatment, and post-treatment samples in the LTBI group. We used both the unweighted and weighted UniFrac distance as a phylogenetically-sensitive measure of the beta-diversity[24]. We used the computed distances as input for principal coordinate analysis (PCoA), with visualization conducted with the R package vegan[25]. We performed a permutational multivariate analysis of variance using the distance matrices (PERMANOVA), with 999 permutations, to test for statistically significant clustering using the adonis function[25]. The Benjamini-Hochberg (BH) procedure was used to adjust all p-values for multiple comparisons[26].

Ethical Clearance

The study protocol was reviewed and approved by the University of Florida (IRB201801385) and the Florida Department of Health (2018-057) Institutional Review Boards, respectively. Each participant provided written informed consent before all study activity. Participants received $125 in incentive; $25 for each stool sample provided.

Subjects Characteristics

After removing LTBI participants with incomplete data/samples, we retained six LTBI patients in the analysis. Six healthy volunteers completed all study activities as controls. Table 1 shows samples used in statistical analyses, stratified by baseline characteristics of participants. Sixty fecal samples, five from each participant, were included. Participants were between 21–58 years old, although LTBI participants were younger (median age = 31 years) than the controls (median age = 35 years). The study subjects were predominantly of non-Hispanic ethnicity. While equal numbers were of non-US birth origin, participants with LTBI were born in countries where TB is more prevalent, consistent with TB epidemiology in low incidence settings, such as the United States[27]. Overall, the 'subjects' BMI at baseline ranged from 18.8–38.1, with similar BMI scores observed in the LTBI and healthy volunteer groups (Table 1).

Table 1

Number of stool samples analyzed stratified by group and key baseline characteristics of study participants.
Groups	Number of Participants	Stool samples	Baseline Characteristics
Groups	Number of Participants	Stool samples	Female Sex	Age in Years (range)	Non-US birth origin*	Hispanic Ethnicity	BMI (range)**
All subjects	12	60	7	21–58	8	4	18.8–38.1
3HP	2	10	1	31–31	1	-	20.8–38.1
4R	4	20	4	22–54	3	3	18.8–30.0
Controls	6	30	2	21–58	4	1	23.3–34.5

Notes: *Ecuador, Peru, Pakistan, Cuba, Germany (2), Russia, Philippines. **BMI was calculated using baseline weight and height. LTBI participants were weighed, and their height assessed in the TB clinic. Controls self-reported their weight and height.

We generated 6,407,447 16S sequencing reads with an average of 97,703 ± 20,189 reads per LTBI patient sample and 115,878 ± 31,784 reads per healthy volunteer sample. After trimming, filtering, dereplicating, and denoising, the reads were classified into 6,333 ASVs. Finally, we randomly resampled to an even sequencing depth to retain 5,612 ASVs and all 60 samples for statistical analyses. The gut microbial composition for LTBI participants (AL002 – MD001) at each of the five time points sampled is shown in Figure S1. The thirty control samples (HC001-HC006) are also shown to reflect non-interventional dynamics when the gut microbiome is sampled longitudinally. The most prevalent phyla across all participant samples were Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. We observed the phylum Tenericutes at 5% prevalence in all samples collected from three of the LTBI cases (AL012, AL003, and MD001) but none of the healthy volunteer samples.

Immediate effect of the rifamycins on the gut microbiota

We determined the immediate effect of the rifamycins on the intestinal microbiota by comparing the Shannon diversity index in stool samples collected before initiating therapy and on three different occasions while on treatment. We averaged the Shannon index values for the three samples taken during LTBI treatment to create one aggregate time point for the treatment interval comparable to the pretreatment time point. The mean species diversity in the fecal microbiota of LTBI patients differed from that of healthy volunteers' pretreatment, during treatment, and posttreatment completion (Fig. 1). Alpha diversity decreased by 4.24% (4.426 vs. 4.625; P = 0.063) following rifamycin exposure (Table S2). In comparison, a 3.27% decrease in alpha diversity was observed in the healthy volunteers sampled over the same period (Table S2). The alpha diversity over each of the five samples collected from each participant is shown in Supplementary Fig. 8. In the LTBI patients, a significant decrease in alpha diversity could be observed at month one of treatment compared to pre-treatment. In the controls, alpha diversity did not significantly change across the time course. Four participants reported antibiotic (AL012, AL005, and HC004) and probiotic (HC004) use during follow-up. The Shannon index pretreatment, during treatment, and posttreatment completion did not change significantly when the samples from these participants were dropped from the analyses (Figure S5). We evaluated differences in community composition at baseline compared to treatment for LTBI and healthy volunteers separately using the unweighted and weighted UniFrac distance between samples. On average the mean UniFrac distance measured in stool samples taken from LTBI patients (0.222, SD = 0.076) was larger than that observed in controls (0.135, SD = 0.025) and this difference was statistically significant (difference in mean UniFrac Distance = 0.087, p = 0.0239). Mapping the differences in the bacterial community by sampling interval and treatment regimen showed significant community dissimilarity correlated with the duration (Fig. 2A; R² = 0.269, P = 0.041) and dose (Fig. 2B; R² = 0.201, P = 0.001) of rifamycin exposure. In contrast, we did not observe significant community dissimilarity when mapping the UniFrac distances by sampling interval for control samples only (Figure S2; R² = 0.019, P = 1). This observation supports the conclusion that bacterial community dissimilarity was driven by rifamycin exposure and not an artifact of the prospective stool sampling design. Except for the non-significant correlation between treatment duration and community dissimilarity, the results remained consistent when we repeated these analyses after removing observations from the four participants who reported antibiotic and probiotic use during study follow (Figures S6 and S7). The relative abundance of eight bacterial taxa in the Firmicutes and Proteobacteria phyla were found to have changed following rifamycin exposure (Table S3). Within the Firmicutes phylum, the counts of the genera Clostridium_XIVa, Romboutsia, and Roseburia were depleted during treatment compared to pretreatment, while Clostridiales, Coprococcus, Lachnospiraceae, and Ruminococcaceae increased in numbers. The relative abundance of the genus Parasutterella within the phylum Proteobacteria also increased after exposure to the rifamycins. In Supplementary Fig. 4 we show a graphical summary of the gut microbiota changes observed in LTBI compared to control described above.

Intestinal microbiota recovery two months after treatment completion

We employed the same statistical methodology described above to investigate the extent to which the gut microbiota recovers following exposure to the rifamycins. In LTBI patients, the Shannon diversity index increased two months after posttreatment completion. However, the values remained lower than pretreatment numbers (Fig. 1). On the other hand, healthy volunteers had a posttreatment Shannon diversity index comparable to the pretreatment time point (Fig. 1). Since we observed a modest change in Shannon diversity index and UniFrac distance from baseline in both groups, we used the percent change estimate (Table S4) to determine the magnitude and direction of change in relative abundance posttreatment, compared to baseline. Five phyla (Candidatus_Saccaribacteria, Cyanobacteria/Chloroplast, Elusimicrobia, Fusobacteria, and Synergistetes) remain unchanged pre-, during, and posttreatment in all participants. In four LTBI patients (66.6% or 4/6), Actinobacteria increased in relative abundance two months posttreatment completion, compared to pretreatment. Two LTBI patients (AL002 and AL007) who had a decrease in Actinobacteria from pretreatment reported gastrointestinal side effects during therapy. AL007 took probiotics as a result; however, the probiotic use was concurrent with the daily dose of 600 mg of RIF. AL002 did not report any remedial behavior or diet changes. Actinobacteria decreased substantially in relative abundance for all healthy volunteers, except for two; HC001 switched from vegetarian to eating fish during follow-up, while HC006 reported drinking more alcohol during the posttreatment interval. The phylum Verrucomicrobia decreased substantially in relative abundance in four LTBI patients (AL003, AL005, AL012, MD001) but remained unchanged or increased in the controls. We observed a two-fold and six-fold increase in the relative abundance of Tenericutes in AL012 and AL002, respectively. These participants were both prescribed 3HP by direct observation. A decrease in Tenericutes was observed in other LTBI participants, but the numbers were unchanged for HC002, HC003, HC005, and HC006, decreased by 100% in HC004 while increasing by 58% in HC001. Overall, some taxa depleted in numbers upon rifamycin exposure increased in relative abundance two months after treatment completion; however, the gut microbiota remained different from baseline. Two LTBI patients (AL002 and AL012) had trace concentrations of the parent rifamycin, and one patient (AL005) had trace concentrations of the partially active metabolite in the stool samples collected two months posttreatment completion, which may explain the incomplete gut microbiota recovery to baseline (Table S5).

Although effective, current ATT carries a significant risk of gastrointestinal and neurological side effects[11]. Another potential important side effect of ATT could be disruption to the host microbiome or dysbiosis[28]. We profiled the gut microbiota of LTBI patients initiating TB disease prophylaxis with 3HP or 4R. We found that the rifamycins induced a modest decrease in alpha diversity but a significant shift in beta diversity correlated with the treatment duration and dose. In our healthy volunteer group unexposed to the rifamycins, we observed a modest decrease in alpha diversity but no significant shift in beta diversity. These findings are consistent with prior reports that ATT induces immediate and significant gut microbial dysbiosis[12, 13]. In our study, patients with LTBI received either 600 mg of RIF daily for four months or 900mg of RPT and 600mg of INH weekly for three months. Standard therapy for TB disease is much longer and uses a combination of at least four antimicrobials daily during the first two months of the 6-month minimum therapy[11]. These differences in dosage and duration of the anti-TB drugs may account for the modest effect of the LTBI therapy on the gut microbiota we observed here, in comparison to other studies done in TB patients treated with combination therapy for a longer period of time[13, 14, 16, 29].

The gut microbiota plays a crucial role in health[30]. Cross-talk between a healthy gut and the lung microbiota via the gut-lung axis is hypothesized to support the host in fending off challenges by respiratory pathogens, such as Mtb[31]. We observed that LTBI patients differed from healthy volunteers on the relative abundance of Tenericutes. However, there was no significant difference in Shannon diversity between the two groups. This observation contrasts with a limited number of prospective studies showing that Mtb infection itself results in gut dysbiosis, manifested as a decrease in bacterial diversity[14, 16]. However, because substantial inter-individual variation in gut microbiota is expected, a comparison of the gut microbial diversity of LTBI versus healthy volunteers is unlikely to tell us much about the effect of Mtb infection on gut dysbiosis[32]. None of our LTBI participants were treated because of recent exposure to an infectious TB case (i.e., recent contact); assuming an effect of Mtb infection, the gut microbiota composition we observed might better be described as a stabilized version of the "pre-Mtb" gut microbiota.

We observed that while TB prophylaxis with the rifamycins resulted in a modest decrease in the mean Shannon diversity index during treatment, alpha diversity increased two months posttreatment completion compared to pretreatment. In particular, the bacterial families Lachnospiraceae (Genus Coprococcus), Ruminococcaceae, and the genus Parasutterella increased in relative abundance during treatment. In contrast, the relative abundance of Roseburia (Family Lachnospiraceae) decreased during treatment. Our observations suggest that treatment is reverting the alpha diversity to a pre-Mtb state. Indeed, treatment naïve TB patients have previously been shown to differ from healthy controls on the relative abundance of Ruminococcaceae and Lachnospiraceae, which are characterized by their anti-inflammatory capacity as well as their ability to utilize carbohydrates in simple and polymeric forms to produce short-chain fatty acids (SCFAs) essential in the maintenance of health[28]. Although our numbers are too small to test this formally, prior reports suggest that a low level of Ruminococcaceae at baseline is associated with antibiotic-induced diarrhea[33]. Two LTBI patients who reported gastrointestinal discomfort upon treatment initiation saw a resolution of their symptoms once treatment was completed. However, it is important to note that one patient reported the use of probiotics to alleviate these GI symptoms. We were able to observe a partial recovery of the gut microbiota to baseline two months after rifamycin-based LTBI therapy was completed in some patients. This finding of an incomplete recovery of the gut microbiota following antimicrobial treatment is consistent with prior reports with longer follow-up[34, 35].

This study provides initial findings on the effect of TB prophylaxis with RIF or RPT on the gut microbiota. However, our results should be evaluated with caution, considering that our study sample is small. Also, we followed our study participants through the Thanksgiving and Christmas holidays and the first few weeks of stay-at-home orders to control COVID-19 spread. Participants reported eating and drinking alcohol more heavily during these times. LTBI patients were advised not to consume alcohol during therapy to limit liver injury[36]. One LTBI patient on 4R reported they also eliminated dairy during treatment to mitigate gastrointestinal side effects. However, volunteers were not instructed to modify their diet or lifestyle. Two healthy volunteers reported taking other drugs at the posttreatment time point; one took Zyrtec® for allergies while the other took amoxicillin. Similarly, one LTBI patient took amoxicillin during the two-month posttreatment follow-up period. Alcohol and other drastic diet changes have been shown to alter the composition and diversity of the gut microbiota[37, 38]. A similar effect in this study may have contributed to the control gut microbiota resembling more the gut microbiota of the treated LTBI patients, thus resulting in a smaller overall impact of the rifamycin exposure[32, 39]. In addition, exposure to amoxicillin during the two months posttreatment follow-up may have further confounded our estimates of the gut microbial reversal to baseline for one LTBI participant. Finally, we discontinued follow-up two months after LTBI treatment was completed, which likely was too short of a follow-up time to document complete reversal to baseline for all LTBI patients[35]. In particular, three out of six LTBI patients had trace concentrations of the parent rifamycins or their partially active metabolites in the stool samples collected two months after taking their last treatment dose. The rifamycins are primarily eliminated in the stool and to a lesser extent in urine[11]. In these patients, the two-month stool may have represented delayed clearance of the rifamycins and confounded our estimate of posttreatment recovery.

In conclusion, important literature on the synergistic interplay between the human gut and lung microbiome on the prevention and control of TB is slowly emerging, but questions remain. Our study adds to this literature by investigating the effect of TB prophylaxis with 3HP or 4R on the diversity and composition of the gut microbiota. We showed a small decrease in alpha diversity following exposure to the rifamycins that was not significantly different from gut dysbiosis observed in a shadow control population sampled in parallel. We also documented a reversal of the gut microbial community to baseline two months posttreatment completion, albeit this recovery was incomplete. These findings need to be confirmed in a larger study where patients are followed for longer than two months posttreatment completion.

3HP three months of isoniazid and rifapentine

4R four months of rifampin

ASV amplicon sequence variant

DNA deoxyribonucleic acid

EPI emerging pathogens institute

HIV human immunodeficiency virus

IDPL infectious disease pharmacokinetics laboratory

IGRA interferon gamma release assay

INH isoniazid

LC-MS-MS liquid chromatography-tandem mass spectrometry

LTBI latent tuberculosis infection

Mtb Mycobacterium tuberculosis

PLHIV people living with HIV

RIF rifampin

RNA ribonucleic acid

RPT rifapentine

rRNA ribosomal ribonucleic acid

TB tuberculosis

TBI tuberculosis infection

TST tuberculin skin test

Ethics approval and consent to participate

The research described involves human participants, human material, and human data. All experiments were performed in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the University of Florida (IRB201801385) and the Florida Department of Health Institutional Review Boards (2018-057). Informed consent for study participation was obtained directly from each participant prior to all study activities.

Consent for publication

Not applicable

Availability of data and materials

The data described in this Data note can be freely and openly accessed on under 10.6084/m9.figshare.21381930. The raw amplicon sequences can be freely and openly accessed on NCBI under accession number PRJNA772261. Please see table 1 for details and links to the data.

Competing interests

The authors declare that they have no competing interests

Funding

This study was supported by the University of Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Translational Sciences under award numbers UL1TR001427, KL2TR001429, and TL1TR001428. MNS completed award KL2TR001429 and is now supported by award K01AI153544.

Authors’ contributions

MNS, CAP, and VM contributed to conceptualization and design of the study. JB, MU, FGS contributed to data collection. MNS organized and curated the data and EK conducted formal analyses. JB conducted literature search and MNS wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version of the manuscript.

Acknowledgements

We thank the staff of the Florida Department of Health (FDOH) in the counties where our patient population was diagnosed and received care. We thank Dr. Massimiliano Tagliamonte for technical assistance with 16S data analysis, Dr. Mohammad H. Alshaer for assistance with the rifamycin parent and metabolite drug concentration data, Dr. Josephine Clark-Curtiss for her feedback on the initial draft manuscript, and Mr. Michael Asare-Baah additional editorial assistance with later drafts.

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Gut Microbiome Dynamics Associated with Rifamycin Therapy for Latent Tuberculosis Infection: Findings from a Prospective Cohort Study

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Abstract

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Introduction

Methods

Study setting and design

DNA extraction and 16S amplicon sequencing

Gut microbiota profiling

Parent and Metabolite drug concentration measurement

Statistical Analysis

Ethical Clearance

Results

Subjects Characteristics

Immediate effect of the rifamycins on the gut microbiota

Intestinal microbiota recovery two months after treatment completion

Discussion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

The authors declare that they have no competing interests

Funding

Authors’ contributions

Acknowledgements

References

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Supplementary Files

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