Minimal Impact on the Resistome of Children in Botswana After Azithromycin Treatment for Acute Severe Diarrheal Disease

Allison K Guitor; Anna Katyukhina; Margaret Mokomane; Kwana Lechiile; David M Goldfarb; Gerard D Wright; Andrew G McArthur; Jeffrey M Pernica

doi:10.1093/infdis/jiae049

. 2024 Jan 31;230(1):239–249. doi: 10.1093/infdis/jiae049

Minimal Impact on the Resistome of Children in Botswana After Azithromycin Treatment for Acute Severe Diarrheal Disease

Allison K Guitor ^1,^2,³, Anna Katyukhina ^4,^5,⁶, Margaret Mokomane ^7,⁸, Kwana Lechiile ⁹, David M Goldfarb ^10,¹¹, Gerard D Wright ^12,^13,^14,^✉,², Andrew G McArthur ^15,^16,¹⁷, Jeffrey M Pernica ^18,^19,^20,^✉

¹ Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada

² Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

³ David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

⁴ Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada

⁵ Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

⁶ David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

⁷ School of Allied Health Professions, Faculty of Health Sciences, University of Botswana, Gaborone, Botswana

⁸ Botswana National Health Laboratory, Gaborone, Botswana

⁹ Botswana-University of Pennsylvania Partnership, Gaborone, Botswana

¹⁰ Botswana-University of Pennsylvania Partnership, Gaborone, Botswana

¹¹ Department of Pathology and Laboratory Medicine, British Columbia Children's Hospital, Vancouver, British Columbia, Canada

¹² Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada

¹³ Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

¹⁴ David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

¹⁵ Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada

¹⁶ Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

¹⁷ David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

¹⁸ Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

¹⁹ Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada

²⁰ Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

^✉

Correspondence: Gerard D. Wright, PhD, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, HSC3N28, Hamilton, ON L8S 4L8, Canada (wrightge@mcmaster.ca). Jeffrey M. Pernica, MSc, MD, FRCPC, DTMH, Health Sciences Centre 3A, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada (pernica@mcmaster.ca).

Potential conflicts of interest . J. M. P.'s institution has received grant funding from medImmune and Merck for a study outside the submitted work. All other authors report no potential conflicts.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

PMCID: PMC11272098 PMID: 39052715

Abstract

Background

Macrolide antibiotics, including azithromycin, can reduce under 5 years of age mortality rates and treat various infections in children in sub-Saharan Africa. These exposures, however, can select for antibiotic-resistant bacteria in the gut microbiota.

Methods

Our previous randomized controlled trial (RCT) of a rapid-test-and-treat strategy for severe acute diarrheal disease in children in Botswana included an intervention (3-day azithromycin dose) group and a control group that received supportive treatment. In this prospective matched cohort study using stools collected at baseline and 60 days after treatment from RCT participants, the collection of antibiotic resistance genes or resistome was compared between groups.

Results

Certain macrolide resistance genes increased in prevalence by 13%–55% at 60 days, without differences in gene presence between the intervention and control groups. These genes were linked to tetracycline resistance genes and mobile genetic elements.

Conclusions

Azithromycin treatment for bacterial diarrhea for young children in Botswana resulted in similar effects on the gut resistome as the supportive treatment and did not provide additional selective pressure for macrolide resistance gene maintenance. The gut microbiota of these children contains diverse macrolide resistance genes that may be transferred within the gut upon repeated exposures to azithromycin or coselected by other antibiotics.

Clinical Trials Registration

NCT02803827.

Keywords: antibiotic resistance, bacterial diarrhea, azithromycin

This study compared azithromycin treatment for bacterial diarrhea in children in Botswana to the usual care to evaluate its impact on antibiotic resistance in the gut microbiome. In both treatment groups, azithromycin resistance genes increased in prevalence at 60 days.

In 2020, 2.7 million children under the age of 5 years died in sub-Saharan Africa [1]. Infectious diseases are associated with almost half of the global deaths in children aged under 5 years, with lower respiratory infections, diarrhea, and malaria among the leading causes [2]. Diarrhea, or acute gastroenteritis, is the third-leading cause of disease burden in children aged under 10 years and is associated with significant morbidity, including cognitive maldevelopment and stunted linear growth [3–6]. Viruses (ie, rotavirus), parasites (ie, Cryptosporidium spp), and bacteria (ie, Shigella spp) are the most common etiological agents of this disease [4]. Despite decreases in childhood mortality and diarrhea-related deaths in the past 30 years, resulting from the implementation of oral rehydration and zinc treatment, supplemental approaches to reducing this burden are needed [1, 7, 8]. Although generally reserved for dysentery or cholera, targeted treatment of bacterial gastroenteritis with the antibiotic azithromycin, perhaps through a rapid testing approach, is associated with a reduction in the duration of diarrhea, hospitalizations, and mortality, as well as benefits in linear growth [9, 10]. Additionally, the mass drug administration (MDA) of azithromycin has been recommended in certain countries with high mortality rates in children aged under 5 years to improve overall survival [11].

Azithromycin, a commonly used macrolide antibiotic, targets many bacterial pathogens associated with diarrhea and respiratory infections [12]. This antibiotic also targets the bacterial-like ribosome of the apicoplast, rendering it effective against malarial parasites [13]. There are obvious concerns with MDA and unnecessary antibiotic use, including unwanted impacts on the gut microbiota, selection for antibiotic-resistant organisms, and potential pressure for the mobilization and spread of antimicrobial resistance (AMR) [14–16]. Macrolide antibiotics target the 23S rRNA of the 50S large ribosomal subunit of bacteria and interfere with mRNA translation [12]. Resistance typically arises through methylation of the 23S rRNA through Erm methyltransferases, efflux, modification of the antibiotic, or mutations to the target [17]. While many studies have assessed the selection for AMR in cultured pathogens, few have investigated the impacts on the gut microbiota and total resistome (ie, collection of all antibiotic resistance genes [ARGs]) [14, 18–22]. It has been estimated that in 2019, over 250 000 deaths in sub-Saharan Africa were attributable to AMR, with about half occurring in children under the age of 5 years [23]. It is imperative to weigh the benefits of MDA of azithromycin and its indication to treat bacterial gastroenteritis against potential impacts on selection for AMR in the long term.

We completed a randomized controlled trial (RCT) of children with severe acute diarrheal disease in southern Botswana, comparing a rapid test-and-treat strategy (resulting in azithromycin treatment for those with treatable enteric bacterial pathogens) against supportive care (usual treatment) [10, 24]. We hypothesized that azithromycin exposure would select for an increase in the prevalence and abundance of macrolide ARGs in the gut microbiome of these children compared to those that received the usual care. Metagenomic DNA from stool samples before and 60 days after treatment was assayed with a targeted capture method to selectively sequence ARGs. The azithromycin-specific and total gut resistome of these children were assessed to identify potential consequences of antibiotic exposure.

METHODS

Study Design and Participants

A full description of the multicentre, randomized, controlled trial (NCT02803827) this prospective matched cohort study is derived from has previously been published [10]. Children hospitalized because of severe acute diarrhea were randomized to either the rapid test-and-treat (RTT, intervention) or usual care (UC, control) only group. All study participants, including those in the intervention group, received usual care, consisting of fluid rehydration and zinc treatment as per World Health Organization standards [25]. Each group was further randomized to either receive a probiotic supplementation of Lactobacillus reuteri DSM 17938 (1 × 10⁸ colony forming units by mouth) once daily or a placebo for 60 days [10]. In the RTT group, rectal swabs were assayed using multiplex polymerase chain reaction (PCR)-based qualitative panels, and those positive for Shigella, enterotoxigenic Escherichia coli, enteropathogenic E. coli, and/or Campylobacter, were treated orally with azithromycin (10 mg/kg once daily for 3 days). Those positive for Cryptosporidium were treated with nitazoxanide. A subset of 34 children from the RTT group that received azithromycin treatment and 34 children from the UC group were randomly selected for this study. Bulk stool samples were collected at baseline (before treatment) and 60 days after enrolment and kept at −80°C until further processing. When available, information on recent antibiotic exposures at the clinic or emergency department before enrolment in the RCT was obtained.

Targeted Sequencing of Antibiotic Resistance Genes

DNA was extracted from 0.1–0.2 g of stool as previously described [26, 27]. Full details on library preparation and enrichment for ARGs are described in Supplementary Methods. Enrichment for ARGs was performed as previously described using a probe set to target over 2000 ARGs [28, 29]. Enriched libraries were sequenced on an Illumina MiSeq with 2 × 300 bp sequencing chemistry to a targeted depth of 250 000 clusters per library. Ten negative controls of a buffer-only extraction blank were processed alongside the stool samples and 5 were sequenced after enrichment.

Analysis of Captured Antibiotic Resistance Genes

A full description of the analysis is in the Supplementary Methods. After subsampling reads, we used 2 analysis modules from the Resistance Gene Identifier (RGI): a read-mapping approach (RGI*bwt) and a de novo assembly and ARG prediction method (RGI*main). The former method estimates the number of reads that map to ARG sequences in the Comprehensive Antibiotic Resistance Database (CARD) version 3.2.1 [30, 31]. This method has difficulty resolving the specific variants of members within large AMR gene families (AGFs) but can infer the abundance of ARGs. AGFs are a higher classification of ARGs; for example, ermF (ARO: 3000498) and ermG (ARO: 3000522) are both members of the “Erm 23S ribosomal RNA methyltransferase” AGF in CARD. The latter method (RGI*main) can better resolve variants but cannot inform on the abundance of ARGs and relies on the assembly of contiguous sequences of DNA (contigs) of sufficient length to predict ARGs through comparison to CARD sequences.

We compared results between the 2 treatment groups at the ARG and the AGF levels. The prevalence of certain ARGs was determined from the RGI*main results and their abundance was estimated from the RGI*bwt results. For the 23S rRNA methyltransferases detected through RGI*bwt, the prevalence of predicted bacterial hosts from the RGI*kmer_query for prediction of pathogen of origin analysis module was compared across cohorts and timepoints. Finally, the surrounding genetic context of these methyltransferases, along with the macrolide phosphotransferases, was used to infer potential bacterial hosts and colocalized ARGs.

Ethics Approval

This study involves human participants and was approved by the Hamilton Integrated Research Ethics Board, the Botswana Ministry of Health and Wellness Health Research Development Committee Institutional Research Board, and the Princess Marina Hospital Research Ethics Committee. Participant's parents or legal guardians gave informed consent to participate in the study before taking part.

RESULTS

Patient Characteristics

Our study focused on a sample of 68 children from the original RCT [10] (Table 1). Approximately 50% of children in each arm of the study received L. reuteri probiotic for 60 days. Many children in each arm had stools in which bacterial pathogens were detected (Table 1). Three children tested positive for Cryptosporidium, in addition to a bacterial pathogen, and received nitazoxanide in addition to azithromycin. Many children in both arms of the study received antibiotics before enrolment, including cefotaxime, amoxicillin, co-trimoxazole, and ampicillin (Supplementary Material File 1). One child in the UC group received erythromycin before enrolment.

Table 1.

Participant Characteristics of Children Included in the Resistome Study

Characteristics	Usual Care (n = 34)	Rapid Test-and-Treat (n = 34)
Male	17 (50.0)	20 (58.8)
Age at baseline, y, average ± SD	0.988 ± 0.736	0.995 ± 0.505
Probiotic supplementation	16 (47.0)	16 (47.0)
Prior antibiotic exposure	14 (41.2)	7 (20.6)
Campylobacter positive	5 (14.7)	8 (23.5)
Shigella positive	3 (8.8)	4 (11.8)
Cryptosporidium positive	1 (2.9)	3 (8.8)
Enterotoxigenic Escherichia coli positive	10 (29.4)	11 (32.4)
Enteropathogenic Escherichia coli positive	16 (47.1)	25 (73.5)
Other pathogen positive	33 (97.1)	22 (64.7)
Azithromycin exposure	0 (0)	34 (100)

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Data are No. (%) except where indicated.

Capturing the Resistome of Children With Diarrhea

Using a method to selectively target and sequence over 2000 ARGs, we detected, on average, 76 to 81 ARGs per child through read mapping to CARD, and slightly fewer through the more stringent method of de novo assembly and RGI (66 to 75 ARGs on average per child) (Supplementary Figure 1B and 1D). Overall, we found no appreciable differences in the number of ARGs or AGFs between the 2 cohorts of children. Through the de novo assembly and RGI*main analysis, the number of ARGs per child increased on average by 5.2 genes for the UC cohort and 6.9 for the RTT cohort at 60 days (Supplementary Figure 1D). In general, the number of AGFs also increased from baseline to 60 days in both cohorts, that is an average increase of 3.8 (RGI*bwt) or 3.3 (RGI*main) AGFs for the RTT children and 2.4 (RGI*bwt) or 1.8 (RGI*main) AGFs for the UC children. We found these increases in ARGs and AGFs were significant only in azithromycin-treated children (Supplementary Figure 1C–E). There were many ARGs, but fewer AGFs, that were unique to one group; however, these were typically rare and only found in a few individuals (Supplementary Figures 2 and 3, and Supplementary Material Files 2 and 3).

Increase in the Prevalence of Macrolide Resistance Genes in Both Cohorts

At baseline, the macrolide resistome appeared similar across azithromycin-treated children and those who received usual care (Figure 1A and Supplementary Figure 4). There were distinct changes in the prevalence and abundance of 23S ribosomal RNA methyltransferases that confer resistance to macrolide, lincosamide, and streptogramin antibiotics. In both cohorts, the prevalence of ermQ, ermF, ermT, ermX, and ermG increased at 60 days by 13%–55% (RGI presence/absence results) (Figure 1). Many children maintained these genes from baseline to 60 days, while in some children they appeared during the study period (Figure 1B). We found no differences in the prevalence of these genes between cohorts at 60 days (Figure 1A). Before antibiotic treatment, the children in the RTT arm had more reads mapping to ermF than those in the UC arm (Supplementary Figure 5A). While both cohorts experienced an increase in reads mapping to ermF (average of 2259 for RTT and 1376 for UC), this gene remained more abundant at 60 days in the azithromycin-treated children (average of 4252 reads in RTT vs 2363 in UC) (Supplementary Figure 5). At 60 days, on average, more reads were mapped to ermT in the UC group (3348 for UC vs 2128 for RTT), which had increased from baseline (average reads of 1792 for UC vs 1757 for RTT) (Supplementary Figure 5). Whereas ermX read counts increased in the azithromycin-treated children by 60 days (average of 5409 reads and an increase of 3554), this gene was slightly decreased (average of 79 reads) in UC children at 60 days (Supplementary Figure 5).

Apart from the ribosomal methyltransferases, there were few changes in other macrolide resistance genes (Supplementary Figures 4 and 5). At baseline, the macrolide phosphotransferase mphA was more prevalent in the RTT arm (46%) than the UC arm (38%) and remained so at 60 days (50% for RTT vs 32% for UC) (Figure 1A). Finally, certain efflux systems (eg, cme genes found in Campylobacter spp and ade genes originating in Acinetobacter spp) were reduced in prevalence in both groups by 60 days (Figure 1, and Supplementary Figures 4 and 5).

Potential Bacterial Hosts of Macrolide Resistance Genes

We next predicted the bacterial hosts of the 23S rRNA methyltransferases, which were similar between the 2 treatments (Figure 2). Bacteroides spp were common for both ermF and ermG, while ermX likely originated in Bifidobacterium spp or Corynebacterium spp (Supplementary Material File 4). Interestingly, in both groups of children at 60 days, ermG was predicted in the additional host Klebsiella spp. A more diverse range of bacteria was detected for ermT, including Enterococcus faecium, Escherichia spp, Klebsiella spp, Staphylococcus spp, and Streptococcus suis. Additional potential hosts identified through the BLAST analysis included Clostridiodes spp for ermG, and Intestinibacter spp, Peptacetobacter spp, or Clostridium perfringens for ermQ (Supplementary Material File 4). We further annotated the genetic context of these genes using Prokka and identified ermTFX alongside insertion sequences in some children (Figure 3 and Supplementary Material File 4).

Figure 3. — Representative genetic context of selected macrolide resistance genes in children. Contiguous DNA sequences (contigs) obtained through *de novo* assembly and predicted to contain various macrolide resistance genes were annotated using Prokka. Any antibiotic resistance genes (ARGs) present within the same contig were identified through RGI*main. While a variety of genetic contexts were observed in both the usual care and rapid test-and-treat cohorts, a representative contig that was found in both groups is shown here as an example. Other contexts are described in Supplementary Material File 4.

Changes in Nonmacrolide Resistance Genes

In general, the gut microbiome of children in both cohorts acquired trimethoprim-resistant dihydrofolate reductase (dfr) genes (ie, dfrF) or their abundance increased at 60 days (Figure 4, and Supplementary Figures 6 and 7). More azithromycin-treated children acquired the β-lactamase bla_cfxA3 over time, and in both cohorts, there was a general increase in the number of reads mapping to members of the bla_cfxA family (Figure 4 and Supplementary Figure 7). Vancomycin resistance genes belonging to the vanG and vanC clusters, and the cepA β-lactamases increased in prevalence, whereas the prevalence of the bla_OXA gene family was reduced in the gut microbiome of children in both groups by 60 days (Figure 4 and Supplementary Figure 6). Many ARGs that are intrinsic to E. coli, including antibiotic efflux-associated systems (ie, mdtEFOP, acrDEF, emrKY), remained prevalent (70%–100%) in both groups at 60 days, although the number of reads mapping to these genes decreased (Figure 4 and Supplementary Figure 6).

Figure 4. — Prevalence of ARGs present in at least 10% of children in at least 1 time point and changes at 60 days. A, Heatmap showing the prevalence and maintenance of ARGs in each group of treated children. In the left panel, the values correspond to the percentage of children in each group at each time point for which a given ARG was detected through *de novo* assembly and RGI*main analysis. The right panel shows the percentage of children in which a gene appeared (+), disappeared (−), or was maintained (=) by 60 days in each treatment group. B, The log₂ fold change in non-macrolide ARG prevalence by 60 days in both groups (macrolide ARGs are omitted because they are included in Figure 1). Abbreviations: ARG, antibiotic resistance gene; AZI, azithromycin; Cc, *Campylobacter coli*; Ec, *Escherichia coli*; Lr, *Limosilactobacillus reuteri*; UC, usual care. Figure was generated in GraphPad Prism v 10.0.3.

From baseline to 60 days, there was an increase in the prevalence and abundance of genes encoding tetracycline-resistant ribosomal protection proteins (tetO, tetQ, tetS, tetB(P), tet32) in both groups (Figure 4, and Supplementary Figures 6 and 7). The gene, tetX, encoding a tetracycline inactivating monooxygenase, also increased in prevalence and abundance in both groups by 60 days (from 19% to 38% in the UC group and 14% to 44% in the RTT children) (Figure 4, and Supplementary Figures 6 and 7). When we assessed the genetic context of the Erm methyltransferases, we found that ermT was commonly associated with tetM and tetracycline efflux pumps, including tet(45) and tet(L). We also identified ermF on assembled DNA fragments containing tetQ and tetX (Figure 3).

DISCUSSION

Azithromycin treatment offers many benefits to children living in low- and middle-income countries, especially for those with acute diarrheal disease; however, the question remains whether these benefits would be obviated by resistance [9, 36]. Our study is, to our knowledge, the first to comprehensively investigate the effect of azithromycin on the gut resistome of children with severe acute diarrheal disease in a southern African context. Although the children that received azithromycin experienced a greater increase in the total number of AGFs by 60 days, this was not associated with a difference in macrolide resistance genes when compared to the children that received the usual treatment for diarrhea. It was observed that certain macrolide and nonmacrolide ARGs persisted or increased in prevalence both in the azithromycin-treated children and in those who did not receive azithromycin. Finally, certain macrolide resistance determinants were linked to insertion sequences and other ARGs highlighting their potential for mobilization and coselection. In this study of children under 5 years of age, we revealed the diversity of the gut resistome and showed that a 3-day exposure to azithromycin did not obviously provide additional pressure to retain macrolide resistance genes 2 months later.

We found a general increase in the number of AGFs in both groups of children; this appeared greater in the azithromycin-treated children and may reflect small interindividual differences. Given that these children presented to the hospital with acute gastroenteritis, their gut microbiota may have been dominated by the infection-causing organism, predominantly E. coli, at this time [37, 38]. While we noted a decrease in reads mapping to E.coli-associated ARGs by 60 days irrespective of treatment, they remained prevalent (>70%). As many of these genes are ubiquitous amongst E. coli, we cannot determine if they originated from commensal strains or the diarrhea-causing strain that may continue to be shed postinfection [38, 39]. Although we did not measure the gut microbial diversity of these children, it is possible that by 2 months, members of the genera Bacteroides and Prevotella became more abundant [37, 40, 41]. The increase in AGFs observed may reflect the appearance of these members of the gut microbiota that encode a variety of intrinsic ARGs distinct from those of diarrhea-causing pathogens [42].

Surveillance of macrolide resistance in Botswana is limited; however, a recent study found high levels of resistant bacteria in wastewater [43]. We identified a diversity of Erm 23S rRNA methyltransferases with similar prevalence in both groups of children at baseline that increased by 60 days, irrespective of the treatment received. By analyzing the genetic surroundings of these erm genes, we inferred they were likely encoded by bacteria often found in the gut microbiota and involved in the general restoration of the microbiome after a diarrheal infection; therefore, minor changes in erm genes in either treatment group are not likely related to an increase in diarrhea-causing bacteria in the gut microbiome [40, 41].

A macrolide phosphotransferase, mphA, was also prevalent in these children upon hospitalization. This gene slightly decreased in prevalence in the UC group of children and increased in the azithromycin-treated children; however, it was also more prevalent in this latter group at baseline. Therefore, it is difficult to associate the increase in prevalence with azithromycin treatment alone. What is concerning, however, is this gene's association with insertion sequence elements. Plasmids containing mphA have been identified in enterotoxigenic E. coli isolates and are common in bacteria circulating in wastewater and river environments in Botswana [43, 44].

Finally, many of the erm genes identified in this group of children were commonly associated with insertion sequences, mobile elements, and tetracycline resistance genes. Although both tetracycline ARGs and erm genes are often found in commensal gut bacteria, their ability to transfer within genera to potential pathogenic strains and risk of coselection is worrisome [45]. Tetracyclines and macrolides are prescribed less often than other antibiotics, such as cefotaxime and metronidazole, in Botswana [46]. Nevertheless, repeated exposure to either antibiotic might put these young children at future risk for the transfer of ARGs within their gut microbiota and the persistence of resistant strains.

In contrast to our study, the MORDOR trials to reduce child mortality assessed the impact of repeated MDA of azithromycin on resistance. They found longer-term effects, including decreased gut microbial diversity and increased macrolide resistance after 2 years of biannual administration [19–22]. Finally, after 4 years, they observed the selection of nonmacrolide resistance genes [20]. Although these studies used the more comprehensive method of metagenomic shotgun sequencing, they relied on pooled samples, which limit the ability to resolve individual-level changes in the resistome [20, 21]. Our approach of targeted metagenomics, however, can identify whether ARGs are appearing, disappearing, or maintained in individuals. The disparities between our results and those of MORDOR may relate to the resilience of the gut microbiome to short-term courses of antibiotics compared to repeated exposures, and major differences in trial design. In the MORDOR trials, whole communities were given azithromycin, while in our RCT, only children with bacterial gastroenteritis were treated [47].

The short-term use of azithromycin for the treatment of acute diarrhea in young children has also been assessed in a multicountry RCT [18]. The ABCD study group did not identify differences in antibiotic susceptibilities in bacterial isolates from participants who received azithromycin as compared to those who received placebo [18]. Their results highlight potential significant benefits (ie, less persistent diarrhea and mortality) of targeted antibiotic therapy when the etiological agent of acute gastroenteritis is bacterial [9]. Our current analysis of the impact on the gut resistome is suggestive that these important gains may be had without a significant increase in AMR, at least in the short term.

One limitation is that we only assessed stool samples at 2 time points; therefore, we may have missed changes in the gut resistome of these children immediately after azithromycin treatment and in the longer term. Many children in both groups also received other antibiotic treatments, but due to unreliable reporting and lack of electronic records in Botswana, we cannot characterize the impact of previous antibiotics on the gut resistome. It remains imperative to perform longitudinal sampling to fully characterize the gut resistome after antibiotic exposure. Shotgun metagenomics is still relatively expensive and may miss rare members of the resistome. A targeted sequencing method, such as the one used in our study, can capture over 2000 ARGs with minimal sequencing depth and cost and provides sensitive and high-quality resolution of the entire resistome. One limitation is the inability to capture resistance due to mutations in target genes (ie, 23S rRNA mutations). Bacteria encode variable copies of the 23S rRNA locus rrn; therefore, the extent of resistance due to mutations depends on the number of modified copies and may not be as relevant as other resistance mechanisms [17].

Although a short 3-day course of azithromycin for diarrhea did not have an appreciable selective effect on the resistome of children in our study, repeated exposures, such as MDA, risk further selection in an environment primed with resistance. We were surprised to identify such a diverse set of macrolide resistance genes in children at a young age and that these generally increased in prevalence in both cohorts regardless of the treatment they received. Bystander exposure to antibiotics, similarities in the recovery of the gut microbiome after diarrhea in both groups, and other unknown factors may have contributed to this result [48]. While the macrolide ARGs identified in this study were predominantly associated with gut commensal organisms, horizontal gene transfer to potential pathogens is still a concern. Despite azithromycin being a relatively cheap and effective intervention to reduce childhood mortality and treat bacterial gastroenteritis, we believe that the high prevalence of macrolide resistance in these populations, the linkage of these resistance genes with mobile elements, and the risk of repeated exposure to antibiotics to select for resistance highlight the need for surveillance of antibiotic resistance in cohorts treated with MDA and rapid diagnostics for targeted treatment of infections to prevent unwarranted antibiotic treatment.

Supplementary Data

Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

Supplementary Material

jiae049_Supplementary_Data

jiae049_supplementary_data.zip^{(1.3MB, zip)}

Contributor Information

Allison K Guitor, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.

Anna Katyukhina, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.

Margaret Mokomane, School of Allied Health Professions, Faculty of Health Sciences, University of Botswana, Gaborone, Botswana; Botswana National Health Laboratory, Gaborone, Botswana.

Kwana Lechiile, Botswana-University of Pennsylvania Partnership, Gaborone, Botswana.

David M Goldfarb, Botswana-University of Pennsylvania Partnership, Gaborone, Botswana; Department of Pathology and Laboratory Medicine, British Columbia Children's Hospital, Vancouver, British Columbia, Canada.

Gerard D Wright, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.

Andrew G McArthur, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.

Jeffrey M Pernica, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.

Notes

Author contributions . The randomized controlled trial from which the samples were selected was led by M. M., D. M. G., and J. M. P. M. M. and K. L. oversaw stool testing and storage. A. G. M., J. M. P., D. M. G., G. D. W., and A. K. G. conceived the study and designed the experiments. A. K. G. and A. K. performed the DNA extraction, library preparation, and targeted enrichments. A. K. G. analyzed the data, generated tables and figures, and wrote the manuscript with primary input from A. G. M., G. D. W., and J. M. P. All authors read and approved the final manuscript.

Acknowledgments. We are grateful to the families and children for their participation in this study. We thank Ms Banno Moorad for their contributions towards data collection, as well as Dr Joycelyne Efua Ewusie (McMaster University, Canada) and Dr David Speicher (Redeemer University, Canada) for their assistance in the early stages of planning this work. We acknowledge Michelle Shah for help with DNA extractions and the McMaster Genomics Facility for next-generation sequencing.

Financial support. This work was supported by the Canadian Institutes of Health Research (CIHR) (grant number FRN-148463 to G. D. W.). A. K. G. was supported by a CIHR Doctoral Research Award (grant number GSD-164145). A. G. M. holds the McMaster University inaugural David Braley Chair in Computational Biology. Computational support was provided by the McMaster University Service Lab and Repository computing cluster, supplemented by hardware donations and loans from Cisco Systems Canada, Hewlett Packard Enterprise, and Pure Storage. The randomized controlled trial was originally funded by Grand Challenges Canada and bioMérieux.

Data availability . Sequence data that support the finding of this study have been deposited in NCBI's Sequence Read Archive with BioProject accession code PRJNA980291. Code used to analyze the data is available at https://github.com/AllisonGuitor/AMR-metatools.

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1. UNICEF . Levels and trends in child mortality, report 2021. https://data.unicef.org/resources/levels-and-trends-in-child-mortality-2021/. Accessed 1 November 2022.
2. Perin J, Mulick A, Yeung D, et al. Global, regional, and national causes of under-5 mortality in 2000–19: an updated systematic analysis with implications for the sustainable development goals. Lancet Child Adolesc Health 2022; 6:106–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Troeger C, Forouzanfar M, Rao PC, et al. Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infect Dis 2017; 17:909–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Nasrin D, Blackwelder WC, Sommerfelt H, et al. Pathogens associated with linear growth faltering in children with diarrhea and impact of antibiotic treatment: the global enteric multicenter study. J Infect Dis 2021; 224:S848–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Rogawski ET, Liu J, Platts-Mills JA, et al. Use of quantitative molecular diagnostic methods to investigate the effect of enteropathogen infections on linear growth in children in low-resource settings: longitudinal analysis of results from the MAL-ED cohort study. Lancet Glob Health 2018; 6:e1319–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Mokomane M, Kasvosve I, de Melo E, Pernica JM, Goldfarb DM. The global problem of childhood diarrhoeal diseases: emerging strategies in prevention and management. Ther Adv Infect Dis 2018; 5:29–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Malande OO, Munube D, Afaayo RN, et al. Barriers to effective uptake and provision of immunization in a rural district in Uganda. PLoS One 2019; 14:e0212270. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Pavlinac PB, Platts-Mills J, Liu J, et al. Azithromycin for bacterial watery diarrhea: a reanalysis of the antibiotics for children with severe diarrhea (ABCD) trial incorporating molecular diagnostics. J Infect Dis 2024; 229:988–98. [Google Scholar]
10. Pernica JM, Arscott-Mills T, Steenhoff AP, et al. Optimising the management of childhood acute diarrhoeal disease using a rapid test-and- treat strategy and/or Lactobacillus reuteri DSM 17938: a multicentre, randomised, controlled, factorial trial in Botswana. BMJ Glob Health 2022; 7:e007826. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. World Health Organization . WHO guideline on mass drug administration of azithromycin to children under five years of age to promote child survival. https://apps.who.int/iris/handle/10665/333942. Accessed 1 November 2022. [PubMed]
12. Parnham MJ, Erakovic Haber V, Giamarellos-Bourboulis EJ, Perletti G, Verleden GM, Vos R. Azithromycin: mechanisms of action and their relevance for clinical applications. Pharmacol Ther 2014; 143:225–45. [DOI] [PubMed] [Google Scholar]
13. Burns AL, Sleebs BE, Gancheva M, et al. Targeting malaria parasites with novel derivatives of azithromycin. Front Cell Infect Microbiol 2022; 12:1063407. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. O'Brien KS, Emerson P, Hooper PJ, et al. Antimicrobial resistance following mass azithromycin distribution for trachoma: a systematic review. Lancet Infect Dis 2019; 19:e14–25. [DOI] [PubMed] [Google Scholar]
15. Keenan JD, Arzika AM, Lietman TM. Azithromycin and childhood mortality in Africa. N Engl J Med 2018; 379:1382–4. [DOI] [PubMed] [Google Scholar]
16. Hooda Y, Tanmoy AM, Sajib MSI, Saha S. Mass azithromycin administration: considerations in an increasingly resistant world. BMJ Glob Health 2020; 5:e002446. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Gomes C, Martinez-Puchol S, Palma N, et al. Macrolide resistance mechanisms in Enterobacteriaceae: focus on azithromycin. Crit Rev Microbiol 2017; 43:1–30. [DOI] [PubMed] [Google Scholar]
18. Antibiotics for Children With Diarrhea (ABCD) Study Group; Ahmed T, Chisti MJ, et al. Effect of 3 days of oral azithromycin on young children with acute diarrhea in low-resource settings: a randomized clinical trial. JAMA Netw Open 2021; 4:e2136726. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Arzika AM, Maliki R, Abdou A, et al. Gut resistome of preschool children after prolonged mass azithromycin distribution: a cluster-randomized trial. Clin Infect Dis 2021; 73:1292–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Doan T, Worden L, Hinterwirth A, et al. Macrolide and nonmacrolide resistance with mass azithromycin distribution. N Engl J Med 2020; 383:1941–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Doan T, Arzika AM, Hinterwirth A, et al. Macrolide resistance in MORDOR I—a cluster-randomized trial in Niger. N Engl J Med 2019; 380:2271–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Doan T, Hinterwirth A, Worden L, et al. Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution. Nat Med 2019; 25:1370–6. [DOI] [PubMed] [Google Scholar]
23. Murray CJL, Ikuta KS, Sharara F, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399:629–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Pernica JM, Steenhoff AP, Mokomane M, et al. Rapid enteric testing to permit targeted antimicrobial therapy, with and without Lactobacillus reuteri probiotics, for paediatric acute diarrhoeal disease in Botswana: a pilot, randomized, factorial, controlled trial. PLoS One 2017; 12:e0185177. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. World Health Organization . The treatment of diarrhoea: a manual for physicians and other senior health workers. https://apps.who.int/iris/handle/10665/43209. Accessed 6 July 2023.
26. Yousuf EI, Carvalho M, Dizzell SE, et al. Persistence of suspected probiotic organisms in preterm infant gut microbiota weeks after probiotic supplementation in the NICU. Front Microbiol 2020; 11:574137. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Stearns JC, Davidson CJ, McKeon S, et al. Culture and molecular-based profiles show shifts in bacterial communities of the upper respiratory tract that occur with age. ISME J 2015; 9:1246–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Guitor AK, Raphenya AR, Klunk J, et al. Capturing the resistome: a targeted capture method to reveal antibiotic resistance determinants in metagenomes. Antimicrob Agents Chemother 2019; 64:e01324-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Guitor AK, Yousuf EI, Raphenya AR, et al. Capturing the antibiotic resistome of preterm infants reveals new benefits of probiotic supplementation. Microbiome 2022; 10:136. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Alcock BP, Huynh W, Chalil R, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 2023; 51:D690–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Raphenya AR. arpcard/rgi. https://github.com/arpcard/rgi. Accessed 26 July 2022.
32. George S, Aguilera X, Gallardo P, et al. Bacterial gut microbiota and infections during early childhood. Front Microbiol 2021; 12:793050. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Rzewuska M, Kwiecień E, Chrobak-Chmiel D, Kizerwetter-Świda M, Stefańska I, Gieryńska M. Pathogenicity and virulence of Trueperella pyogenes: a review. Int J Mol Sci 2019; 20:2737. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Nilsen M, Rehbinder EM, Lødrup Carlsen KC, et al. A globally distributed Bacteroides caccae strain is the most prevalent mother-child shared Bacteroidaceae strain in a large Scandinavian cohort. Appl Environ Microbiol 2023; 89:e0078923. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Zhang RM, Sun J, Sun RY, et al. Source tracking and global distribution of the tigecycline non-susceptible tet(X). Microbiol Spectr 2021; 9:e0116421. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Bar-Zeev N, Moss WJ. Hope and humility for azithromycin. N Engl J Med 2019; 380:2264–5. [DOI] [PubMed] [Google Scholar]
37. Pop M, Walker AW, Paulson J, et al. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome Biol 2014; 15:R76. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Mizutani T, Aboagye SY, Ishizaka A, et al. Gut microbiota signature of pathogen-dependent dysbiosis in viral gastroenteritis. Sci Rep 2021; 11:13945. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. McMurry TL, McQuade ETR, Liu J, et al. Duration of postdiarrheal enteric pathogen carriage in young children in low-resource settings. Clin Infect Dis 2021; 72:e806–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
40. David LA, Weil A, Ryan ET, et al. Gut microbial succession follows acute secretory diarrhea in humans. mBio 2015; 6:e00381-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Chung The H, Le S-NH. Dynamic of the human gut microbiome under infectious diarrhea. Curr Opin Microbiol 2022; 66:79–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. van Schaik W. The human gut resistome. Philos Trans R Soc Lond B Biol Sci 2015; 370:20140087. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Tapela K, Rahube T. Isolation and antibiotic resistance profiles of bacteria from influent, effluent and downstream: a study in Botswana. Afr J Microbiol Res 2019; 13:279–89. [Google Scholar]
44. Xiang Y, Wu F, Chai Y, et al. A new plasmid carrying mphA causes prevalence of azithromycin resistance in enterotoxigenic Escherichia coli serogroup O6. BMC Microbiol 2020; 20:247. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. de Vries LE, Valles Y, Agerso Y, et al. The gut as reservoir of antibiotic resistance: microbial diversity of tetracycline resistance in mother and infant. PLoS One 2011; 6:e21644. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Anand Paramadhas BD, Tiroyakgosi C, Mpinda-Joseph P, et al. Point prevalence study of antimicrobial use among hospitals across Botswana; findings and implications. Expert Rev Anti Infect Ther 2019; 17:535–46. [DOI] [PubMed] [Google Scholar]
47. Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A 2011; 108(Suppl 1):4554–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Rogawski McQuade ET, Brennhofer SA, Elwood SE, et al. Frequency of bystander exposure to antibiotics for enteropathogenic bacteria among young children in low-resource settings. Proc Natl Acad Sci U S A 2022; 119:e2208972119. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

jiae049_Supplementary_Data

jiae049_supplementary_data.zip^{(1.3MB, zip)}

[jiae049-B1] 1. UNICEF . Levels and trends in child mortality, report 2021. https://data.unicef.org/resources/levels-and-trends-in-child-mortality-2021/. Accessed 1 November 2022.

[jiae049-B2] 2. Perin J, Mulick A, Yeung D, et al. Global, regional, and national causes of under-5 mortality in 2000–19: an updated systematic analysis with implications for the sustainable development goals. Lancet Child Adolesc Health 2022; 6:106–15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B3] 3. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B4] 4. Troeger C, Forouzanfar M, Rao PC, et al. Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infect Dis 2017; 17:909–48. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B5] 5. Nasrin D, Blackwelder WC, Sommerfelt H, et al. Pathogens associated with linear growth faltering in children with diarrhea and impact of antibiotic treatment: the global enteric multicenter study. J Infect Dis 2021; 224:S848–55. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B6] 6. Rogawski ET, Liu J, Platts-Mills JA, et al. Use of quantitative molecular diagnostic methods to investigate the effect of enteropathogen infections on linear growth in children in low-resource settings: longitudinal analysis of results from the MAL-ED cohort study. Lancet Glob Health 2018; 6:e1319–28. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B7] 7. Mokomane M, Kasvosve I, de Melo E, Pernica JM, Goldfarb DM. The global problem of childhood diarrhoeal diseases: emerging strategies in prevention and management. Ther Adv Infect Dis 2018; 5:29–43. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B8] 8. Malande OO, Munube D, Afaayo RN, et al. Barriers to effective uptake and provision of immunization in a rural district in Uganda. PLoS One 2019; 14:e0212270. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B9] 9. Pavlinac PB, Platts-Mills J, Liu J, et al. Azithromycin for bacterial watery diarrhea: a reanalysis of the antibiotics for children with severe diarrhea (ABCD) trial incorporating molecular diagnostics. J Infect Dis 2024; 229:988–98. [Google Scholar]

[jiae049-B10] 10. Pernica JM, Arscott-Mills T, Steenhoff AP, et al. Optimising the management of childhood acute diarrhoeal disease using a rapid test-and- treat strategy and/or Lactobacillus reuteri DSM 17938: a multicentre, randomised, controlled, factorial trial in Botswana. BMJ Glob Health 2022; 7:e007826. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B11] 11. World Health Organization . WHO guideline on mass drug administration of azithromycin to children under five years of age to promote child survival. https://apps.who.int/iris/handle/10665/333942. Accessed 1 November 2022. [PubMed]

[jiae049-B12] 12. Parnham MJ, Erakovic Haber V, Giamarellos-Bourboulis EJ, Perletti G, Verleden GM, Vos R. Azithromycin: mechanisms of action and their relevance for clinical applications. Pharmacol Ther 2014; 143:225–45. [DOI] [PubMed] [Google Scholar]

[jiae049-B13] 13. Burns AL, Sleebs BE, Gancheva M, et al. Targeting malaria parasites with novel derivatives of azithromycin. Front Cell Infect Microbiol 2022; 12:1063407. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B14] 14. O'Brien KS, Emerson P, Hooper PJ, et al. Antimicrobial resistance following mass azithromycin distribution for trachoma: a systematic review. Lancet Infect Dis 2019; 19:e14–25. [DOI] [PubMed] [Google Scholar]

[jiae049-B15] 15. Keenan JD, Arzika AM, Lietman TM. Azithromycin and childhood mortality in Africa. N Engl J Med 2018; 379:1382–4. [DOI] [PubMed] [Google Scholar]

[jiae049-B16] 16. Hooda Y, Tanmoy AM, Sajib MSI, Saha S. Mass azithromycin administration: considerations in an increasingly resistant world. BMJ Glob Health 2020; 5:e002446. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B17] 17. Gomes C, Martinez-Puchol S, Palma N, et al. Macrolide resistance mechanisms in Enterobacteriaceae: focus on azithromycin. Crit Rev Microbiol 2017; 43:1–30. [DOI] [PubMed] [Google Scholar]

[jiae049-B18] 18. Antibiotics for Children With Diarrhea (ABCD) Study Group; Ahmed T, Chisti MJ, et al. Effect of 3 days of oral azithromycin on young children with acute diarrhea in low-resource settings: a randomized clinical trial. JAMA Netw Open 2021; 4:e2136726. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B19] 19. Arzika AM, Maliki R, Abdou A, et al. Gut resistome of preschool children after prolonged mass azithromycin distribution: a cluster-randomized trial. Clin Infect Dis 2021; 73:1292–5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B20] 20. Doan T, Worden L, Hinterwirth A, et al. Macrolide and nonmacrolide resistance with mass azithromycin distribution. N Engl J Med 2020; 383:1941–50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B21] 21. Doan T, Arzika AM, Hinterwirth A, et al. Macrolide resistance in MORDOR I—a cluster-randomized trial in Niger. N Engl J Med 2019; 380:2271–3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B22] 22. Doan T, Hinterwirth A, Worden L, et al. Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution. Nat Med 2019; 25:1370–6. [DOI] [PubMed] [Google Scholar]

[jiae049-B23] 23. Murray CJL, Ikuta KS, Sharara F, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399:629–55. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B24] 24. Pernica JM, Steenhoff AP, Mokomane M, et al. Rapid enteric testing to permit targeted antimicrobial therapy, with and without Lactobacillus reuteri probiotics, for paediatric acute diarrhoeal disease in Botswana: a pilot, randomized, factorial, controlled trial. PLoS One 2017; 12:e0185177. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B25] 25. World Health Organization . The treatment of diarrhoea: a manual for physicians and other senior health workers. https://apps.who.int/iris/handle/10665/43209. Accessed 6 July 2023.

[jiae049-B26] 26. Yousuf EI, Carvalho M, Dizzell SE, et al. Persistence of suspected probiotic organisms in preterm infant gut microbiota weeks after probiotic supplementation in the NICU. Front Microbiol 2020; 11:574137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B27] 27. Stearns JC, Davidson CJ, McKeon S, et al. Culture and molecular-based profiles show shifts in bacterial communities of the upper respiratory tract that occur with age. ISME J 2015; 9:1246–59. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B28] 28. Guitor AK, Raphenya AR, Klunk J, et al. Capturing the resistome: a targeted capture method to reveal antibiotic resistance determinants in metagenomes. Antimicrob Agents Chemother 2019; 64:e01324-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B29] 29. Guitor AK, Yousuf EI, Raphenya AR, et al. Capturing the antibiotic resistome of preterm infants reveals new benefits of probiotic supplementation. Microbiome 2022; 10:136. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B30] 30. Alcock BP, Huynh W, Chalil R, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 2023; 51:D690–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B31] 31. Raphenya AR. arpcard/rgi. https://github.com/arpcard/rgi. Accessed 26 July 2022.

[jiae049-B32] 32. George S, Aguilera X, Gallardo P, et al. Bacterial gut microbiota and infections during early childhood. Front Microbiol 2021; 12:793050. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B33] 33. Rzewuska M, Kwiecień E, Chrobak-Chmiel D, Kizerwetter-Świda M, Stefańska I, Gieryńska M. Pathogenicity and virulence of Trueperella pyogenes: a review. Int J Mol Sci 2019; 20:2737. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B34] 34. Nilsen M, Rehbinder EM, Lødrup Carlsen KC, et al. A globally distributed Bacteroides caccae strain is the most prevalent mother-child shared Bacteroidaceae strain in a large Scandinavian cohort. Appl Environ Microbiol 2023; 89:e0078923. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B35] 35. Zhang RM, Sun J, Sun RY, et al. Source tracking and global distribution of the tigecycline non-susceptible tet(X). Microbiol Spectr 2021; 9:e0116421. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B36] 36. Bar-Zeev N, Moss WJ. Hope and humility for azithromycin. N Engl J Med 2019; 380:2264–5. [DOI] [PubMed] [Google Scholar]

[jiae049-B37] 37. Pop M, Walker AW, Paulson J, et al. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome Biol 2014; 15:R76. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B38] 38. Mizutani T, Aboagye SY, Ishizaka A, et al. Gut microbiota signature of pathogen-dependent dysbiosis in viral gastroenteritis. Sci Rep 2021; 11:13945. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B39] 39. McMurry TL, McQuade ETR, Liu J, et al. Duration of postdiarrheal enteric pathogen carriage in young children in low-resource settings. Clin Infect Dis 2021; 72:e806–14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B40] 40. David LA, Weil A, Ryan ET, et al. Gut microbial succession follows acute secretory diarrhea in humans. mBio 2015; 6:e00381-15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B41] 41. Chung The H, Le S-NH. Dynamic of the human gut microbiome under infectious diarrhea. Curr Opin Microbiol 2022; 66:79–85. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B42] 42. van Schaik W. The human gut resistome. Philos Trans R Soc Lond B Biol Sci 2015; 370:20140087. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B43] 43. Tapela K, Rahube T. Isolation and antibiotic resistance profiles of bacteria from influent, effluent and downstream: a study in Botswana. Afr J Microbiol Res 2019; 13:279–89. [Google Scholar]

[jiae049-B44] 44. Xiang Y, Wu F, Chai Y, et al. A new plasmid carrying mphA causes prevalence of azithromycin resistance in enterotoxigenic Escherichia coli serogroup O6. BMC Microbiol 2020; 20:247. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B45] 45. de Vries LE, Valles Y, Agerso Y, et al. The gut as reservoir of antibiotic resistance: microbial diversity of tetracycline resistance in mother and infant. PLoS One 2011; 6:e21644. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B46] 46. Anand Paramadhas BD, Tiroyakgosi C, Mpinda-Joseph P, et al. Point prevalence study of antimicrobial use among hospitals across Botswana; findings and implications. Expert Rev Anti Infect Ther 2019; 17:535–46. [DOI] [PubMed] [Google Scholar]

[jiae049-B47] 47. Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A 2011; 108(Suppl 1):4554–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jiae049-B48] 48. Rogawski McQuade ET, Brennhofer SA, Elwood SE, et al. Frequency of bystander exposure to antibiotics for enteropathogenic bacteria among young children in low-resource settings. Proc Natl Acad Sci U S A 2022; 119:e2208972119. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Minimal Impact on the Resistome of Children in Botswana After Azithromycin Treatment for Acute Severe Diarrheal Disease

Allison K Guitor

Anna Katyukhina

Margaret Mokomane

Kwana Lechiile

David M Goldfarb

Gerard D Wright

Andrew G McArthur

Jeffrey M Pernica

Abstract

Background

Methods

Results

Conclusions

Clinical Trials Registration

METHODS

Study Design and Participants

Targeted Sequencing of Antibiotic Resistance Genes

Analysis of Captured Antibiotic Resistance Genes

Ethics Approval

RESULTS

Patient Characteristics

Table 1.

Capturing the Resistome of Children With Diarrhea

Increase in the Prevalence of Macrolide Resistance Genes in Both Cohorts

Figure 1.

Potential Bacterial Hosts of Macrolide Resistance Genes

Figure 2.

Figure 3.

Changes in Nonmacrolide Resistance Genes

Figure 4.

DISCUSSION

Supplementary Data

Supplementary Material

Contributor Information

Notes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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