Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution


The MORDOR I trial1, conducted in Niger, Malawi and Tanzania, demonstrated that mass azithromycin distribution to preschool children reduced childhood mortality1. However, the large but simple trial design precluded determination of the mechanisms involved. Here we examined the gut microbiome of preschool children from 30 Nigerien communities randomized to either biannual azithromycin or placebo. Gut microbiome γ-diversity was not significantly altered (P = 0.08), but the relative abundances of two Campylobacter species, along with another 33 gut bacteria, were significantly reduced in children treated with azithromycin at the 24-month follow-up. Metagenomic analysis revealed functional differences in gut bacteria between treatment groups. Resistome analysis showed an increase in macrolide resistance gene expression in gut microbiota in communities treated with azithromycin (P = 0.004). These results suggest that prolonged mass azithromycin distribution to reduce childhood mortality reduces certain gut bacteria, including known pathogens, while selecting for antibiotic resistance.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Study design.
Fig. 2: Gut microbiome community, diversity, and composition of preschool children randomized to the placebo- and azithromycin-treated groups.
Fig. 3: Relative abundances of individual microbial genera and species differ between children treated with placebo versus azithromycin.
Fig. 4: Bacterial functional profile and resistome between treatment groups.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The microbial sequencing reads have been deposited with the NCBI Sequence Read Archive under BioProject no. PRJNA549968. Additional processed data reported in this study are available upon request. Limitations apply to variables that may compromise participant privacy or consent.


  1. 1.

    Keenan, J. D. et al. Azithromycin to reduce childhood mortality in sub-Saharan Africa. N. Engl. J. Med. 378, 1583–1592 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Jimenez, S. G., Heine, R. G., Ward, P. B. & Robins-Browne, R. M. Campylobacter upsaliensis gastroenteritis in childhood. Pediatr. Infect. Dis. J. 18, 988–992 (1999).

    CAS  Article  Google Scholar 

  3. 3.

    Bourke, B., Chan, V. L. & Sherman, P. Campylobacter upsaliensis: waiting in the wings. Clin. Microbiol. Rev. 11, 440–449 (1998).

    CAS  Article  Google Scholar 

  4. 4.

    Sire, J. M. et al. Community-acquired infectious diarrhoea in children under 5 years of age in Dakar, Senegal. Paediatr. Int. Child Health 33, 139–144 (2013).

    Article  Google Scholar 

  5. 5.

    Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    Article  Google Scholar 

  7. 7.

    Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    CAS  Article  Google Scholar 

  8. 8.

    Yoshioka, S. & Newell, P. D. Disruption of de novo purine biosynthesis in Pseudomonas fluorescens Pf0-1 leads to reduced biofilm formation and a reduction in cell size of surface-attached but not planktonic cells. PeerJ. 4, e1543 (2016).

    Article  Google Scholar 

  9. 9.

    Zhao, H., Patel, V., Helmann, J. D. & Dörr, T. Don’t let sleeping dogmas lie: new views of peptidoglycan synthesis and its regulation. Mol. Microbiol. 106, 847–860 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Typas, A., Banzhaf, M., Gross, C. A. & Vollmer, W. From the regulation of peptidoglycan synthesis to bacterial growth and morphology. Nat. Rev. Microbiol. 10, 123–136 (2011).

    Article  Google Scholar 

  11. 11.

    O’Neill, L. A., Kishton, R. J. & Rathmell, J. A guide to immunometabolism for immunologists. Nat. Rev. Immunol. 16, 553–565 (2016).

    Article  Google Scholar 

  12. 12.

    Doan, T. et al. Macrolide resistance in MORDOR I: a cluster-randomized trial in Niger. N. Engl. J. Med. 380, 2271–2273 (2019).

    Article  Google Scholar 

  13. 13.

    Oldenburg, C. E. et al. Gut resistome after oral antibiotics in preschool children in BurkinaFaso: a randomized controlled trial. Clin. Infect. Dis. (2019).

  14. 14.

    O’Brien, K. S. et al. Antimicrobial resistance following mass azithromycin distribution for trachoma: a systematic review. Lancet Infect. Dis. 19, e14–e25 (2019).

    Article  Google Scholar 

  15. 15.

    Doan, T. et al. Macrolide resistance in MORDOR I - A cluster-randomized trial in Niger. N. Engl. J. Med. 380, 2271–2273 (2019).

    Article  Google Scholar 

  16. 16.

    McMullan, B. J. & Mostaghim, M. Prescribing azithromycin. Aust. Prescr. 38, 87–89 (2015).

    Article  Google Scholar 

  17. 17.

    Engberg, J., Aarestrup, F. M., Taylor, D. E., Gerner-Smidt, P. & Nachamkin, I. Quinolone and macrolide resistance in Campylobacter jejuni and C. coli: resistance mechanisms and trends in human isolates. Emerg. Infect. Dis. 7, 24–34 (2001).

    CAS  Article  Google Scholar 

  18. 18.

    Nachamkin, I., Ung, H. & Li, M. Increasing fluoroquinolone resistance in Campylobacter jejuni, Pennsylvania, USA, 1982–2001. Emerg. Infect. Dis. 8, 1501–1503 (2002).

    Article  Google Scholar 

  19. 19.

    Same, R. G. & Tamma, P. D. Campylobacter infections in children. Pediatr. Rev. 39, 533–541 (2018).

    Article  Google Scholar 

  20. 20.

    Kotloff, K. L. et al. The Global Enteric Multicenter Study (GEMS) of diarrheal disease in infants and young children in developing countries: epidemiologic and clinical methods of the case/control study. Clin. Infect. Dis. 55(Suppl 4), S232–S245 (2012).

    Article  Google Scholar 

  21. 21.

    Amour, C. et al. Epidemiology and impact of Campylobacter infection in children in 8 low-resource settings: results from the MAL-ED study. Clin. Infect. Dis. 63, 1171–1179 (2016).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Nachamkin, I. Chronic effects of Campylobacter infection. Microbes Infect. 4, 399–403 (2002).

    Article  Google Scholar 

  23. 23.

    Kotloff, K. L. et al. The incidence, aetiology, and adverse clinical consequences of less severe diarrhoeal episodes among infants and children residing in low-income and middle-income countries: a 12-month case-control study as a follow-on to the Global Enteric Multicenter Study (GEMS). Lancet Glob. Health 7, e568–e584 (2019).

    Article  Google Scholar 

  24. 24.

    Buss, S. N. et al. Multicenter evaluation of the BioFire FilmArray gastrointestinal panel for etiologic diagnosis of infectious gastroenteritis. J. Clin. Microbiol. 53, 915–925 (2015).

    Article  Google Scholar 

  25. 25.

    Patrick, M. E. et al. Features of illnesses caused by five species of Campylobacter, Foodborne Diseases Active Surveillance Network (FoodNet)—2010–2015. Epidemiol. Infect. 146, 1–10 (2018).

    CAS  Article  Google Scholar 

  26. 26.

    Bolinger, H. & Kathariou, S. The current state of macrolide resistance in Campylobacter spp.: trends and impacts of resistance mechanisms. Appl. Environ. Microbiol. 83, e00416-17 (2017).

    Article  Google Scholar 

  27. 27.

    Founou, L. L., Amoako, D. G., Founou, R. C. & Essack, S. Y. Antibiotic resistance in food animals in Africa: a systematic review and meta-analysis. Microb. Drug Resist. 24, 648–665 (2018).

    CAS  Article  Google Scholar 

  28. 28.

    Schiaffino, F. et al. Antibiotic resistance of Campylobacter species in a pediatric cohort study. Antimicrob. Agents Chemother. 63, e01911-18 (2019).

    Article  Google Scholar 

  29. 29.

    Keenan, J. D. et al. Longer-term assessment of azithromycin for reducing childhood mortality in Africa. N. Engl. J. Med. 380, 2207–2214 (2019).

    CAS  Article  Google Scholar 

  30. 30.

    Doan, T. et al. Gut microbial diversity in antibiotic-naive children after systemic antibiotic exposure: a randomized controlled trial. Clin. Infect. Dis. 64, 1147–1153 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Chandramohan, D. et al. Effect of adding azithromycin to seasonal malaria chemoprevention. N. Engl. J. Med. 380, 2197–2206 (2019).

    Article  Google Scholar 

  32. 32.

    Amza, A. et al. Does mass azithromycin distribution impact child growth and nutrition in Niger? A cluster-randomized trial. PLoS Negl. Trop. Dis. 8, e3128 (2014).

    Article  Google Scholar 

  33. 33.

    Sié, A. et al. Effect of antibiotics on short-term growth among children in Burkina Faso: a randomized trial. Am. J. Trop. Med. Hyg. 99, 789–796 (2018).

    Article  Google Scholar 

  34. 34.

    Amza, A. et al. A cluster-randomized controlled trial evaluating the effects of mass azithromycin treatment on growth and nutrition in Niger. Am. J. Trop. Med. Hyg. 88, 138–143 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Doan, T. et al. Mass azithromycin distribution and community microbiome: a cluster-randomized trial. Open Forum Infect. Dis. 5, ofy182 (2018).

    Article  Google Scholar 

  36. 36.

    Doan, T. et al. Metagenomic DNA sequencing for the diagnosis of intraocular infections. Ophthalmology 124, 1247–1248 (2017).

    Article  Google Scholar 

  37. 37.

    Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    CAS  Article  Google Scholar 

  38. 38.

    Ziv, J. & Lempel, A. A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23, 337–343 (1977).

    Article  Google Scholar 

  39. 39.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  40. 40.

    Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, 1721–1729 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Lakin, S. M. et al. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 45, D574–D580 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  43. 43.

    Hunt, M. et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb. Genom. 3, e000131 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Gibreel, A. et al. Macrolide resistance in Campylobacter jejuni and Campylobacter coli: molecular mechanism and stability of the resistance phenotype. Antimicrob. Agents Chemother. 49, 2753–2759 (2005).

    CAS  Article  Google Scholar 

  45. 45.

    Luangtongkum, T. et al. Antibiotic resistance in Campylobacter: emergence, transmission and persistence. Future Microbiol. 4, 189–200 (2009).

    CAS  Article  Google Scholar 

  46. 46.

    Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).

    Article  Google Scholar 

  47. 47.

    Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88, 2427–2439 (2007).

    Article  Google Scholar 

Download references


We thank S. Kumar for the helpful discussions regarding the gut pathogens identified in this study. We thank the program officers from the trial’s sponsor: Bill and Melinda Gates Foundation, R. Izadnegahdar, J. Jacobson, T. Kanyok, E. Shutes, C. Damman, A. Sarangi and L. Lamberti. We also thank the members of the Data and Safety Monitoring Committee: University of Washington, J. Walson; Liverpool School of Tropical Medicine, A. Hightower; Loyola University, E. Anderson; Berhan Public Health & Eye Care Consultancy, W. Alemayehu; Tulane University, L. Rajan. This work was supported by the Bill and Melinda Gates Foundation (award no. OPP1032340 to T.M.L.), the Peierls Foundation (T.M.L.), a Research to Prevent Blindness Career Development Award (award no. 127613A to T.D.) and an unrestricted grant from Research to Prevent Blindness. The funding organizations had no role in the analysis or interpretation of the data or the preparation of the manuscript.

Author information




T.D. and T.M.L designed the experiments and supervised the project. T.D., L.Z., S.L.C., S.S. and C. Chen performed the experiments. T.C.P. performed the randomization. A.M.A., R.M., S.K., A.A., C. Cook, E.L., J.D.K. and T.M.L. oversaw the field work and sample collection. E.D.C. assisted with sample sequencing. T.D., A.H., L.W. and T.C.P. wrote the scripts and performed the bioinformatics analyses with contributions from T.M.L. I.N. assisted with data interpretation. T.D. and T.M.L. wrote the initial draft; all coauthors reviewed the manuscript and agreed to publication.

Corresponding author

Correspondence to T. Doan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relative abundances of species significantly different between treatment groups.

Normalized abundance, averaged at the village level, arranged by supervised hierarchical clustering. Thirty-five differential species identified with a false discovery rate < 0.01 and log2(fold change) > 2 (two-sided negative binomial Wald test with Benjamini–Hochberg correction for multiple comparisons).

Source data

Extended Data Fig. 2 Resistome of the gut microbiome between treatment groups.

Box plots, with individual values shown as dots, for all classes of resistance determinants, except macrolides, detected in the rectal samples of preschool children at baseline and at 24 months for 30 villages. Each data point is the prevalence of resistance determinants per village. The boxes indicate the median and quartiles of prevalence; the whiskers indicate the estimated 95% CI. Az, azithromycin; Pl, placebo.

Source data

Extended Data Fig. 3 Campylobacter macrolide resistance between treatment groups.

Box plots, with individual values shown as dots, of the prevalence of any nucleotide change from A at position 2,075 of the 23S rRNA transcript detected in the rectal samples of preschool children at baseline and at 24 months (n = 30 villages; P = 0.66, ANCOVA). Each data point is the prevalence of resistance transcripts per village. The boxes indicate the median and quartiles of prevalence; the whiskers indicate the estimated 95% CI.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–6 and Statistical Analysis Plan

Reporting Summary

Source data

Source Data Fig. 2

Statistical Data Analysis

Source Data Fig. 3

Statistical Data Analysis

Source Data Fig. 4

Statistical Data Analysis

Extended Data Fig. 1

Statistical Data Analysis

Extended Data Fig. 2

Statistical Data Analysis

Extended Data Fig. 3

Statistical Data Analysis

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Doan, T., Hinterwirth, A., Worden, L. et al. Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution. Nat Med 25, 1370–1376 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing