
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01851-0
10.1016/j.isci.2024.110626
110626
Article
Effect of intrapartum azithromycin on gut microbiota development in early childhood: A post hoc analysis of a double-blind randomized trial
Sanyang Bakary 1
de Silva Thushan I. 12
Camara Bully 1
Beloum Nathalie 1
Kanteh Abdoulie 1
Manneh Jarra 1
de Steenhuijsen Piters Wouter A.A. 34
Bogaert Debby 35
Sesay Abdul Karim 1
Roca Anna aroca@mrc.gm
16∗
1 Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine, Banjul, The Gambia
2 The Florey Institute and Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
3 Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children’s Hospital/University Medical Center Utrecht, Utrecht, the Netherlands
4 Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
5 Centre for Inflammation Research, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
∗ Corresponding author aroca@mrc.gm
6 Lead contact

03 8 2024
20 9 2024
03 8 2024
27 9 11062627 3 2024
28 6 2024
29 7 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Intrapartum azithromycin prophylaxis has shown the potential to reduce maternal infections but showed no effect on neonatal sepsis and mortality. Antibiotic exposure early in life may affect gut microbiota development, leading to undesired consequences. Therefore, we here assessed the impact of 2 g oral intrapartum azithromycin on gut microbiota development from birth to the age of 3 years, by 16S-rRNA gene profiling of rectal samples from 127 healthy Gambian infants selected from a double-blind randomized placebo-controlled clinical trial (PregnAnZI-2). Microbiota trajectories showed, over the first month of life, a slower community transition and increase of Enterobacteriaceae (p = 0.001) and Enterococcaceae (p = 0.064) and a decrease of Bifidobacterium (p < 0.001) in the azithromycin compared to the placebo arm. Intrapartum azithromycin alters gut microbiota development and increases proinflammatory bacteria in the first month of life, which may have undesirable effects on the child.

Graphical abstract

Highlights

• Intrapartum azithromycin alters neonatal gut microbiota

• It increases proinflammatory bacteria while decreasing Bifidobacterium

• Yet, the intervention does not alter the long-term gut microbiota development

Health sciences; Medicine; Medical specialty; Medical microbiology; Pharmacology; Microbiome

Subject areas

Health sciences
Medicine
Medical specialty
Medical microbiology
Pharmacology
Microbiome
Published: August 3, 2024
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pmcIntroduction

In the first decade of the 21st century, sub-Saharan Africa (SSA) experienced a notable 28% reduction in maternal mortality and a 21% reduction in neonatal mortality.1 Despite this progress, SSA still bears the highest burden of global maternal and neonatal mortality, and progress in further improvement has stalled. Projections indicate that by 2030, maternal mortality in SSA could be at 390 per 100,000 live births, more than five times higher than the Sustainable Development Goals (SDGs) target of less than 70 per 100,000 live births.1 Likewise, the current neonatal mortality rate stands at 27 per 1,000 live births,2 needing a reduction to less than 12 per 1,000 live births by 2030 to meet the SDG target.3 Infections contribute significantly to these statistics, with up to 11.5% of maternal deaths and one-third of neonatal deaths attributed to infections.4,5 Intrapartum and postpartum infections emerge as substantial contributors to morbidity and mortality for both mothers and neonates in SSA. Interventions tailored to local contexts are crucial to prevent infections in those countries with highest burden, mostly in SSA, and hasten the reduction of maternal and neonatal mortality rates.

An initial proof of concept trial, known as PregnAnZI-1, demonstrated that giving oral azithromycin to women during labor decreases the colonization of pathogenic bacteria in both mother and child in different carriage sites including the nasopharynx, the breast milk, and the vaginal tract up to 4 weeks following the intervention.6 Recognizing bacterial colonization as a necessary precursor for disease, these results inspired larger trials of the same intervention across SSA, Asia, and Latin America, aiming to reduce maternal and neonatal morbidity and mortality. One of such trials, PregnAnZI-2,7 conducted in The Gambia and Burkina Faso, evaluated the impact of the intervention on a composite endpoint of neonatal sepsis or mortality.8 The A-PLUS trial, a parallel multicenter trial, had two co-primary composite endpoints: assessing (1) the impact on reducing maternal sepsis or mortality and (2) the impact on reducing neonatal sepsis or mortality. Both trials, however, showed no reduction of neonatal sepsis and mortality.8,9 Despite this, they demonstrated other beneficial effects. The PregnAnZI-2 trial showed a reduction of maternal infections during the 4 weeks following the intervention (such as mastitis and puerperal fever) and neonatal infections (such as skin infections), whereas the A-PLUS revealed a decline in the maternal sepsis. Thus, there is strong evidence that intrapartum azithromycin reduces morbidity in both women and neonates,10 and it has been proposed as an intervention to reduce maternal morbidity.11 The lack of effect on mortality in neonates in the abovementioned trials compared to reduced mortality in infants from mass azithromycin administration trials12,13 could be due to differences in microbiota dynamics between the neonatal period and infancy.

It is therefore imperative to determine the overall impact of the intervention, balancing its benefits with the potential risks before widespread implementation. An essential consideration in this regard is its effects on the development of the child’s microbiota. The role of the microbiota in health and disease has become increasingly clear through numerous microbiome studies conducted over the recent years. Research indicates that early exposure to antibiotics may lead to alterations in the microbiota.14,15 The neonatal period represents a critical window of microbiota assembly. Perturbations during this period may alter the development trajectory of the microbiota or immune programming in early life, potentially resulting in long-lasting health consequences.16,17,18 Mass administration of azithromycin to pre-school children from the MODOR-I trial showed no effect on bacterial community diversity but reduced carriage of several bacterial species including Campylobacter upsaliensis for up to 6 months after four rounds of biannual treatment.19 In newborns, Bifidobacterium plays a key role as an early gut microbiota colonizer, enhancing nutrient availability, immune development, and colonization resistance.20,21 This important bacterium is, however, vulnerable to antibiotic-induced microbiota perturbations. In a recent study on Dutch infants, exposure to different antibiotic regimens in the first week of life showed a reduction in Bifidobacterium and Bacteroides, coupled with an increase in Klebsiella and Enterococcus, among other changes.22 However, antibiotic effects on the microbiota can vary depending on the type of regimen, dose administered, duration of treatment,22 age at exposure, and geographical location. Although in the PregnAnZI trials women were given azithromycin during labor, the antibiotic reaches the baby through breast milk up to 4 weeks after birth.23 Previously, we had shown that intrapartum azithromycin leads to short-term alterations in the nasopharyngeal microbiota, which wane by the age of 12 months24 In the current study, we aim to assess the effect of the same intervention on the development of the gut microbiota from birth to early childhood using 16S rRNA gene sequencing.

Results

Description of study population

Baseline characteristics of the selected participants are shown in Tables 1 (main cohort) and 2 (3-year follow-up visit). Children in the azithromycin and placebo arms were comparable except for differences in the ethnic group distribution.Table 1 Baseline characteristics for main cohort

Ethnicity, n (%)	Azithromycin (N = 48)	Placebo (N = 54)	p value	
Mandinka	14 (29.2)	23 (42.6)	0.059	
Wollof	8 (16.7)	8 (14.8)	
Jola	7 (14.6)	6 (11.1)	
Fula	14 (29.2)	5 (9.3)	
Others	5 (10.4)	12 (22.2)	
Maternal age (yrs), mean (SD)	26.9 (5.9)	28.6 (6.6)	0.180	
Birth weight (Kg), mean (SD)	3.1 (0.5)	3.2 (0.5)	0.671	
Sex, female (%)	24 (50.0)	28 (51.9)	1.000	
Delivery season, wet (%)a	21 (43.8)	32 (59.3)	0.172	
Age at sample collection	
 Day 6 (days), median (IQR)	6 (5.5–6.5)	6 (5.0–6.0)		
 Day 28 (days), median (IQR)	27 (26–28)	27 (26–28)		
 Month 4 (months), median (IQR)	3.9 (3.9–4.0)	4.0 (3.9–4.0)		
a Wet season = June–October.

Table 2 Baseline characteristics for year 3 cohort

Ethnicity, n (%)	Azithromycin (N = 42)	Placebo (N = 55)	p-value	
Mandinka	15 (35.7)	27 (49.1)	0.019	
Wollof	8 (19.0)	7 (12.7)	
Jola	7 (16.7)	7 (12.7)	
Fula	9 (21.4)	2 (3.6)	
Others	3 (7.1)	12 (21.8)	
Maternal age (yrs), mean (SD)	27.7 (6.1)	28.5 (6.6)	0.542	
Birth weight (kg), mean (SD)	3.1 (0.5)	3.1 (0.5)	0.563	
Sex, female (%)	23 (54.8)	29 (52.7)	1.000	
Delivery season, wet (%)a	17 (40.5)	30 (54.5)	0.243	
Mode of BF in first 6 months	
 Exclusive BF (%)	28 (66.7)	37 (67.3)	0.857	
 Predominant BF (%)	6 (14.3)	6 (10.9)	
 Complementary BF (%)	8 (19.0)	12 (21.8)	
Recent antibiotic consumption, n (%)b	6 (14.3)	14 (25.5)	0.252	
Recent sickness, n (%)b	18 (42.9)	24 (43.6)	0.664	
BF duration (months), median (IQR)	20 (18–24)	18 (17–21)	0.079	
Age at sample collection	
 Year 3 (months), median (IQR)	36.5 (34.7–39.4)	39.4 (35.5–42.1)		
BF = breastfeeding.

a Wet season = June–October.

b Data on recent sickness and antibiotic consumption were based on one-month recall.

Study samples

A total of 476 samples (218 azithromycin, 258 placebo) from the children selected (Figure 1) were available for the microbiota analysis.Figure 1 A summary of the selection process

(A) Selection of samples for microbiome study.

(B) Distribution of samples included in microbiota analysis between trial arms at each time point.

The median read count after quality control was 27,975 (IQR: 13,341–42,646) for the samples and 50 (IQR: 27–82) for the negative controls, including field, PCR, and extraction controls (Figure S1C). To control for differences in sequencing depth between samples, each sample was rarefied by subsampling to 3,000 sequences with 1,000 iterations before diversity estimation. Ninety-one samples (43 Azi vs. 48 placebo), of which 85 were day 0 samples, had less than 3,000 reads and were dropped by subsampling. This is not surprising as we expect low biomass in day 0 rectal swabs (Figure S2). As a result, only 11 days 0 samples remained after subsampling, and the time point was therefore excluded from all statistical comparisons between trial arms with only descriptive data being presented. Overall, 385 samples (175 Azi vs. 210 placebo) were available for statistical analysis. Details on the distribution of these samples between arms per time point can be found in Table S1. Community coverage was estimated by Good’s coverage, which showed median coverage of 99.5% (range 97.7%–99.9%) for azithromycin arm and 99.4% (range 97.7%–99.9%) for placebo arm.

Within-sample diversity (alpha diversity) not impacted by azithromycin

Changes in Shannon diversity and OTU richness by age and between trial arms are shown in Figures 2A and 2B. In each arm, Shannon diversity increased significantly with age (Figure 2A). The increase was steady between day 6 and month 4 followed by a marked increase at year 3 (p value <0.0001 within each trial arm) (Figure 2A; Table 3). No difference in Shannon diversity was observed between the trial arms at any time point (Figure 2A; Table 4). OTU richness was lower in the azithromycin arm at day 6 and day 28 but no difference was found between the trial arms thereafter (Figure 2B; Table S2).Figure 2 Shannon diversity and OTU richness observed over time

(A) Shannon diversity between trial arms at each time point and within each trial arm over time. In both trial arms, alpha diversity increased significantly with age (Kruskal-Wallis test). There was no difference between arms at any time point (Wilcoxon test). Details of statistical comparisons between trial arms are shown in Table 4. (B) OTU richness between trial arms at each time point. OTU richness was lower in the azithromycin arm at days 6 and 28 but there was no difference between arms at subsequent time points (Wilcoxon test). Details on statistical comparison of OTU richness between trial arms can be found in Table S2. For both (A) and (B), the boxes show the median, lower, and upper quartiles (middle 50% values), whereas the whiskers show the lower and upper 25% values excluding outliers.

Table 3 Change in alpha diversity (Shannon diversity) by age within each trial arm assessed using a linear mixed effects model

Interval	Azithromycin p value	Placebo p value	
Day 6 to day 28	0.283	0.296	
Day 28 to month 4	0.036	0.011	
Month 4 to year 3	<0.001	<0.001	

Table 4 Alpha diversity (Shannon diversity) between trial arms at each time point compared by Wilcoxon Rank-Sum test

Time point	Azithromycin
Mean (95% CI)	Placebo
Mean (95% CI)	p value	
Day 6	1.38 (1.17–1.59)	1.54 (1.34–1.73)	0.291	
Day 28	1.60 (1.40–1.80)	1.76 (1.57–1.95)	0.259	
Month 4	1.95 (1.75–2.15)	2.16 (1.96–2.35)	0.197	
Year 3	3.67 (3.46–3.88)	3.53 (3.35–3.72)	0.225	

Azithromycin strongly impacted community composition (beta-diversity)

Overall community composition varied significantly with age (p < 0.001, R2 = 23.4%) (Figure 3A). Similar to Shannon diversity, the changes were steady over the first 4 months of life and then varied markedly between month 4 and year 3. We saw a relatively large effect (R2 = 8.0%, p = 0.001) of azithromycin on the overall community composition at day 6 and a lesser effect (R2 = 2.0%, p = 0.072) at day28 (Figure 3B; Table 5). No difference was found between the trial arms beyond day 28.Figure 3 Non-metric multidimensional scaling plots (NMDS) showing overall community composition (beta diversity) calculated by Bray-Curtis dissimilarity index

(A) Overall community composition compared by age by PERMANOVA.

(B) Overall community composition compared between trial arms at each time point by PERMANOVA. The effect of azithromycin on overall community composition was largest at day 6.

Table 5 Community composition (beta diversity) compared at each time point by trial arm, mother’s ethnicity, and season of birth using PERMANOVA

Time point	Treatment	Mother’s ethnicity	Season of birth	
R2	p value	R2	p value	R2	p value	
Day 6	0.08	0.001	0.05	0.175	0.01	0.552	
Day 28	0.02	0.072	0.04	0.360	0.02	0.089	
Month 4	0.01	0.608	0.04	0.477	0.01	0.379	
Year 3	0.01	0.827	0.05	0.199	0.01	0.566	

In terms of covariates, maternal ethnicity showed a consistent effect on community composition at all the follow-up time points though these effects were not statistically significant (Table 5; Figure S3). Community composition did not vary by season of birth at any time point (Table 5; Figure S4). There was a trend of association of season of birth with community composition only at day 28 (p = 0.089, R2 = 1.7%), where we did not find an interaction with trial arm (p = 0.500).

Community profiles and OTU abundance varied between trial arms

Community profiles showed similar taxonomic representation on phylum and genus level between trial arms at day 0 (Figures 4 and S5). The genus profiles showed diverse genera including Bifidobacterium, Enterobacteriaceae, Lactobacillus, Pseudomonas, Prevotella, Staphylococcus, and Streptococcus. The profiles were mostly similar between trial arms except at day 6, where there was a marked difference between trial arms in the relative abundances of Bifidobacterium and Enterobacteriaceae.Figure 4 Bacterial genus profiles showing the mean relative abundances of the top 20 genera in either arm of the trial, grouped by time point

At day 6, distinct profile differences emerged between trial arms. Specifically, we observed a decrease in the relative abundance of Actinobacteria at the phylum level (3.7% vs. 13.5%), accompanied by a corresponding decrease in Bifidobacterium spp at genus level (8.1% vs. 32.8%) in the azithromycin when compared to the placebo arm. Conversely, we observed an increase in the relative abundance of Proteobacteria (24.2% vs. 10.9%) mirrored by an increase in the abundance of Enterobacteriaceae spp (43.7% vs. 19.6%) in the azithromycin arm when compared to the placebo arm (Figures 4 and S5). Differential abundance analysis at OTU level showed an increased abundance in the azithromycin arm of three OTUs (OTU00003-Enterobacteriaceae, OTU00017-Enterococcaceae, and OTU00032-Corynebacteriaceae) and a decreased abundance of three OTUs (OTU00001-Bifidobacterium, OTU00004-Collinsella, and OTU00008-Bacteroides) (Figure 5A; Table 6). Only OTU00004-Collinsella showed a decreased prevalence in the azithromycin arm (Table 7).Figure 5 OTU differential abundance between trial arms

(A) OTUs that had significantly different representation between trial arms at days 6 and 28. A positive fold change indicates increased abundance in azithromycin arm, and a negative fold change indicates decreased abundance in the azithromycin arm. The length of the bar indicates the magnitude of fold change. Statistical details are shown in Table 6. (B) Time trend of relative abundance of the top four differentially abundant OTUs between trial arms.

Table 6 Differentially abundant OTUs and their relative abundance between trial arms at days 6 and 28

Time point	OTU	Log-fold change	Azithromycin rel_abund (%)	Placebo rel_abund (%)	p-adj (FDR)	
Day 6	Otu00001-Bifidobacterium	−2.64	11.36	31.37	<0.001	
Otu00004-Collinsella	−1.9	2.10	6.30	0.072	
Otu00003-Enterobacteriaceae_uncl	2.29	22.98	3.23	0.001	
Otu00032-Corynebacteriaceae_uncl	1.13	1.90	0.76	0.019	
Otu00017-Enterococcaceae_uncl	1.01	3.32	0.33	0.064	
Otu00008-Bacteroides	−1.74	1.33	2.17	0.024	
Day 28	Otu00031_Dialister	−1.22	0.01	0.27	<0.001	
Otu00079-Negativicoccus	−3.07	0.02	0.17	0.006	
Otu00143_Dermabacter	2.10	0.24	0.01	0.006	
Otu00015_Bifidobacterium	−2.22	1.30	2.33	0.017	
Otu00039-Peptostreptococcus	−1.48	0.06	0.43	0.039	
Otu00003_Enterobacteriaceae_uncl	1.15	4.62	0.75	0.087	
Otu00005_Veillonellaceae_uncl	1.08	5.42	2.76	0.099	
Note: a negative value for fold change indicates lower abundance in azithromycin arm and vice versa. p values were adjusted by false discovery rate (FDR), and a significance cutoff of <0.1 was used. rel_abund = relative abundance. uncl = unclassified.

Table 7 OTUs with different prevalence between trial arms at days 6 and 28

Time point	OTU	Azithromycin
Prev (%)	Placebo
Prev (%)	OR (95% CI)	p-adj (FDR)	
Day 6	Otu00004-Collinsella	42.5	81.6	0.10 (0.03–0.28)	<0.001	
Day 28	Otu00079-Negativicoccus	8.7	67.3	0.06 (0.01–0.21)	<0.001	
Otu00093-Propionibacteriaceae_uncl	17.4	71.2	0.14 (0.05–0.37)	<0.001	
Otu00017-Enterococcaceae_uncl	89.1	61.5	3.75 (1.42–10.70)	0.033	
Otu00143-Dermabacter	65.2	19.2	8.90 (3.12–28.63)	0.001	
Otu00031_Dialister	2.2	57.7	0.02 (0.00–0.12)	<0.001	
Otu00055_Bacteroides	23.9	46.2	0.29 (0.10–0.80)	0.004	
Otu00003-Enterobacteriaceae_uncl	97.8	82.6	9.58 (2.04–91.43)	0.005	
Otu00015_Bifidobacterium	30.4	46.2	0.34 (0.12–0.89)	0.089	
Note: OR = odds ratio. OR lower than 1 indicates lower prevalence in azithromycin arm and vice versa. p values were adjusted by false discovery rate (FDR), and a significance cutoff of <0.1 was used. Prev = prevalence. uncl = unclassified.

At day 28, both trial arms showed similar community profiles, with high relative abundances of the phylum Actinobacteria (14.5% in Azithromycin arm vs. 16.7% in Placebo arm) and the genus Bifidobacterium (36.6% in Azithromycin arm vs. 41.4% in Placebo arm) (Figures 4 and S5). Differential abundance analysis showed increased abundance in the azithromycin arm of three OTUs (OTU00143_Dermabacter, OTU00003_Enterobacteriaceae, and OTU00005_Veillonellaceae) and a decrease of four OTUs (OTU00031_Dialister, OTU00039-Peptostreptococcus, OTU00015_Bifidobacterium and OTU00079-Negativicoccus) (Figure 5A; Table 6). Differences in OTU prevalence showed an increase of two (OTU00143_Dermabacter and OTU00003_Enterobacteriaceae) and a reduction of three (OTU00031_Dialister, OTU00015_Bifidobacterium, and OTU00079-Negativicoccus) of these OTUs among others (Table 7).

At month 4, both trial arms showed similar microbiota profiles with Bifidobacterium still dominating, and Enterobacteriaceae decreased in abundance compared to the previous time points (Figures 4 and S5). There were no differences in abundance or prevalence of any OTUs between the trial arms. Time trends of the relative abundances of the top four differentially abundant OTUs show recovery of the initial perturbations within 4 months after birth (Figure 5B).

At year 3, both trial arms showed high abundances of the phyla Firmicutes and Bacteroidetes (Figure S5). Genus profiles were dominated by Prevotella, Finegoldia, Peptoniphilus, Faecalibacterium, Ezakiella, Anaerococcus, and Bacteroides (Figure 4). Relative abundances of Bifidobacterium and Enterobacteriaceae were very low (combined relative abundance <5% in each arm). Neither abundance nor prevalence of any OTU was different between trial arms.

Slower microbiota community transition observed in azithromycin arm

Samples were divided into three microbiota community types by unsupervised clustering based on similarities in highest abundant bacteria identified in each sample. The three community types namely Community_1, Community_2, and Community_3 varied in representation over time (Azi p < 0.001, Placebo p < 0.001) and between trial arms at day 6 (p < 0.001) and day 28 (p < 0.001) by Fisher’s exact test. Community_1 was characterized by diverse genera (Figure 6B) and was most prevalent at day 0 and day 6 (Figure 6A). Community_2 was characterized by high Bifidobacterium abundance and increased in prevalence from day 6 up to month 4. At year 3, nearly all samples had the Community_3 microbiota type with diverse anaerobes including Prevotella, Finegoldia, Peptoniphilus, Anaerococcus, and Ezakiella. Trajectories of microbiota development appear to have a slower transition from Community_1 to Community_2 in the azithromycin arm (from 2.4% at day 6 to 30.4% at day 28) when compared to the placebo arm (from 34% at day 6 to 69.2% at day 28) (Figure 6C). Interestingly, we observed a gradual increase of Peptoniphilus, Anaerococcus, and Ezakiella with age (Figure 7), which seems to be uncommon in previous studies withing West Africa.Figure 6 Microbiota community types (clusters) and development trajectory

(A) Microbiota community types based on highest abundant taxa generated by Dirichlet Multinomial Mixtures model and their frequencies between trial arms at each time point.

(B) Heatmap generated by hierarchical clustering showing relative abundance of the top 20 OTUs across the three community types.

(C) Microbiota trajectories showing progression of individuals through the three community types over time in either trial arm. The size of the edges indicates number of individuals, and the color indicates frequency of transitions.

Figure 7 A summary of count and relative abundance of the genera Anaerococcus, Ezakiella, and Peptoniphilus

(A) Abundance of the three genera in true samples stratified by time point (age) and in controls stratified by control type. For each genus, each data point shows the abundance in an individual sample or control. For each genus in a sample, only counts ≥3 are shown.

(B) Relative abundance of each genus over time stratified by trial arm. Both arms show increasing abundance of all three genera with age.

Discussion

While intrapartum azithromycin has demonstrated no effect in reducing neonatal sepsis or mortality, it has shown a positive impact on decreasing short-term morbidity for both mothers and neonates. However, the influence of this intervention on the gut microbiota during the neonatal period and beyond had not been previously explored. Our study shows that the administration of a single dose of 2 g oral azithromycin to mothers during labor had a transient but strong effect on the development of the child’s gut microbiota. Specifically, the intervention reduced abundance of Bifidobacterium and increased abundance of Enterobacteriaceae during the first week of life. Although Shannon diversity remained unaffected by the intervention, we observed an 8% change in the overall community composition at day 6, which decreased to 2% by day 28. The intervention also caused a delay in the progression of the child’s gut microbiota development. As expected, age emerged as a primary driver of microbiota changes, with both trial arms converging to the same microbiota type by year 3.

Most of the alterations observed among children whose mothers took intrapartum azithromycin have the potential to disrupt the balance of the gut microbiota. The most striking changes happened at day 6 and included the decrease of Bifidobacterium, Bacteroides, and Collinsella, coupled with the increase of Enterobacteriaceae and Enterococcaceae. These findings align with previous research involving neonates treated with antibiotics22 or exposed to them via caesarean section delivery.25 The decrease of Bifidobacterium species is concerning as these bacteria are important symbionts in the infants’ gut, aiding in the digestion of human milk oligosaccharides and facilitating the release of essential nutrients from breast milk for the host.21 In addition, prior research has suggested that higher abundance of Bifidobacterium and Collinsella in the first few months of life favors maintenance of a healthy body weight in growing infants.26 While the abundance of Bifidobacterium and Collinsella was lower in the azithromycin arm at day 6, a critical period for severe neonatal infections, it is reassuring that the effects were transient and waned by month 4. This short-lasting effect of the intervention on Bifidobacterium in the child’s gut microbiota is likely driven by active breastfeeding, a nearly unanimous behavior in The Gambia, which enhanced recolonization.20 Moreover, anthropometric data from the former trial in The Gambia using the same intervention (namely PregnAnZI-1) showed a lower prevalence of undernutrition at 1 year of age among children whose mothers received intrapartum azithromycin compared to those whose mothers received placebo.27

Some Enterobacteriaceae species can be highly pathogenic in neonates, and therefore, the observed increase in the gut of children from the azithromycin arm also needs to be highlighted. The increase of Enterobacteriaceae, which extended to day 28, can be attributed to the ability of these bacteria to acquire resistance genes via horizontal gene transfer, facilitated by mobile genetic elements. Newborns may acquire some of these bacteria from the hospital environment,28,29 including key species of clinical and public health importance such as Escherichia coli and Klebsiella pneumoniae.30 The higher prevalence of Enterobacteriaceae in the azithromycin arm was observed by an increased abundance at day 6 and an increased prevalence at day 28 of OTU00003-Enterobacteriaceae. These differences between trial arms likely resulted from the selection of resistant species within this family coupled with the decreased abundance of other commensal bacteria as a result of azithromycin, as the case for Bifidobacterium explained above. This agrees with microbiology data from the PregnAnZI-2 carriage sub-study cohort in The Gambia, which showed an overall increase of K. pneumoniae carriage at days 6 and 28.31 In the same study though, microbiology data showed a reduction of E. coli carriage among newborns whose mothers took intrapartum azithromycin at both day 6 and day 28 but this decrease was coupled with an increased prevalence of carriage of azithromycin-resistant E. coli during the early neonatal period.31 Notwithstanding, the clinical results of the main PregnAnZI-2 trial did not show any increase in disease caused by E. coli and K. pneumoniae or other Enterobacteriaceae. However, the number of sepsis cases bacteriologically confirmed was little, and Enterobacteriaceae represented a small percentage of the overall numbers in the cohort.8 Although our analysis could not reach species level for some OTUs, the method of classification used is important for avoiding potential misclassifications.

The transient increase of Enterococcaceae at day 6 may also pose potential risks due to the presence of opportunistic pathogens like E. faecalis and E. faecium within this bacterial family. While normally constituting a minor portion of the gut microflora,32 these bacteria are known for their resilience to antimicrobial stress and have been linked to hospital-acquired infections including bacteremia, intraabdominal infections, and mixed infections with obligate anaerobes.33 Therefore, the increased presence of Enterococcaceae, though uncertain if driven by these specific species, necessitates further investigation in future microbiology or genomic studies of similar cohorts.

For Staphylococcus, despite a previously reduced abundance at day 6 among study children whose mothers took intrapartum azithromycin,24 such reduction was not observed here in the children’s gut. Still, the intervention reduced diseases caused by S. aureus such as skin infections, and a decreasing trend was observed for S. aureus neonatal sepsis.8 This may suggest niche-specific and/or species-specific variations in the impact of the intervention on Staphylococcus.

The shifting community types over time provide highlights into the dynamic nature of the child’s gut bacterial community. Initially prevalent at day 0, Community_1 represents a diverse seeding microbiota composed of obligate and facultative anaerobes likely derived from the maternal recto-vaginal flora and the hospital environment. The gradual decrease of Community_1 and increase of Community_2 from day 6 to month 4 reflects the development of a niche-specific community type influenced by site-specific physiological conditions such as increased anaerobic conditions, microbial interactions, and the child’s diet.34 The progressive increase of Community_2 during the first 4 months suggests a symbiotic relationship between Bifidobacterium and the breastfeeding child,21 as all children in the study were actively breastfed during this period. Notably, children from the azithromycin arm showed a lower representation of Community_2 at day 6 and day 28, attributable to delayed colonization due to the antibiotic, evidenced by a slower progression from Community_1 and a reduced number of observed OTUs at these time-points. The temporality of these effects is likely influenced by active breastfeeding, and therefore, different results may be observed in populations with lower prevalence of or shorter durations of breastfeeding.

The complete progression of all children in both trial arms to the same community type (Community_3) at year 3 indicates that azithromycin did not alter the maturation of the gut microbiota of the children. Such maturation pattern of the gut microbiota among our study children exhibits both shared and distinctive features compared to data from rural Gambia and other African studies. The abundance of Prevotella increased with age, likely driven by gradual introduction of solid foods. At year 3, all the children had stopped breastfeeding about a year earlier, and their gut microbiota have shown increased representation of Prevotella (Figure S8), highlighting the important role of these bacteria in the metabolism of complex plant polysaccharides and thus reflecting changes to an adult-type diet.35,36 This feature is similar to what was reported by Goffau et al., in another cohort of similarly aged children from rural Gambia,37 and elsewhere in Ghana,38 and it is typical of non-industrialized settings.39 However, high abundance of Anaerococcus, Ezakiella, and Peptoniphilus at year 3 was not observed by earlier studies on stool samples, perhaps because these bacteria are specifically associated with the rectal microbiota in older children and adults.40 The gradual increase of these bacteria with age thus suggests their establishment in the rectal microbiota of the children in our cohort. This is an interesting finding, which highlights possible microbiota variations between rural and urban settlements in Sub-Saharan Africa, and further echoes the need to understand microbiota development in the contexts of the different heterogeneous sub-populations across African settings.

An important strength of this study is that it was embedded into a well-designed and robustly conducted clinical trial, and all sample and data collections were carried out following standardized protocols. Moreover, adequate controls were included to check every stage of the process from sampling to analysis, thus ensuring the validity and integrity of the results generated.

In conclusion, the impact of intrapartum azithromycin on the child’s gut microbiota was complex, encompassing both potentially beneficial but mostly undesirable results. The observed neutral outcomes of intrapartum azithromycin on neonatal sepsis and mortality from our trial may be explained by the effects observed here in the gut microbiota, juxtaposed with the predominantly potentially beneficial outcomes observed in a parallel analysis of the nasopharyngeal microbiota.24 The changes observed here, however, were transient and did not significantly alter the gut microbiota development trajectory of the child. Finally, it will be important to understand the effects of this intervention on the overall gut resistome and the potential long-term public health implications.

Limitations of the study

The following are limitations of this study that need to be acknowledged. Firstly, the methodology employed describes the impact of intrapartum azithromycin on the gut bacterial community of the child up to genus level. Consequently, caution is warranted in interpreting species level effects. Secondly, we used rectal swab (RS) as proxy for stool to assess the gut microbiome. While RS generally exhibits lower biomass compared to stool, recent studies have shown that RS is a viable alternative for the analysis of gut microbiota composition and function.41,42 Thirdly, there appeared to be a trend of association between season and microbiota composition at day 28. Though we did not find evidence of interaction between season and the effect of intrapartum azithromycin, we could not dismiss the possibility of seasonal variation in the impact of the intervention on the gut microbiome of the child. Fourthly, we did not link the microbiota data with growth or morbidity in early childhood and therefore cannot show any direct biological or clinical significance with our results.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Biological samples	
	
Rectal swabs of healthy babies whose mothers took 2g oral azithromycin or placebo during labour	PregnAnZI-2 trial, MRCG@LSHTM biobank	N/A	
	
Deposited data	
	
Raw fastq files	This paper	SRA: PRJNA1129542	
	
Oligonucleotides	
	
Barcoded 16S V4 primers	Fadrosh et al.43	N/A	
	
Software and algorithms	
	
FastQC	Babraham Bioinformatics	http://www.bioinformatics.babraham.ac.uk/projects/fastqc/	
Trimmomatic	Bolger et al.44	https://doi.org/10.1093/bioinformatics/btu170	
mothur	Schloss et al.45	https://doi.org/10.1128/AEM.01541-09	
R	http://www.r-project.org/	RRID:SCR_001905	
rStudio	https://posit.co/	RRID:SCR_000432	
decontam	Davis et al.46	https://doi.org/10.1186/s40168-018-0605-2	
Tidyverse	Posit	https://www.tidyverse.org/packages/	
phyloseq	McMurdie and Holmes,47	https://doi.org/10.1371/JOURNAL.PONE.0061217	
Vegan	Oksanen et al.48	https://cran.r-project.org/web/packages/vegan/vegan.pdf	
metagenomeSeq	Paulson et al.49	https://doi.org/10.1038/nmeth.2658	
microbiomer	https://github.com/wsteenhu/microbiomer	N/A	
	
Other	
	
Silva rRNA data base	http://www.arb-silva.de/	RRID:SCR_006423	
mothur MiSeq SOP	https://mothur.org/wiki/miseq_sop/	N/A	
Code for microbial community analysis	This paper	Method S1	

Resource availability

Lead contact

Requests for further information and resources should be directed to the lead contact, Prof Anna Roca (aroca@mrc.gm).

Materials availability

This study did not generate new unique reagents.

Data and code availability

• The raw fastq files have been deposited on SRA and are publicly available. The project accession number is listed in the key resources table.

• The code used for 16S analysis with mothur is publicly available and the source is listed in the key resources table. The code used for microbial community analysis in R are provided in Method S1.

• Any additional information required to reproduce the data reported in this paper is available upon request. The data in this study has been collected following provision of informed consent under the prerequisite of strict participant confidentiality. Access can be requested through the Gambia Government/MRC Joint Ethics Committee. The review process and release of data will be facilitated by MRC Unit The Gambia (http://www.mrc.gm/) through the Head of Governance at MRCG, Elizabet Batchilli (ebatchilli@mrc.gm) and the lead contact Prof Anna Roca (aroca@mrc.gm).

Experimental model and study participant details

Trial design

PregnAnZI-2 (ClinicalTrials.org NCT03199547) was a phase-III, double-blind, randomised, placebo-controlled trial in which 11,983 women from The Gambia and Burkina Faso were randomized to receive a single dose of 2g of oral azithromycin or placebo (ratio 1:1) during labour. The median time from administration of azithromycin to delivery was 1.6 hours (IQR 0.5 – 4.1).8 Details of inclusion and exclusion criteria can be found elsewhere.7

In The Gambia, the trial was conducted in two health facilities located in the coastal region of the country. In The Gambian cohort, a subset of 253 mother-baby pairs were recruited in a bacterial carriage sub-study. These were randomly selected study children born between January 2019 and March 2020.7 After post-delivery hospital discharge, these mother-baby pairs were followed for 4 months and different biological samples including infant rectal swabs (RS) were collected using FLOQSwabs (COPAN, REF:519CS01) during home visits. Samples were collected in skim milk-tryptone-glucose-glycerine (STGG) transport medium without preservative and transported in temperature-monitored (2 – 8oC) cooler boxes with ice packs to the laboratory within 8 hours. Upon reception, the samples were homogenized and stored at -80oC for later processing.

Microbiome study of children’s rectal swabs (RS)

We selected 102 children out of the 253 recruited in the carriage sub-study of the PregnAnZI-2 trial in The Gambia to be part of the microbiome study. These 102 children were the first term-born, healthy, and vaginally delivered that were part of the carriage sub-study. Baseline characteristics of the children selected for the microbiome study are summarised in Table 1. At the time of selecting these children, the trial was still blinded. As the ratio of azithromycin to placebo in the trial was 1:1, we expected a similar number of pairs in both trial arms by conducting a random selection. Figure 1A illustrates the sample selection process.

For the 102 children selected, we included rectal swabs (RS) collected at 4 different time points: day 0, day 6, day 28, and 4-months, with an expected total of 408 RS. Overall, 387 RS (95% of the expected samples) were collected from these selected children, of which eight samples had errors in the identification numbers and were excluded. The remaining 379 RS were taken forward for 16S-rRNA sequencing. The number of samples per time-point ranged from 41 – 46 in the azithromycin arm, and 49 – 52 in the placebo arm (Figure 1B).

Additional long term (3-years) survey

We extended the original carriage sub-study trial design, which involved a 4 months follow-up period, by incorporating a follow-up visit when the children included in the microbiome analysis turned 3 years of age. During this visit, we collected an additional RS to further assess the effect of the intervention on gut microbiota maturation. At the time of this long-term survey the trial had already been unblinded. However, the research teams involved in both the field and lab work remained blinded to the trial arm allocation. We initially approached the same 102 children previously selected for the microbiome study. Two (2) of the children moved out of the study area, six travelled, 21 were unreachable, and one declined to participate. The remaining 72 children were included (70.6%) in this survey. To increase the sample size to similar numbers as the previous time-points, we added an additional 25 children (Azithromycin = 10, Placebo = 15) randomly selected from the same carriage sub-study in the PregnAnZI-2 trial Gambian cohort. This resulted in a total of 97 children (42 azithromycin, 55 placebo) participating in this long term (3-years) survey.

Collection of RS and environmental controls

The same materials and protocols for sample collection and transport were used for all time points, including the additional long-term survey (see extended details of methods in Methods S2 and S3). The staff collecting RS were experienced nurses specially trained for the study procedures. They were trained before starting the main trial, and a refresher training was conducted prior to the long-term survey. Details of sample collection procedures can be found elsewhere.7 To control for contaminants from sample collection materials and the environment, we collected field controls during both the main study and the 3 years long-term survey. A sample storage vial with 1ml of plain STGG and a sample collection swab was exposed to the environment at the sample collection site for about 1 minute and used as a field control. For the main study, this was done at the hospital site where samples were collected from the participants, whereas for the 3 years survey every participant household had a control. Samples and controls were transported to the laboratory within 8 hours of collection at temperatures between 2°C – 8°C.

Overall RS samples included

Overall, 476 RS from all 5 timepoints (379 from the initial 4-months follow up and 97 from the 3-year follow up) were included in this microbiota analysis. Figure 1B shows the distribution of the samples between trial arms at each time-point.

Ethical approval

The main trial (PregnAnZI-2) was approved by the Gambia Government-MRCG Joint Ethics Committee, the Comité d’Ethique pour la Recherche en Santé, the Ministry of Health of Burkina Faso, and the London School of Hygiene and Tropical Medicine Ethics Committee. In addition, this post-hoc study was approved by the Gambia Government-MRCG Joint Ethics Committee and the London School of Hygiene and Tropical Medicine Ethics Committee. Study women signed informed consent for the trial during their antenatal visits and samples collected during the 4-months follow-up. An additional consent was sought from mothers of the study children for the 3-year survey.

Method details

DNA extraction and quality control

DNA extraction was done using DNeasy® PowerLyzer® PowerSoil® Kit from Qiagen (Qiagen, Germany) with slight modification in the lysis step adapted from elsewhere.50 100μl of homogenized RS in 1 ml of STGG transport medium was used as input and two rounds of 10 cycles of beat beating at 2500 RPM for 30 seconds was applied with 10 minutes break between rounds. Blank extraction and field controls were included and taken through all downstream analyses. Plain sterile swabs in sample storage vials with STGG were exposed to the environment at the field site and used as field controls. DNA quantification pre and post amplification was done with a Qubit 4·0 Fluorometer (Invitrogen/Thermo Fisher Scientific) using the double stranded DNA high-sensitive kit.

Library preparation and sequencing

Using indexed primers for the 16S V4 region (515F and 806R) developed by Fadrosh and colleagues,43 libraries were made in a one-step PCR approach in three batches of fairly even sample sizes (∼260 per batch). An inhouse mock community consisting of 9 bacterial species and non-template PCR controls were included. Each batch was pooled at equimolar sample concentrations and sequenced on a MiSeq (Illumina Inc., San Diego, CA) using the paired end v3 kit (600 cycles).

Bioinformatic analysis

Quality filtering and denoising

Quality of raw sequences was assessed using FastQC (version 0·11·8).51 Sequences were then trimmed using trimmomatic (version 0·39).44 Leading and trailing ends were trimmed at thresholds of Q30 and Q20 respectively and then paired sequences with average quality of Q25 and minimum length of 36bp were selected. Paired trimmed sequences were analysed using mothur (version 1.44.0),45 following the miseqSOP.52 After denoising and chimera removal, sequences were classified using the Silva rRNA gene database (Silva version 132) by naive Bayesian classification using the Wang method,53 at a bootstrap confidence of 80%. Unclassified sequences were removed. The remaining sequences were binned into operational taxonomic units (OTUs) at a distance cut-off of 0·03. Singleton OTUs and OTUs present in < 1% of the samples were excluded. We appreciate that bacteria or bacterial DNA from the environment and reagents including STGG can impact the bacterial composition of the samples and therefore we used decontam (version 1.20.0),46 to identify contaminant bacteria present the extraction blanks, PCR blanks, and the field controls. A total of 120 contaminant OTUs were identified with decontam and removed (Table S3). One OTU (Otu00007-Streptococcus) which was present in all the year-3 field controls at similar relative abundance, higher than the relative abundance in true samples and other controls (Figure S7), was not detected by decontam and thus we removed it manually. This OUT showed a higher relative abundance in the field controls than in the true samples indicating that it is a contaminant. A total of 1,071 OTUs remained in the cleaned dataset. A summary of the read counts and genus profiles of the controls pre- and post-filter are shown in Figure S1. Though we applied a rigorous technique to remove significant contaminants, we acknowledge that STGG may not perform equally in preserving microbiota community as other transport media specifically designed for microbiome sample collection.

Microbial community analysis

Microbial community analysis was mainly done in R (version 4·3·1) and RStudio (version 1.4.1103).54 The Tidyverse (version 2.0.0), and phyloseq (version 1.48.0),47 packages were mainly used for data handling and organization. The relative abundances of phyla and genera were calculated and visualised, grouped by treatment and timepoint. The dataset was then rarefied by sub-sampling to 3000 sequences for each sample with 1000 iterations before diversity and differential abundance estimation using the vegan (version 2.6.4) package,48 and metagenomSeq (version 1.38.0) respectively. Within-sample diversity (alpha-diversity) was estimated using Shannon index and OTU richness. Overall microbiota composition (beta-diversity) was calculated using Bray-Curtis dissimilarity index. Visualizations were done using ggplot2 (version 3.4.2) package. To evaluate if samples were characterized by distinct community-types, we applied an unsupervised clustering using Dirichlet’s Multinomial Mixture Models (dmm),55 implemented in mothur. We showed the representation of the top 20 OTUs in the dataset across these community-types using a heatmap generated by hierarchical clustering using microbiomer.56

Quantification and statistical analysis

Power calculation

We calculated sample size based on power to detect at least 10% difference in the top 10 operational taxonomic units (OTUs) and 20% difference in the next 10 OTUs in the gut microbiota using a sample size and power calculation tool for case-control microbiome study design developed by Mattiello et al.57 We used the gut microbiome dataset from the human microbiome study embedded in the tool. With a sample size range of 30 -70 per group, we estimated power by Monte Carlo simulations with 100 replications using the top 50 OTUs from the dataset. A sample size of 45 per group at each timepoint had over 90% power to detect these differences (Figure S8).

Statistical analysis

Alpha-diversity (Shannon and OTU richness) was compared between trial arms at each time-point by Wilcoxon Rank Sum test. Changes in Shannon diversity over time was assessed using a linear mixed effects model. Mother’s ethnicity and child’s birth season (i.e. birth in rainy season or dry season) were both added in the model as covariates because (i) ethnic representation varied between trial arms at baseline and (ii) season is a potential microbiota covariate,58 and the Gambia has two very different annual seasons. P-values were adjusted by Tukey’s correction. Beta-diversity was compared between trial arms at each time-point by permutational multivariate analysis of variance (PERMANOVA) using the adonis function in vegan. Beta-diversity was also compared across timepoints for the overall dataset and within each trial arm using the adonis function with permuations restricted within subjects. We assessed differential OTU abundance (i.e. difference in OTU abundance) between trial arms at each time-point using the Fit-feature model in metagenomeSeq (version 1.38.0) package.49 We also compared OTU prevalence between trial arms at each time-point using the fitPA model in metagenomeSeq. To exclude rare OTUs, for each time-point OTUs with at least 30% prevalence and represent ≥ 0·1% of the reads in each sample were included in differential abundance and prevalence analysis. Also, p-values were corrected for false discovery (FDR).

Additional resources

The PregnAnZI-2 trial was registered at ClinicalTrials.gov: https://clinicaltrials.gov/study/NCT03199547?term=NCT03199547&rank=1.

Supplemental information

Document S1. Figures S1–S8, Tables S1 and S2, and Methods S1–S3

Table S3. Excel file containing list of contaminant OTUs, which is too large to fit in a PDF, related to STAR Methods

Acknowledgments

Our sincere thanks and appreciation goes to the PregnAnZI-2 study participants, both mothers and their children, for taking part in this trial. We would like to acknowledge the entire PregnanAnZI-2 team, including Prof Umberto D’Alessandro and Prof Halidou Tinto (co-investigators), the coordination team in the Gambia (led by Dr Bully Camara), the field, lab, and the data management teams. We thank the funders 10.13039/100014013 UK Research and Innovation (MC_EX_MR/P006949/1 ), 10.13039/100000865 Bill & Melinda Gates Foundation (OPP1196513 ), and MRCG@LSHTM Doctoral Training Program. We also thank our collaborators who have contributed to this work, including the Ministry of Health, Gambia, Bundung Maternal and Child Hospital, Serrekunda Health Centre, and the MRCG@LSHTM Genomics Core Platform.

Author contributions

This study was conceived and designed by A.R. and supported by A.K.S., T.d.S., and B.S. Sample selection was carried out by A.R. and B.S. The field work was led by B.C. and N.B. The laboratory work including DNA extraction, library preparation, and sequencing was carried out by B.S. with support from J.M. All laboratory work was supervised by A.K.S. B.S. carried out the primary bioinformatic analysis with support from A.K. 16S microbial community analysis was carried out by B.S. and supported by T.d.S., W.A.A.d.S.P., and D.B. B.S. and A.R. drafted the manuscript. T.d.S., D.B., W.A.A.d.S.P., A.K.S., B.C., and A.K. contributed to editing the manuscript. All authors approved the last version of the manuscript.

Declaration of interests

All the authors declare no conflict of interest.

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110626.
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