Early-life PAT alters murine growth

Female C57BL/6J mice received three short courses of therapeutic-dose amoxicillin, tylosin, alternating courses of either antibiotic (mixture) or no antibiotics (control) (Fig. 1a and Supplementary Fig. 1). To mimic early life use by human children, the PAT was completed shortly after weaning, and then mice received high-fat chow to enhance metabolic phenotypes13. After one pulse, all mouse groups had identical weights on day 21 (Fig. 1b), but early-life PAT significantly increased the cumulative weight gain from 3 to 6 weeks, an effect continuing through late life in tylosin mice (Fig. 1c and Supplementary Fig. 1b).

Figure 1: Effect of PAT on growth. (a) Timeline of antibiotic pulses and dietary changes. (b) Scale weight of mice. (c) Growth rates in early-, mid-, and late-life expressed as percent difference from control. (d–h) Dual energy X-ray absorptiometry measurements. (d–f) Total, lean and fat mass; (g) bone mineral content (BMC) and (h) bone area. (i) Calculated total body mass-to-bone area ratio is represented as a fraction of control. *P<0.05, **P<0.01, ANOVA with Dunnett's post test. (c–h) Bars represent standard error of the mean. Number of mice: control, n=6; amoxicillin, n=6; tylosin, n=7 and mixture, n=8. Full size image

Tylosin and amoxicillin PAT had different effects on body composition. Compared with controls, tylosin significantly increased both total and lean mass, while amoxicillin only significantly increased lean mass (Fig. 1d–f). All groups of PAT mice developed larger bones than controls (Fig. 1g,h), but increases in bone area and mineral content were most pronounced in the amoxicillin group, demonstrating body composition variation based on antibiotic class. When mass-to-bone ratio (total body mass/bone area) was calculated as a proxy for body mass index (Fig. 1i), the tylosin and mixture mice both gained mass/area at significantly higher rates than control (P=0.015 and 0.005, respectively; longitudinal analysis using the linear spline models). Altogether, these data demonstrate significant early-life PAT-antibiotic-specific growth promotion.

Effects on host physiology

As hepatic metabolism can be altered with early-life antibiotic exposure13, potentially as a result of altered growth, direct effect of antibiotics or influence of the microbiota through the enterohepatic circulation, we examined whether we could detect PAT effects on the liver long after antibiotic cessation. Early-life tylosin significantly elevated micro- and overall hepatic steatosis in later life (Supplementary Fig. 2a–e), while amoxicillin significantly reduced microsteatosis. All groups had similar liver mass (Supplementary Fig. 2f). PAT induced changes in hepatic gene expression, with more upregulated than downregulated genes with respect to controls (Fig. 2a). While few genes had been significantly modulated by both the early-life tylosin and amoxicillin exposures (Fig. 2b–d), hierarchical clustering of genes significant in either group (Fig. 2e) indicates similar trends in both antibiotic groups.

Figure 2: PAT alters hepatic gene expression. (a) Number of differentially expressed genes (P<0.01 and |log 2 fold change|>0.5) that are up- or down-regulated in the underlined group with respect to the comparator group. (b,c) Venn diagrams showing numbers of upregulated or downregulated genes, respectively, that are shared or unique. (d) Principal component analysis plot of hepatic gene expression data, representing 30.2% of total variation. (e) Expression of hepatic genes significantly altered by amoxicillin or tylosin with respect to control. (f) Predicted biological functions that are differentially represented (P<0.05, z-score |2|) based on Ingenuity Pathway Analysis of hepatic expression. Amox., amoxicillin; Ctrl, control; Tylo., tylosin. Full size image

Altered biological functions, predicted by Ingenuity Pathway Analysis, revealed antibiotic-specific effects; several pathways, including those related to gene expression, cancer, and organismal survival, were abnormal in both groups (Fig. 2f, green bars). Early-life tylosin exposure also increased expression of genes relating to lipid metabolism and cellular movement and assembly, consistent with the increased steatosis. Both by microarray and qPCR, Hspb1, a heat-shock protein reported as microbiota modulated15, was significantly downregulated in mice receiving either antibiotic (Supplementary Fig. 2g). The alterations in hepatic gene expression, long after the final antibiotic course, demonstrate that these early-life exposures have metabolic influences, partially conserved across both antibiotic classes, which can be detected long after antibiotic exposure.

To investigate systemic impacts of PAT, metabolic hormones were measured in fasting serum samples at sacrifice. Early-life tylosin or mixture PAT significantly reduced ghrelin compared with controls, whereas amoxicillin showed a lesser effect (Supplementary Fig. 3a–e). Peptide YY, leptin, amylin and insulin did not significantly differ between the groups. As expected, leptin levels were positively correlated with fat mass in control mice alone, PAT mice alone and in all mice (Supplementary Fig. 3g–i). Peptide YY negatively correlated with fat mass in the control mice, as expected, but not in the PAT mice (Supplementary Fig. 3j–l), indicating that PAT had disrupted this metabolic relationship. In all groups, faecal caloric contents decreased over time (Supplementary Fig. 3f), not differing between PAT and control.

PAT-induced microbiota disruption

Microbiota composition was surveyed by sequencing the 16S rRNA gene from serial PAT and control pup faecal pellets and from representative mothers (Fig. 3a), yielding 2,683,548 quality-filtered sequences with a mean±s.d. depth of 6,899±3,009 sequences per sample. The richness and Shannon Index remained relatively constant in control mice, with α-diversity similar to their mothers; however, PAT decreased both richness and Shannon evenness even after one antibiotic pulse, with immediate and sustained reductions in diversity more pronounced following tylosin exposure (Fig. 3b). While amoxicillin resulted in milder reductions, there was a progressive loss with each dose, indicating the importance of both class and number of courses. Although the PAT groups largely converged with control over time, differences never fully resolved.

Figure 3: Ecological outcomes from early-life PAT and response to dietary intervention. (a) Experimental design and timing of microbiota samples. (b) α-diversity measured at a coverage depth of 3,000 sequences per sample. (c) Differentiating bacterial families immediately before and after introduction of HFD. Area-under-curve (AUC) with 95% confidence intervals (grey lines) for differentiating pre-HFD and post-HFD mice is plotted by treatment group in major families (>1% relative abundance in at least one mouse). Significantly predictive results (Mann–Whitney test) after false-discovery rate correction (q<0.05) are indicated by grey-filled circles. Full size image

We next examined compositional changes at the phylum level by qPCR and high-throughput sequencing. Following antibiotic cessation and switch to HFD, Bacteroidetes in the mixture group and some of the tylosin mice were dramatically reduced compared with control, whereas little change was observed in the amoxicillin mice (Supplementary Fig. 4a–d). Beyond changes at the phylum level, the microbiota from mice receiving tylosin or the mixture had significantly different overall microbiota composition from controls (adonis test of unweighted UniFrac distances with Bonferroni-corrected P-value<0.05; Supplementary Fig. 4e), detected as early as the first time point (one antibiotic pulse), extending to months after antibiotic cessation. For amoxicillin, a significant shift only was detected after all three pulses, and overall community structure did not differ from controls 1 week after antibiotic cessation and switch to HFD, indicating the much more prolonged effects of the macrolide-based treatments compared with the beta-lactam. Unweighted UniFrac distances by group and cage (Supplementary Fig. 4f) revealed that control microbiota remained relatively homogenous over time (cage 1 and 2); one cage of amoxicillin mice had microbiota similar to control (cage 3), while the other two showed divergence from control either immediately after three pulses of antibiotic (cage 5) or weeks after antibiotic cessation (cage 4). Tylosin and mixture microbiota behaved similarly, regardless of cage, with the greatest divergence after three pulses of antibiotic, and gradually approaching control composition by the end of the experiment. These data indicate that the strong perturbation by macrolide produced consistent effects, whereas the milder disruption by beta-lactam had more variation in microbiota effects.

To identify key members of the microbiota associated with a rapid transition to HFD, we performed area-under-the-curve analysis (Fig. 3c). Especially for the controls, multiple families changed significantly (false-discovery rate-adjusted P-value, q<0.05, Mann–Whitney U-test), either increasing or decreasing after HFD commencement. Members of the phylum Firmicutes have been reported to increase with HFD exposure at the expense of Bacteroidetes16, and such patterns were observed in control mice with the increase of Erysipelotricaceae, Ruminococcaceae, Streptococcaceae, unclassified Clostridiales and Firmicutes other, and the decrease of Rikenellaceae, Prevotellaceae, Bacteroidales other and Bacteroidetes other. Many similar families were changed in the amoxicillin mice in the same direction, but were not significant. In tylosin mice, changes were partially in the same direction (Streptococcaceae, Clostridiales other, Firmicutes other and Prevotellaceae) and partially in the opposite direction (Erysipelotrichaceae, Ruminococcaceae, Rikenellaceae, Bacteroidales other and Bacteroidetes other). Thus, the antibiotic exposures modified typical microbiota responses to HFD, with more aberrant responses observed with the macrolide than the beta-lactam.

PAT delays microbiota maturation

Because of the importance of the developing microbiota, we compared the relative maturation rates of control and antibiotic-perturbed microbiota using a Random Forests17 regression model to predict day of life as a function of microbial composition18. Microbial maturity of control samples could be accurately predicted (Supplementary Fig. 5a) using 42 key operational taxonomical units (OTUs; Fig. 4a). Most of these 42 biomarkers showed marked population increases after HFD initiation, with delayed responses in the tylosin and mixture groups (Fig. 4b) accounting for their persistent microbial immaturity through day 142 (Fig. 4c). As the effects of diet and ageing cannot be separated, we constructed a second maturity model for the period of early life before HFD (Supplementary Fig. 5b). In the normally developing (control) mice, several OTUs predominated after weaning, diminished over time and were succeeded by other OTUs. Amoxicillin caused minimal disruption, whereas mixture and tylosin treatment substantially reduced OTUs associated with normal maturation (Supplementary Fig. 5c).

Figure 4: The effect of early-life PAT on microbiota maturity and dietary responses. (a) The OTUs that predict maturity and explained the greatest degree of variation in the model, ranked by contribution to reduction of mean square error (MSE). (b) Abundance of predictive OTUs over time. Dashed lines indicate introduction of HFD, time points 1–14 correspond to sequential samples (correlating with increasing day of life). (c) Average MAZ over time; z-score=0 indicates appropriate maturation; higher or lower z-scores indicate accelerated or delayed microbiota development, respectively. *** P<0.001 one-way ANOVA with Fisher’s least significant difference adjusted for false-discovery rate. Full size image

Microbiota-by-age z-scores (MAZ)18 can quantify delayed or accelerated microbiota development in response to an exposure. Dietary composition strongly influences intestinal microbiota19 and thus microbial age predictions; accordingly, some of the control samples immediately shifted after HFD initiation, predicting an older age (Fig. 4c). PAT delayed both maturation and response to HFD, with the greatest effects from tylosin or mixture exposure. The first antibiotic pulse had no effect on microbial maturity, but MAZ dropped substantially after the second pulse, progressively decreasing during the third pulse. The amoxicillin group approached control between pulses, and the MAZ score converged with control after ∼1 week of HFD. The tylosin and mixture groups trended towards control on HFD, but never converged. In total, antibiotic exposure delayed microbiota maturation and response to diet, with both the number of courses and antibiotic class determining the extent of disruption.

Linking perturbation with outcomes

Grouping samples by composition facilitates characterization of microbiota perturbations. Following evidence that human microbiota may segregate into clusters20, we asked whether the PAT-induced microbial shifts could be similarly characterized, and found that four distinct groups best captured the taxonomic variation (Supplementary Fig. 6a). Offspring control microbiota stably clustered with the maternal samples initially (cluster 1), then shifted to a new community state (cluster 4) immediately following introduction of HFD (Fig. 5a). Amoxicillin samples showed mild disruptions for half of the mice after at least two pulses, but converged with controls in cluster 4 shortly after HFD introduction. In tylosin and mixture mice, one tylosin pulse immediately disrupted microbiota, shifting samples to cluster 2 (mildly altered state) or cluster 3 (markedly altered from control). All samples shifted to cluster 3 following at least two doses of tylosin. As with amoxicillin, the samples eventually converged in cluster 4 following HFD, but this recovery was delayed and followed a distinct trajectory (Fig. 5b,c). These community dynamics indicate that sequential antibiotic treatments were more disruptive than single antibiotic exposure, show that tylosin had greater effect on the gut microbiota than amoxicillin and confirm the strong effect of diet on the microbiota19.

Figure 5: Dynamics of disruption, recovery and response to HFD. (a) Community structure over time within the four clusters identified by Calinski analysis shown for mothers and for pups at selected representative time points: after the first, second and third antibiotic pulses and after starting HFD. (b) Cluster assignment by mouse and time point. a, antibiotic group; c, cage (bars indicate mice in the same cage); m, mouse. Time points 1–14 correspond to sequential samples (correlating with increasing day of life). (c) Microbiota transition map, circles and lines are scaled to represent number of mice in each cluster (circle) or transitioning (line) between clusters. (d,e), Body composition grouped by day 50 cluster type at ∼50 days of life (d) and at ∼135 days of life (e). *P<0.05, **P<0.01, ***P<0.001, ANOVA with Tukey-post test. C, control; A, amoxicillin; T, tylosin; M, mother. Full size image

Each cluster was characterized by different dominant microbial populations at the phylum and OTU levels (Supplementary Fig. 6b–c). In maternal and normal chow-associated cluster 1, Bacteroidetes (B) and Firmicutes (F) dominated (B>F) and had higher levels of Lactobacillus than other clusters. In response to HFD (cluster 4), the B/F proportions reversed, and the genus Allobaculum, known to be HFD associated13, increased. Similar to HFD, both clusters 2 and 3 (associated with antibiotics) also had F>B, and had large blooms of Verrucomicrobia (Akkermansia), small blooms of Proteobacteria (Enterobacter) and reductions in other taxa. Bacteroidetes was almost completely lost in the tylosin-associated cluster 3, demonstrating that the composition of this cluster was more deviated from baseline than that of the cluster associated with either amoxicillin or one pulse of tylosin (cluster 2).

Because there was variability in the way mice in each antibiotic group responded to and recovered from antibiotics, we next interrogated whether certain microbiota conformations were associated with changes in body composition. We used the cluster identity at day 50 to categorize how the microbiota recovered from antibiotics and responded to HFD in a short time period, and examined the concurrent and later changes in body composition. We found that mice that had a mild dysbiosis in cluster 2 (including mice receiving amoxicillin, tylosin and mixture) had significantly (P<0.05, analysis of variance (ANOVA) with Tukey post-test) higher total and lean mass, and bone mineral content, area and density at day 50 compared with mice that transitioned directly from normal chow cluster 1 to HFD cluster 4 (all control and three amoxicillin mice) (Fig. 5d). Of these changes, elevated lean mass, bone mineral content and bone mineral density remained significantly elevated at day 135 of life (Fig. 5e). Mice in cluster 3 (tylosin and mixture mice), which exhibited more extensive shifts in the microbiota from control, did not have significant changes in body composition from cluster 2, indicating that a larger perturbation may dampen the growth promotion effect.

PAT alters the intestinal metagenome

In metagenomic analyses of 60 faecal samples subjected to shotgun sequencing at a depth of 5 GB each, all mothers and pre-weaning controls (day 21) segregated together on the basis of hierarchical clustering, with high levels of Lactobacillus, while all but one sample from the antibiotic-treated mice segregated separately and had high levels of A. muciniphila genes (Supplementary Fig. 7). Most (83.3%) of the 18 samples from tylosin mice aggregated together, with substantial Lactobacillus depletion and simultaneous Enterococcus expansion. Consistent with the phylogenetic distribution, nearly all tylosin samples aggregated together when clustered by Kyoto Encyclopedia of Genes and Genomes (KEGG) module abundance (Supplementary Fig. 8), with decreases in modules related to glycolysis, gluconeogenesis and tRNA biosynthesis, among others, and increases in modules related to the citric acid cycle and nucleoside (inosine) and amino acid (leucine) biosynthesis, among others. Collectively, these trends provide evidence that PAT, especially tylosin, shaped metabolic gene populations.

To further examine impact on microbiota function, control and PAT metabolic KEGG module abundance was compared using univariate analyses by LEfSe (ref. 21) (Fig. 6a). Compared with control, tylosin significantly affected several metabolic modules in both early-life (normal chow, pre-day 41; Supplementary Fig. 9b) and late-life (HFD, post-day 41; Supplementary Fig. 10b, Supplementary Table 1). Notably, tylosin shifted carbohydrate glycolytic metabolism, depleting the classic Embden–Meyerhoff pathway while increasing the alternate Entner–Doudoroff pathway. Importantly, the early-life tylosin-altered microbial functions related to energy yield from glucose were maintained after antibiotic exposure ceased and diet changed. Amoxicillin also significantly, but less extensively, altered late-life KEGG modules compared with control (Fig. 6a and Supplementary Fig. 10c). Both antibiotics depleted genes related to glycolysis, isoprenoid biosynthesis, tRNA biosynthesis and ribosomes, and enriched genes related to LPS synthesis, proline and vitamin biosynthesis, pyruvate oxidation and molecular transport (Supplementary Table 1). These data indicate metagenomic changes due to the early-life PAT, persisting well into adulthood. The conserved metabolic effects of both antibiotics indicate global, persistent responses to early-life perturbation.

Figure 6: Metagenomic alterations from early-life PAT. (a) Number of KEGG modules significantly upregulated in PAT and control mice (P<0.05, LEfSe). No modules were significantly different between control and amoxicillin in early life. (b–d) Median relative abundance of oxalate metabolism genes over time. (b) frc, formyl-CoA transferase, (c) oxc, oxalyl-CoA decarboxylase and (d) oxlT, oxalate/formate exchanger. (e) Faecal oxalate levels. (f) Hierarchical clustering of 73 host-associated microbial oxlT orthologs (rows) and the associated 60 shotgun sequencing samples (columns). NC, normal chow. (g) Relative abundance of 67 oxlt orthologs; each cluster group shows a unique presence pattern by condition, diet and time. Full size image

Microbial oxalate-degrading capacity

To assess the impact of PAT on microbial metabolic capacity, we examined the oxalate-degradation pathway, absent in mammals but well-conserved in bacteria. Oxalate is often present in foods of plant origin, and its metabolism and clearance are critical for urologic health in mammals, which rely on gut microbial species for its digestion. We monitored the three microbial oxalate degradation pathway genes, frc, oxc and oxlT (ref. 22), and found that the relative abundance of the linked genes oxc and frc showed nearly identical flux over time in all three experimental groups, whereas oxlT showed overall lower abundances and unique trajectory (Fig. 6b–d). In the controls, relative ortholog abundances generally remained stable near maternal levels, whereas frc and oxc varied dramatically in early-life PAT mice. Compared with maternal samples, faecal oxalate levels were reduced during early life in control and amoxicillin pups, but not in the tylosin pups (Fig. 6e), consistent with simultaneous low oxc and frc relative abundances. Following the second antibiotic pulse, frc and oxc relative abundances declined further in both antibiotic groups. After HFD initiation, as frc and oxc abundances normalized, faecal oxalate levels fell in all groups.

Because different orthologous genes within bacterial populations may account for the substantial intergroup oxlT relative abundance differences, we examined the responsible variants. Hierarchical clustering (Fig. 6f) showed deep branching, with samples from tylosin mice clustering at one pole and those from mother, pre-weaning control and day 21 amoxicillin mice at the other. The tylosin samples showed unique orthologs not otherwise detectable in control. A large ortholog set, detected uniformly across control and PAT mice, appeared solely after HFD introduction. The relative abundance of the oxlT orthologs in the amoxicillin mice over time was similar to controls, whereas tylosin mice were markedly altered in early-life, with partial recovery by late adulthood (Fig. 6g).

Distinct disruption and recovery patterns (p1–p6) could be detected for oxlT, which can serve as a proxy for disruption and recovery of broader microbiota functions (Fig. 6g). Pattern p1 contained orthologs exclusive to PAT mice, predominantly in the tylosin mice, disappearing only in the final sample obtained long after antibiotic cessation. In contrast, patterns p3 and p4 included orthologs absent in tylosin mice either entirely or during development, or reestablished at the final time sampled, respectively. Patterns p5 and p6 corresponded to orthologs appearing after HFD introduction in all mice. In total, the substantial flux in early-life (tylosin>amoxicillin) metagenomic content was consistent with the 16S data. Through metagenomic sequencing and metabolite characterization, we detected a microbial pathway strongly influenced by PAT and dietary alterations, differing by antibiotic regimen, with partial functional recovery by gain of redundant genes.

PAT selects for antimicrobial resistance genes

Multiple classes of antibiotic resistance-associated genes were examined by mapping the metagenomic reads to the resistance gene database. Among genes related to macrolide resistance, four—acrA, acrB, ant3Ia and ant2Ia—were present at very low frequency (<10−7) among dams (Fig. 7a–d). However, all three mice in the tylosin group showed blooms of these genes to ∼10−4, as did one of the mice receiving amoxicillin. No controls showed a change in frequency of these genes. The same mouse receiving amoxicillin and the three tylosin-receiving mice all had blooms of ampC, a beta-lactamase gene (Fig. 7e). For 15 tetracycline genes (Fig. 7f) and for hundreds of other genes in the metagenome, there were no differences between mothers, controls and antibiotic-receiving mice, indicating a lack of selection for their resistances.