Gut Prevotella stercorea associates with protection against infection in rural African children

Domain: neuroscience
Published: 2025-12-12
Editorial outcome: minor_revision

Abstract
Understanding how the gut microbiome confers immune protection in early life remains a fundamental challenge, particularly in high-infection-burden, underrepresented populations. Here, we analyse longitudinal stool microbiome and illness data from 633 Gambian children aged 7–37 months enroled in a randomised, double-blind iron supplementation trial (NCT02941081). The primary endpoint was correction of iron deficiency anaemia, and the secondary endpoint was diarrhoea incidence and other adverse events; IHAT was non-inferior to ferrous sulphate for iron correction and showed fewer moderate-to-severe diarrhoea episodes. Using 16S rRNA sequencing at three timepoints (Days 1, 15, and 85), we find that children who remained infection-free harboured a consistently higher relative abundance of Prevotella stercorea, a keystone taxon of non-industrialised gut ecologies. In contrast, Escherichia coli and other opportunistic pathogens were enriched in children who developed infections. These taxonomic signatures were temporally stable and embedded within distinct, co-occurring microbial networks. Strikingly, higher P. stercorea abundance was inversely associated with both infection frequency and duration, with the greatest differences observed in children aged 1–2 years, potentially explaining their increased infectious risk. Our findings reveal a Prevotella-dominated enterotype associated with reduced infection risk, with implications for vaccine responsiveness, child survival, and microbiota-targeted interventions in global child health.

Introduction
The concept of enterotypes has provided a foundational framework for classifying gut microbiome configurations in human populations. Typically, these enterotypes are dominated by one of three bacterial genera; Bacteroides, Prevotella, or Ruminococcus, with Bacteroides and Prevotella often exhibiting mutual exclusion and competitive dynamics. In our earlier cluster analysis of the IHAT-Gut cohort, we identified two dominant taxa, Prevotella stercorea and Faecalibacterium prausnitzii, as key components of the gut microbiota of Gambian children1. While Prevotella predominates in many pre-industrial societies, Bacteroides is characteristically dominant in industrialised Western populations2,3. This geographic divergence reflects broader differences in diet, lifestyle and environmental microbial exposures.

Gut microbiome development in early childhood typically follows a well-characterised trajectory: an initial period of instability during infancy, followed by a gradual maturation toward an adult-like configuration by ~3 years of age4. However, much of this understanding derives from studies conducted in low-infection-burden, industrialised contexts. Consequently, there remains a critical knowledge gap regarding microbiome maturation and immune function in high-infection-burden regions such as sub-Saharan Africa. Recent studies in Nigeria, Malawi, Burkina Faso and The Gambia have begun to address this gap, consistently reporting Prevotella-dominant microbiomes as the regional norm1,5–7. Yet, few have examined how these microbial configurations influence infection susceptibility and health outcomes during early life.

While microbiome maturation is shaped by age, diet and environmental exposure, it is also increasingly linked to urbanisation, lifestyle transitions and long-term disease risk8,9. In settings where infectious diseases remain the leading cause of child mortality, understanding how gut microbial ecology relates to infection risk and immune development is both a scientific imperative and a public health priority.

In this follow-up analysis, we return to the IHAT-Gut dataset of 633 children aged 7–37 months from a randomised iron supplementation trial in rural Gambia10,11. We hypothesise that specific gut microbiota compositions confer protection against infection and predict future adverse events. Building on prior analyses describing a Prevotella-based trophic network1, we provide new insights into how this network may underpin immune resilience and modulate infection risk during a critical window of microbiome and immune system co-development. Here, we show that a Prevotella-dominated gut microbiome, with higher Prevotella stercorea and lower Escherichia coli, is associated with reduced infection frequency and duration, forms temporally stable co-occurring networks and has the strongest impact in 1–2-year-olds, revealing a microbiome configuration that may underpin immune resilience and guide global child health interventions.

Results
Sampling framework and cohort description
In our previous microbiome analysis of the IHAT-Gut study1, we examined 1546 stool samples collected from 633 children aged 7–37 months who were enroled in the IHAT-Gut double-blind, randomised trial (NCT02941081) in The Gambia. After quality control, 1389 samples were retained for analysis. Stool samples were collected at three timepoints: Day 1 (D1), Day 15 (D15) and Day 85 (D85) (Supplementary Fig. 1). That earlier study focused primarily on describing the gut microbiome composition and developmental trajectories and showed that iron supplementation had no confounding effect on microbial structure, allowing us to omit treatment arm from downstream analysis.

However, the relationship between microbiome composition and adverse events (AEs) recorded during the 113-day follow-up period was not explored. These AEs were predominantly infectious in nature (693 of 707 events; 98.02%; Supplementary Table 1), with diarrhoea (n=169), acute respiratory infections (n=383) and fever (468) being the most common.

In this follow-up study, we stratified children into two cohorts based on illness status: Ill (≥1 infectious AE; n=418) and Not-Ill (no recorded AEs; n=210) (Table 1; Supplementary Table 2). We then examined how gut microbiome composition relates to infection risk, through analyses of alpha and beta diversity and by identifying taxa that differ between Ill and Not-Ill children.

Participant demographics and gut microbiome stability across age groups and illness status (D1 vs D85)
Because our central hypothesis posits that microbiome composition predicts illness, while microbiome itself is strongly age-dependent, we first examined participant demographics and within-individual short-term microbiome stability. Demographic analyses revealed that ill children were slightly younger than not-ill children (mean age: 22.6 vs 24.2 months), with age group distributions differing between illness statuses (Mann–Whitney U test, p=0.0005) (Table 1; Supplementary Fig. 2; Supplementary Tables 3, 4). Gender distributions remained balanced across groups (48% female overall), ruling out sex-related confounding.

Within-individual Bray–Curtis similarity analysis (Supplementary Fig. 3) confirmed expected age-dependent maturation patterns: the youngest children (7–12 months) showed the greatest instability (similarity=0.460±0.185), intermediate-aged children (13–24 months) displayed moderate stability (0.528±0.182), while the oldest children (>24 months) exhibited the highest stability (0.609±0.173). These gradients reflect developmental consolidation of the gut microbiome over the study period.

Crucially, when stratified by age group, within-individual stability did not differ between Ill and Not-Ill children (FDR q=0.561–0.654; Supplementary Table 5). This indicates that illness status does not disrupt fundamental patterns of short-term microbiome maturation. Instead, the predictive signal of illness must be sought in between-individual differences in microbiome composition. Accordingly, all subsequent analyses incorporated age-adjusted or stratified models to ensure that demographic factors, especially age, were properly accounted for in microbiome–illness associations.

Iron treatment does not confound microbiome–illness associations
Given the confounding potential of illness, it was essential to evaluate the independent impact of iron supplementation on the gut microbiome and infection risk. To determine whether oral iron supplementation influenced gut microbiome structure or illness risk, we tested for associations between treatment arm (iron vs placebo) and (i) microbial taxonomic composition (via MaAsLin2), (ii) alpha diversity (Shannon, Richness, Fisher’s alpha, Simpson, Pielou), (iii) beta diversity (Bray–Curtis distances) and (iv) AE occurrence.

Iron treatment showed no significant overlap with illness-associated taxa after false discovery rate (FDR) correction. Alpha and beta diversity metrics also showed no consistent differences by treatment group, and AE incidence did not differ between iron and placebo arms (Supplementary Fig. 4). These findings replicate our earlier results¹ and support excluding treatment group as a covariate in downstream models focused on illness-related microbiome differences.

Alpha diversity differences suggest disrupted microbiome maturation in ill children
Age-adjusted generalised linear models (Benjamini–Hochberg FDR) revealed that at each timepoint, Ill children exhibited significantly lower Richness and Fisher’s α compared to Not-Ill peers, whereas Shannon, Simpson and Pielou’s evenness were generally non-significant (Table 2; Supplementary Fig. 5A; Supplementary Table 6).

Specifically, same-timepoint comparisons showed deficits in Ill children at D1 (Richness: F(1, 512)=6.05, p=0.016, η²p=0.012, 95% CI [−7.66, 1.00]; Fisher’s α: F(1, 512)=5.80, p=0.016, η²p=0.011, 95% CI [−1.19, 0.19]), D15 (Richness: F(1, 404)=7.49, p=0.006, η²p=0.018, 95% CI [−8.02, 1.58]; Fisher’s α: F(1, 404)=7.55, p=0.006, η²p=0.018, 95% CI [−1.25, 0.26]) and D85 (Richness: F(1, 448)=15.30, p=1.3×10⁻⁴, η²p=0.033, 95% CI [−10.57, −1.24]; Fisher’s α: F(1, 448)=14.83, p=1.3×10⁻⁴, η²p=0.032, 95% CI [−1.65, −0.16]) (Fig. 1A; Table 2; Supplementary Fig. 5B).

Using D85 Not-Ill samples as a reference for healthy microbiome maturation (minimising pre-study illness effects), early Ill samples at D1 and D15 exhibited even more pronounced deficits. At D1, Richness was significantly reduced in Ill versus D85 Not-Ill children (F(1, 473)=26.30, p=4.26×10⁻⁷, η²p=0.053, 95% CI [−12.70, −3.11]; n=477), as was Fisher’s α (F(1, 473)=25.95, p=5.07×10⁻⁷, η²p=0.052, 95% CI [−1.98, −0.46]; n=477). Similar reductions were observed at D15 for both Richness (F(1, 418)=28.35, p=1.66×10⁻⁷, η²p=0.064, 95% CI [−12.07, −2.72]; n=422) and Fisher’s α (F(1, 418)=28.38, p=1.63×10⁻⁷, η²p=0.064, 95% CI [−1.89, −0.41]; n=422), providing a robust baseline comparison for disrupted microbiome development (Fig. 1B; Table 2; Supplementary Fig. 5B; Supplementary Table 6).

Within-group temporal trajectories indicated that α-diversity did not significantly change over time within Ill or Not-Ill children (Ill: D1 vs D15 vs D85, all p≥0.061; Not-Ill: D1 vs D15 vs D85, all p≥0.201) (Table 2; Supplementary Fig. 5A; Supplementary Table 6). This persistence highlights that deficits in Ill children are stable across the study period, rather than reflecting transient fluctuations.

Age-stratified analyses further revealed that the 1–2 year age group was most affected. In this group, Ill children had significantly lower diversity indices at D15 and D85 compared to age-matched Not-Ill peers (D15: Richness, F(1, 178)=4.67, p=0.032, η²p=0.026, 95% CI [−15.92, −0.72]; Fisher’s α, F(1, 178)=4.87, p=0.032, η²p=0.027, 95% CI [−2.53, −0.14]; D85: Richness, F(1, 194)=4.41, p=0.037, η²p=0.022, 95% CI [−15.56, −0.49]; Fisher’s α, F(1, 194)=4.61, p=0.037, η²p=0.023, 95% CI [−2.48, −0.11]).

Early Ill samples (D1, D15) also showed markedly lower diversity compared to D85 Not-Ill (D1: Richness, F(1, 213)=13.33, p=3.28×10⁻⁴, η²p=0.059, 95% CI [−21.11, −6.31]; Fisher’s α, F(1, 213)=13.79, p=2.61×10⁻⁴, η²p=0.061, 95% CI [−3.39, −1.04]; D15: Richness, F(1, 180)=15.18, p=1.38×10⁻⁴, η²p=0.078, 95% CI [−21.96, −7.19]; Fisher’s α, F(1, 180)=16.12, p=8.72×10⁻⁵, η²p=0.082, 95% CI [−3.53, −1.20]) (Fig. 1A-B; Supplementary Table 7; Supplementary Fig. 5B). Children in the 7–12 month and >2 year groups showed less pronounced differences, indicating that microbiome disruption in Ill children is both persistent and developmentally window-specific.

The absence of significant differences in other alpha diversity metrics (Shannon, Simpson and Pielou’s evenness) suggests that while the number of taxa and the abundance-weighted richness (Fisher’s alpha) are reduced in Ill children, the overall distribution of abundances among the remaining taxa is relatively preserved. In other words, Ill children’s microbiomes show a loss of rare or less abundant taxa without major disruption to community evenness or dominance, highlighting that microbiome maturation is specifically impaired in terms of richness rather than global diversity structure.

Overall, these analyses demonstrate that Ill children exhibit sustained reductions in Richness and Fisher’s alpha from the earliest sampling point, with the 1–2 year group most strongly affected. This pattern is consistent with disrupted microbiome maturation in early life, highlighting both baseline deficits and age-dependent vulnerabilities.

Beta diversity reveals early and persistent differences in microbiome composition
Beta diversity analysis revealed significant and persistent differences in gut microbiome composition between children who developed infections (‘Ill’) and those who remained healthy (‘Not-Ill’), even after adjusting for age (Table 2; Fig. 2; Supplementary Table 8). PERMANOVA and dispersion models were FDR-adjusted using the Benjamini–Hochberg procedure. PERMANOVA demonstrated clear compositional separation between Ill and Not-Ill groups at all timepoints: Day 1 (D1): F(3, 512)=20.46, p=0.002, R² =0.107, 95% CI [0.087, 0.141]; Day 15 (D15): F(3, 404)=19.27, p=0.002, R² =0.125, 95% CI [0.101, 0.169]; and Day 85 (D85): F(3, 448)=13.03, p=0.002, R² =0.080, 95% CI [0.063, 0.114]. While R² values were modest, they nonetheless reflect biologically meaningful shifts in community structure. These findings demonstrate consistent and statistically robust differences in overall microbial composition between Ill and Not-Ill children across all sampling points.

The nature of these differences, however, shifted over time. At D1 and D15, the separation reflected true compositional changes, as within-group dispersion did not differ significantly between Ill and Not-Ill children (H(1)=0.08, p=0.851 at D1; H(1)=1.22, p=0.583 at D15). By contrast, at D85, a significant dispersion effect emerged (H(1)=18.52, p<0.001), indicating that infection status at this later stage was associated with increased inter-individual variability in the Ill group relative to the more uniform, reconstituted microbiomes of D85 Not-Ill children.

Longitudinal pairwise comparisons revealed distinct microbiome trajectories within each illness group (Supplementary Fig. 6; Supplementary Table 8). In Not-Ill children, significant compositional changes between D15 and D85 (p=0.012) and between D1 and D85 (p=0.002) were accompanied by decreased dispersion (p=0.015 and p=0.001, respectively), reflecting progressive microbiome maturation and stabilisation. Conversely, Ill children showed compositional shifts over time (D1 vs. D15: p=0.007; D1 vs. D85: p=0.014) without changes in dispersion (all p>0.85), indicating persistent inter-individual variability. Cross-group comparisons between early illness timepoints and D85 Not-Ill samples further emphasised these divergent trajectories, with significant compositional and dispersion differences (all p≤0.002).

Consistent with alpha diversity findings, these differences were most pronounced in the 1–2-year-old group, highlighting this developmental window as one of heightened microbiome vulnerability. Age-stratified PERMANOVA within the same timepoint comparisons confirmed this pattern: in 1–2-year-olds, Ill and Not-Ill children showed a trend toward divergence at D15 (F(1, 178)=2.32, p=0.084, R² =0.013, 95% CI [0.008, 0.030]) and significant separation at D85 (F(1, 194)=2.64, p=0.049, R² =0.013, 95% CI [0.008, 0.031]), whereas no significant separation was observed in the 7–12-month or >2-year groups at any timepoint (Fig. 2; Supplementary Table 9). Together, these results suggest that susceptibility to infection is driven more by differences in gut microbiome composition and reduced microbial richness than by overall evenness, and they pinpoint the second year of life as a critical period when the gut microbiome may play a pivotal role in shaping infection risk.

Microbial network analysis reveals antagonistic clusters centred on P. stercorea and E. coli
To elucidate ecological relationships within the gut microbiota, we conducted bacterial co-occurrence network analysis using the 50 most abundant taxa (≥0.2% relative abundance). Taxa clustered into ten, of which six formed two antagonistic and mutually exclusive modules: a health-associated network centred on Prevotella stercorea, and a dysbiosis-associated network anchored by Escherichia coli (Fig. 3A, B). The P. stercorea cluster comprised Prevotella copri, Faecalibacterium prausnitzii, Succinivibrio dextrinosolvens and other obligate anaerobes known for short-chain fatty acid production and immune modulation. In contrast, the E. coli cluster included Bacteroides, Bifidobacterium and several pathobionts such as Klebsiella pneumoniae and Streptococcus equinus. Correlation heatmaps revealed strong positive associations within each cluster (red), and strong negative correlations (blue) between the two clusters, consistent with competitive exclusion and antagonistic niche occupation (Fig. 3A).

To enhance interpretability, we generated a cluster relationship map (Fig. 3B), illustrating the negative bidirectional correlations between the P. stercorea and E. coli networks. The Ill cohort predominantly aligned with the E. coli cluster, whereas the Not-Ill cohort was enriched for P. stercorea and its co-associated taxa. These data suggest the existence of two competing microbial ecologies: one potentially protective and the other permissive of inflammation and pathogen expansion.

Prevotella stercorea and Escherichia coli differentiate Ill from Not-Ill cohorts
Significant differences in taxa were observed between the Ill and Not-Ill cohorts following age-adjusted ANOVA across the three timepoints. Among the ten most abundant taxa (>1% total reads), Escherichia coli and Prevotella stercorea consistently differed between groups (FDR-corrected p<0.05; Table 2; Supplementary Table 10, Fig. 4A, B). Specifically, E. coli abundance was significantly higher in the Ill cohort at all timepoints, while P. stercorea was significantly reduced. Both taxa exhibited parallel temporal trends with no significant interaction effects (interaction p=0.871 for E. coli, p=0.349 for P. stercorea), indicating stable group differences independent of sampling time.

These findings imply that susceptibility to infection may reflect intrinsic baseline compositional differences rather than dynamic microbiome shifts following illness onset. P. stercorea showed significant time (F(2, 512)=5.78, p=0.0115, η²p=0.005, 95% CI [0.001, 1]) and group (F(1, 512)=7.33, p=0.0228, η²p=0.005, 95% CI [0.001, 1]) effects, indicating sustained lower levels in the Ill cohort. This interpretation is further supported by independent MaAsLin2 analysis comparing Day 85 samples, which revealed that P. stercorea remained significantly depleted in Ill children at the final timepoint (coef= −0.026, 95% CI [−0.046, −0.005], q=0.025, Table 2 Comparison 9), reinforcing the persistence of group-level differences.

In contrast, E. coli demonstrated a significant group effect (F(1, 512)=7.03, p=0.0249, η²p=0.005, 95% CI [0.001, 1]) and a marginal time effect (F(2, 512)=3.52, p=0.0703), suggesting consistently elevated levels in the Ill group with modest temporal change.

Extending this analysis, the trajectories and Day 85 relative abundances of the 50 most abundant bacterial taxa across the three timepoints provide further insight into potentially protective microbes (Fig. 4B). Within the next 40 most abundant taxa, Eubacterium coprostanoligenes (F(1, 512)=2.32, p=0.044, η²p=0.005, 95% CI [0.000, 0.010]) was significantly enriched in the Not-Ill group. Notably, all Prevotella species consistently aligned with the Not-Ill group, reinforcing their potential protective role, whereas pathogenic taxa generally clustered with the Ill group, underscoring candidates implicated in microbiome-mediated susceptibility.

Other dominant taxa displayed varying temporal and group patterns. Bifidobacterium showed a marginal time effect (F(2, 512)=3.80, p=0.060) without group or interaction differences, suggesting possible age-related shifts independent of illness. Faecalibacterium prausnitzii exhibited a significant temporal change (F(2, 512)=4.72, p=0.0259, η²p=0.001, 95% CI [0, 1]) but no group-level effects. Prevotella copri, Bacteroides, Succinivibrio dextrinosolvens, Paraprevotella xylaniphila, Sutterella wadsworthensis and Streptococcus salivarius did not differ significantly between cohorts. Only P. copri (F(2, 512)=3.41, p=0.074) and S. salivarius (F(2, 512)=3.33, p=0.0758) exhibited marginal time effects, consistent with shared developmental dynamics (Supplementary Table 10).

Together, these results identify E. coli and P. stercorea as the most robust microbial markers of future infection. Their consistent divergence between Ill and Not-Ill children, supported by both longitudinal ANOVA and targeted MaAsLin2 comparisons, points to persistent compositional differences preceding clinical outcomes rather than illness-driven microbial change.

Baseline microbiome composition predicts future infection risk
Given the relative stability of microbial diversity and taxonomic composition at D85, and the reduced likelihood of residual confounding from recent pre-enrolment illnesses, D85 Not-Ill samples were designated as the optimal ‘healthy control’ group for all downstream analyses. To evaluate the predictive capacity of early microbiome features, D1 samples were stratified by time-to-illness onset: ≤35 days (‘Soon-Ill’, n=189), 36–70 days (‘Later-Ill’, n=100), and >70 days (‘Much-Later-Ill’, n=51) and compared to D85 Not-Ill controls (n=137).

PERMANOVA and dispersion models were FDR-adjusted using the Benjamini–Hochberg procedure. Age-adjusted PERMANOVA analyses (Fig. 5; Supplementary Table 8) revealed significant microbiome compositional differences across all illness-timing groups: Soon-Ill (F(3, 322)=11.33, p=0.001, R² =0.096, 95% CI [0.073, 0.144]); Later-Ill (F(3, 233)=8.18, p=0.001, R² =0.095, 95% CI [0.076, 0.149]); and Much-Later-Ill (F(3, 184)=6.25, p=0.001, R² =0.092, 95% CI [0.072, 0.154]), indicating modest but consistent shifts in baseline microbiome structure associated with future infection risk.

Community dispersion, assessed with Kruskal–Wallis tests, showed that the Soon-Ill group exhibited the largest increase in between-sample dispersion relative to D85 Not-Ill controls (H(1)=16.83, p=1.23×10⁻⁴), followed by smaller but significant increases in Much-Later-Ill (H(1)=9.26, p=0.004) and Later-Ill (H(1)=7.87, p=0.005). These differences indicate that the compositional separation observed in PERMANOVA reflects a combination of true location shifts (changes in group centroid) and increased heterogeneity (dispersion) among children who develop infection, particularly in the Soon-Ill group. This pattern suggests a disrupted or developmentally delayed microbial state that may predispose to infection.

MaAsLin2 analyses (Fig. 6A; Supplementary Table 11) identified several D1 taxa with predictive associations. Prevotella stercorea was significantly depleted in all illness groups: Soon-Ill (coef= –0.029, 95% CI [–0.050, –0.009], q=0.015), Later-Ill (coef= –0.040, 95% CI [–0.064, –0.016], q=0.003) and Much-Later-Ill (coef= –0.044, 95% CI [–0.073, –0.014], q=0.011), reinforcing its role as a core protective taxon. The pathogen Escherichia coli was significantly elevated in Soon-Ill (coef= 0.028, 95% CI [0.007, 0.049], q=0.026), while Bacteroides was increased in both Soon-Ill (coef= 0.027, 95% CI [0.000, 0.054], q=0.095) and Much-Later-Ill (coef= 0.044, 95% CI [0.005, 0.082], q=0.055). Group-specific enrichments included Bifidobacterium in Later-Ill (coef= 0.024, 95% CI [0.000, 0.050], q=0.099), Sutterella wadsworthensis in Much-Later-Ill (coef= 0.013, 95% CI [0.000, 0.027], q=0.047) and Streptococcus salivarius in Soon-Ill (coef= 0.012, 95% CI [0.000, 0.026], q=0.084).

At D15, Prevotella stercorea remained significantly depleted in Soon-Ill (coef= –0.039, 95% CI [−0.062, −0.017], q=0.002) and depleted in Much-Later-Ill (coef= –0.051, 95% CI [–0.096, –0.005], q=0.068), while Escherichia coli was again significantly enriched in Soon-Ill (coef= 0.043, 95% CI [0.014, 0.071], q=0.009). Given the short interval from D1, these results align with earlier observations. Nevertheless, the recurrence of these associations at D15 provides internal validation of the predictive signals first observed at D1 and underscores the consistency of microbial alterations linked to future illness risk.

Importantly, age-stratified analyses for both beta diversity (Fig. 5; Supplementary Table 9) and taxa (Fig. 6B; Supplementary Table 12) revealed that these associations were again concentrated in children aged 1–2 years. For beta diversity, PERMANOVA demonstrated significant compositional separation between Ill and Not-Ill groups across all illness-timing strata in this age group: Soon-Ill (F(1, 146)=2.66, p=0.024, R² =0.018, 95% CI [0.011, 0.042]); Later-Ill (F(1, 89)=3.29, p=0.004, R² =0.036, 95% CI [0.020, 0.080]); and Much-Later-Ill (F(1, 66)=4.20, p=0.004, R² =0.060, 95% CI [0.035, 0.119]). At the taxon level, consistent depletion of Prevotella stercorea (D1 Ill 1–2 yr: coef= −0.048, 95% CI [−0.076, −0.019], q=0.012; D15 Ill 1–2 yr: coef= −0.037, 95% CI [−0.068, −0.005], q=0.047; D15 Ill 7–12 mths: coef= −0.052, 95% CI [−0.089, −0.015], q=0.077) and Paraprevotella xylaniphila (D1 Ill 1–2 yr: coef= −0.019, 95% CI [−0.033, −0.005], q=0.036; D15 Ill 1–2 yr: coef= −0.021, 95% CI [−0.036, −0.007], q=0.023) was observed at D1 and D15, alongside enrichment of Escherichia coli (D15 Ill 1–2 yr: coef= 0.058, 95% CI [0.009, 0.107], q=0.047) and Bacteroides (D1 Ill 1–2 yr: coef= 0.044, 95% CI [0.007, 0.081], q=0.064; D15 Ill 1–2 yr: coef= 0.049, 95% CI [0.013, 0.085], q=0.030) and marked depletion of Succinivibrio dextrinosolvens (D15 Ill 1–2 yr: coef= −0.037, 95% CI [−0.055, −0.018], q=0.001). In contrast, children >2 years showed no significant associations, underscoring the heightened vulnerability of the 1–2 year age group.

Although effect sizes were modest, the consistency of directional changes across timepoints and illness groups strengthens the biological plausibility of these associations. Notably, the depletion of Prevotella stercorea emerged as the most robust and reproducible marker of susceptibility. Its predictive relevance at D1 and D15, highlighted in both MaAsLin2 (Fig. 6A, Supplementary Fig. 7) and ANOVA-based analyses (Fig. 4A), is supported by diversity metrics (Figs. 1, 2; Fig. 5) and visualised compositional shifts (Fig. 4B), all pointing to early microbial divergence in children who subsequently develop infection.

Collectively, these findings underscore the potential of early-life microbiome profiling to forecast infection risk weeks to months in advance. Perturbations in gut microbial composition, particularly involving Prevotella stercorea, may precede and even contribute to the emergence of pathogenic illness in susceptible children.

Residual microbial alterations persist after illness
To determine whether illness leaves lasting microbiome signatures, we examined D85 stool samples from children who had experienced illness at different intervals. The ‘Recent-Ill’ group (n=85) had recovered from an adverse event (AE) 10 days prior, while the ‘Early-Ill’ group (n=83) had been ill shortly after D1 but remained healthy for at least 50 days before D85. Compared to Not-Ill controls (n=137), Recent-Ill children exhibited significant microbiome compositional differences (PERMANOVA: F(3, 218)=7.27, p=0.001, R² =0.091, 95% CI [0.070, 0.149]; Kruskal–Wallis: H(1)=5.25, p=0.022), whereas the Early-Ill group showed weaker but still significant divergence (PERMANOVA: F(3, 216)=5.82, p=0.001, R² =0.075, 95% CI [0.065, 0.123]; Kruskal–Wallis: H(1)=18.52, p=3.36×10⁻⁵) (Supplementary Fig. 8; Supplementary Table 8). These findings indicate that while the gut microbiome gradually recovers following illness, residual compositional alterations can persist, especially shortly after recovery.

Consistent with this temporal recovery, Prevotella stercorea remained significantly depleted in the Recent-Ill group (coef= −0.039, 95% CI [–0.066, –0.012], q=0.016), whereas its abundance in Early-Ill children was statistically indistinguishable from that of Not-Ill controls (q=0.657) (Supplementary Table 11). This trajectory supports the hypothesis that P. stercorea is disrupted during illness but gradually restored over time.

In contrast, the pathogen Escherichia coli did not differ significantly between groups at either timepoint (q=0.136 for Recent-Ill; q=0.450 for Early-Ill), aligning with its expansion (in Soon-Ill) prior to illness onset. While overall community composition remained distinguishable in Early-Ill children, the absence of significant taxon-level differences point to a broader recovery process not driven by dominant taxa alone.

Prevotella stercorea abundance correlates with infectious burden and duration
Linear regression of Prevotella stercorea abundance at Day 85 (D85) revealed a significant inverse association with total infectious AEs (n=1215). At the group level, higher P. stercorea abundance predicted fewer total events (slope=–0.37, 95% CI [–0.55, –0.20], R² =0.90, p=0.004) and shorter cumulative duration (slope=–0.14, 95% CI [–0.25, –0.03], R² =0.24, p=0.014). At the individual level, associations were weaker but still significant for frequency (slope=–0.31, 95% CI [–0.57, –0.04], R² =0.01, p=0.025) and duration (slope=–0.07, 95% CI [–0.13, –0.02], R² =0.01, p=0.014) (Fig. 7; Supplementary Table 13).

Parallel analyses of diarrhoeal (n=169), ARI (n=383) and febrile (n=468) AEs showed consistent inverse trends, with group-level significance reflecting population-wide protection and individual-level significance indicating that children with higher P. stercorea abundance experienced fewer and shorter episodes. These results underscore the capacity of P. stercorea to modulate early-life infectious burden across multiple infection types, even at this secondary, sub-optimal timepoint and suggest potentially stronger effects in prospective studies specifically designed to capture microbiome–infection dynamics.

Age-dependent increase in Prevotella stercorea and peak illness susceptibility in toddlers
Prevotella stercorea abundance (Fig. 8A) increased significantly with age (7–12 months, 1–2 years, >2 years; one-way ANOVA F(2, 1373)=49.41, p=1.90×10⁻²¹, η² =0.067). Post-hoc Tukey HSD comparisons confirmed that abundance was significantly higher in 1–2-year-olds versus 7–12-month-olds (diff = 1.63, 95% CI [1.03, 2.23], p=6.94×10⁻¹⁰), in >2-year-olds versus 7–12-month-olds (diff= 2.53, 95% CI [1.93, 3.14], p<1×10⁻²⁰) and in >2-year-olds versus 1–2-year-olds (diff=0.90, 95% CI [0.48, 1.32], p=1.84×10⁻⁶).

Logistic regression examining the association between P. stercorea abundance and illness revealed no significant interaction terms between P. stercorea and age groups (log P × 1–2 years: OR=0.98, 95% CI [0.875, 1.097], p=0.732; log P × >2 years: OR=1.018, 95% CI [0.908, 1.14], p=0.757), indicating that the relationship between P. stercorea abundance and illness was consistent across ages.

Illness patterns were age-dependent (χ² =22.69, df = 2, p=1.19×10⁻⁵), peaking in 1–2-year-olds and lowest in children >2 years (Fig. 8B). This middle age group exhibited the most pronounced illness-associated differences in microbial diversity and taxa composition across all analyses.

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Remarks to the Authors.\nImpression: This study offers valuable insight into the complex interactions between microbiome and childhood illness in a\nlow- and middle-income country context, where repeated pathogen exposure, environmental contamination, malnutrition,\nand limited healthcare access can profoundly shape microbial communities. The findings underscore how microbiome\nstructures shaped in high infection settings that potentially influencing immune development, pathogen carriage, and\nresilience to disease. Importantly, such studies from LMICs help fill a critical evidence gap, as most microbiome research\nhas been conducted in high-income settings, which may not reflect the realities of populations facing greater infectious risk.\nBy capturing these unique ecological and epidemiological dynamics, the work provides an essential foundation for context-\nspecific interventions aimed at improving child health, reducing infection burden, and supporting microbiome resilience.\nHowever, there are number of issues to be addressed before its consideration for publication.\nMajor comments\n1. Most of the figures are blurred and of poor visual quality, making it difficult to read the annotated text. Figures should be\npresented in high-resolution PDF or PNG format, with maximum graphical quality and visually appealing design. In their\ncurrent form, the figures occupy excessive space while offering low clarity. The quality can be improved by reducing the\nfigure dimensions without compromising resolution. Additionally, remove grid lines from all figures and retain only the X and\nY axes for visualization.\n2. The statement in lines 103–105, “Age-adjusted alpha diversity analyses initially revealed no significant differences\nbetween Ill and Not-Ill children at the same sampling timepoints (D1, D15, or D85), indicating comparable microbial richness\nand evenness after controlling for developmental stage (Supp. Fig. 3A; Table 1 Comparison 7–9)”, is not presented in Supp.\nFig. 3A . The data presented in Supp. Fig. 3A do not show age-adjusted alpha diversity analyses between Ill and Not-Ill\nchildren at the same sampling timepoints (D1, D15, or D85); rather, they present pairwise comparisons of alpha diversity\nmetrics between Ill and Not-Ill groups. This analysis is correctly reported in Table 1 (Comparisons 7–9). The authors are\nadvised to either remove the reference to Supp. Fig. 3A from line 105 or replace it with the correct figure representing age-\nadjusted alpha diversity analyses between Ill and Not-Ill children at the same sampling timepoints.\n3. Furthermore, as the data presented in Supp. Fig. 3A is not age-adjusted alpha diversity comparisons between Ill and Not-\nIll children at the same timepoints (D1, D15, D85) but rather pairwise comparison and this needs to be corrected according\nto the data presented in the figure.\n4. The authors present age-adjusted results in this manuscript, which is appropriate, as age is a well-documented\ncovariate influencing gut microbiome dynamics in young children. However, age is not the only factor that can shape the gut\nmicrobiome in this population. Variables such as current breastfeeding status, socioeconomic conditions, previous history of\nillness, and contact with domestic animals may also have significant effects. Therefore, applying distance-based\nredundancy analysis (db-RDA) to identify all potential confounding variables influencing the gut microbiome in this study,\nand adjusting for them, accordingly?\n5. In lines 181–183, the authors state that “susceptibility to infection may reflect intrinsic baseline compositional differences\nrather than dynamic microbiome shifts following illness onset.” Have the authors the chance to examine objective laboratory\ndata on intestinal infections to determine factors driving microbiome shifts? Such data would be highly relevant, as intestinal\ninfections are known to play a key role in shaping the host microbiome, thereby influencing susceptibility or resistance to\nsubsequent infections. Given that these children live in low-resource settings with limited access to clean water and\nsanitation, they are likely to experience repeated intestinal infections. Recent evidence also suggests that certain intestinal\nprotozoan parasites can protect children in low-income settings from symptomatic diarrhea and substantially influence gut\nmicrobiome composition and they might confound the result of this analysis.\n6. The context in which the authors interpret the data focuses primarily on “microbiome immunity” from an evolutionary and\ninfection perspective. While the current discussion is engaging and easy to follow, it does not address whether the observed\ninfection-protective patterns of the Prevotella enterotype (such as P. stercorea) in this high-infection setting are reproducible\nfindings or unique to this study. The manuscript would be strengthened by interpreting these results in comparison with other\nstudies conducted in similar high-infection-incidence settings, to assess whether the observed patterns are consistent with\nprior literature or represent isolated observations.\nMinor comments\n1. In line 320, the title of Figure 4D states: “Error bars represent standard error of the mean where shown.” However, the data\nis presented as boxplot, and error bars are not standard practice in boxplot visualization. It would be more appropriate to\nrevise this to indicate that the plot displays “medians and interquartile ranges.”\n2. At Line 337 italicize the species name “Escherichia coli”\n3. Supplementary Table 1 line 87 and Supp. Table 5 at line 89 use consistency of referencing of figures.\n4. In Suppl. Fig. 6. move the illness status figure legend in the lower part of the figure and I would rather use red color plate\nfor illness and blue for not ill. Some taxa names are not readable so use high quality figures to improve visualizations.\n5. The “CopyEdit” at the method part decreased the readability of the manuscript. It can be available for the public with\nadditional R code on GitHub or other online platforms.", "reviewer_2": "This a very rich and complex dataset, coupled with detailed host metadata from a unique region. It is a great opportunity to\nexplore relationships between the child gut microbiome and health. The robust relationship between P. stercorea\nabundance and infection risk is novel and clear. The narrative and clarrity of the manuscript however can be much improved\nby reviewing the choice of statistics vs. graphical representations used throughout and building more concise and\nappropriately annotated figure panels. Specific comments below:\nDemographics:\nThe authors did not describe any demographic differences between the ill and not-ill children. This would be an important\nfirst step to first determine whether any host confounders may have influenced both the microbiome and infection risk.\nSpecifically, the methods describe that participants were between 7 and 37 months of life. With this, the changes one would\nexpect in the gut microbiome on study day 1 and 85 would be much more dramatic for a 7 month infant compared to a 37\nmonth (3.5 year old) child. The authors should describe the spread of ages among their samples between the ill and not ill\nchildren. I would also suggest that authors compute similarity scores between matched samples to illustrate the extent\nwithin-individual variation across age groups.\nFigure 1: Alpha diversity and 2-way ANOVA: It would be more appropriate to employ pairwise comparisons for each time\npoint separately. Given that at each time point, the same individuals are included, they don’t meet the assumption of\nindependent observations – especially since the gut microbiome is expected to be highly similar within one individual over\ntime. Linear mixed effect models can take repeat measures into account, and would be another suitable alternative.\nAlternatively, the authors can focus their analysis on the D1 time point, representing the gut microbiome of the child at\nbaseline in relation future infections.\nBeta diversity: The effect of age is difficult to discern in the ordinations presented. At first glance it appears that most of the\n2+ year samples cluster together, and are overall less variable compared to some of the earlier time points. Consider\nstratifying the ordinations using the age bins used for analyses later in the manuscript.\nTable 1: The authors have compared ill to not ill children at D1, D15, and D85 and only found differences at the 85 day time\npoint, and then proceeded to compare D1 and D15 ill to D85 not ill children. The choice to use the D85 not ill as a reference\ngroup only appears later in the MS based on presumed reduction in possible confounding from prior illness. Given that\nD1/D15 microbiomes are likely to be highly similar, this analysis in some ways compares the same microbiomes twice. The\nauthors later write that finding the P. stercorea relationship at D1 and D15 provides more confidence in the findings (in\nessence it is a repeated measure). This is fine, but should be clearly described as the main conclusion to be drawn.\nAlso, comparisons between D1/D15 not ill and D85 not ill are not shown. It would be thorough to make these comparisons\nas well to demonstrate that the taxonomic differences between D1/15 ill and D85 not ill children were specific to the ill\nchildren.\nFig. 2: consider a log scale for at least the E. coli boxplots, it is otherwise difficult to discern any trends. Also, the choice to\nrepresent the data as pairwise comparisons between D1, D15, and D85 samples while showing the overall results of an\nANOVA is hard to interpret. Did the authors compare groups within each time point separately? This would be more\nappropriate for the graphical display selected.\nTable 2: Were only the 10-most abundant taxa included in differential abundance analysis? I would suggest this table be\nmoved to the supplement, and replaced by a figure panel illustrating taxa that differ across age groups or time.\nFig. 3: This figure would be better suited ahead of Fig. 2, so the narrative first describes the overall taxonomic structure of the\nmicrobiome and then focuses on the taxa that discriminated between ill and not-ill children. For the heatmap, the figure\nlegend describes 10 clusters that associate into 6 main groups. However, the heatmap itself only visualizes 3 groupings: P.\nstercorea, E. coli, and the “Larger group”. It is not clear what is meant by the “Larger group”. The authors should modify this\nfigure so the illustration matches the legend and results described in the text. Also, what is meant by the blue rectangle in the\nmiddle of Fig. 3b?\nFig. 4: The analyses on baseline microbiome predicting future illness are the most interesting! Of note, the soon, later, and\nmuch later ill groupings make sense, but I would suggest incorporating these into the figure legends for easier interpretability\nof the data.\nThe figure labels for ordinations vs. centroid distance boxplots should be labelled separately. The beta diversity plots\ncomparing not ill and soon-ill children are interesting. It appears that many more of the soon-ill children belong to the\nyounger children. Is there some effect modification of this relationship by age group? If you stratify the analyses by age,\nwould you still find the same trend in all groups?\nAgain, the authors have chosen ANOVA to compare centroid distances (e.g. Fig 4 A boxplots). This would be suitable for\nnormally distributed data. The authors are also displaying medians. I suggest performing a non-parametric test that is more\nsuitable for these kinds of data distributions, unless the authors have performed the necessary tests for normality.\nFig. 4 E could be moved to the supplement to simplify the figure, or at minimum should be labelled E for early and F for\nrecent ill. Similarily, the statistican comparisons tables throughout the MS (e.g. Table 3) could be moved to the supplement.\nFig. 5: Could the authors include ill vs. not ill as labels for the P. stercorea abundances in panel D? What remains unclear is\nhow the relationship between P.setrcora and illness prediction might change according to age."}, "author_response": "Response\nWe thank the reviewer for this feedback. All figures have been updated to high-resolution PDF formats, with\noptimized dimensions to improve clarity without compromising resolution. Grid lines have been removed, retaining\nonly the X and Y axes for visualization, and overall figure design has been refined for improved readability and visual\nappeal.\nResponse\nWe thank the reviewer for pointing this out. The reference to Supp. Fig. 3A has been removed and replaced with the\nreference to Fig. 1A, and now references Table 1. The Alpha diversity comparisons are now presented using age-\nadjusted generalized linear models at each timepoint (D1, D15, D85), ensuring proper control for developmental\nstage and within-individual dependencies. Ill children at each timepoint were additionally compared to D85 Not-Ill\nsamples as a baseline for healthy microbiome maturation.\nResponse\nWe thank the reviewer for this observation. All alpha diversity comparisons were in fact age-adjusted; however, due\nto the previous figure’s resolution, it may have been unclear that the comprehensive Supp. Fig. 3A displayed\npairwise comparisons in addition to within-timepoint comparisons. We have now improved the figure resolution,\nensuring that age-adjusted alpha diversity comparisons between Ill and Not-Ill children at each timepoint (D1, D15,\nD85) are clearly visible. These analyses are fully reflected in Figure 1 and Table 1, accurately representing age-\nadjusted differences at the same sampling timepoints.\nResponse\nWe thank the reviewer for this important suggestion. Unfortunately, individual-level data on variables such as\ncurrent breastfeeding status, detailed dietary intake, socioeconomic conditions, prior illness history, or contact with\ndomestic animals were not collected in this study, and we have acknowledged these limitations in the manuscript.\nWhile our age-adjusted analyses account for one of the most important determinants of early-life microbiome\ncomposition, residual confounding from unmeasured factors cannot be entirely excluded.\nSeveral measures were implemented to minimize potential confounding: children with severe malnutrition were\nexcluded, antibiotic-exposed stool samples were removed, and the trial was conducted in a relatively homogeneous\nrural population, where prior work has documented substantial uniformity in diet and socioeconomic conditions.\nThese safeguards, together with the consistent patterns observed across analyses, support the robustness of our\nfindings and reinforce the ecological and immunological role of Prevotella stercorea–dominated communities in\npromoting infection resilience during early life.\nWe have explicitly discussed these caveats in the Discussion and Conclusions, noting that unmeasured factors such as\nsubclinical infections, environmental exposures, or contact with domestic animals may contribute to microbiome\nvariation, and that validation across diverse geographic and ecological settings will be important to strengthen\ngeneralizability.\nResponse\nWe thank the reviewer for this important point. Unfortunately, objective laboratory data on prior intestinal\ninfections or protozoan parasite load were not available for these children, and we acknowledge this limitation in the\nmanuscript. However, as noted in the Methods, very few children harbored helminths (or protozoan parasites) due\nto a national anti-helminthic program in The Gambia, which provides metronidazole every six months. While we\ncannot fully account for subclinical or unmeasured intestinal infections, our analyses capture baseline microbiome\ndifferences and their association with subsequent infection susceptibility, and we have highlighted these caveats in\nthe Discussion.\nResponse\nWe thank the reviewer for this suggestion. We have now expanded the Discussion to place our findings in a broader\ncontext with this addition. “Evidence from other global cohorts indicates that our observations are not unique to The\nGambia. Earlier studies in Nigeria and Burkina Faso reported genus-level enrichment of Prevotella in rural\ncommunities compared with urban or European populations, although species-level resolution was limited. More\nrecent shotgun metagenomic surveys have consistently detected P. stercorea in non-Western settings, including\nLatin America, South Asia, and sub-Saharan Africa. Region-specific studies in Brazil, the Peruvian Amazon, and rural\nIndia have also identified P. stercorea within Prevotella-rich enterotype clusters, frequently co-occurring with P. copri\nin the context of high-fiber, subsistence diets. Although P. stercorea is rarely the dominant species, its recurrent\ndetection across rural, high-infection-incidence populations suggests that its association with infection protection in\nour cohort reflects a broader ecological pattern rather than an isolated finding. Importantly, whereas prior work has\nlargely interpreted P. stercorea prevalence in dietary terms, our study is the first to examine its role primarily from\nan infection-centered perspective”.\nResponse\nWe thank the reviewer for this observation. The figure legend for Figure 4D has been updated to indicate that the\nboxplots display medians and interquartile ranges, replacing the previous reference to standard error of the mean.\nResponse\nWe have italicized the species name Escherichia coli at Line 337, in accordance with proper taxonomic formatting.\nResponse\nWe have corrected the figure referencing in Supplementary Table 1 (line 87) and Supplementary Table 5 (line 89) for\nconsistency. All supplementary figure references now use the format “Supp.”\nResponse\nSupplementary Figure 6 has been updated and is now presented as Figure 3B. The illness status legend has been\nrepositioned to the lower part of the figure, and colors have been revised to a red palette for Ill and blue for Not-Ill\nsamples. Figure resolution has also been enhanced to ensure all taxa names are clearly readable. We thank the\nreviewer for this helpful suggestion.\nResponse\nWe thank the reviewer for this comment. The “CopyEdit” section in the Methods, which reduced readability, has\nbeen removed from the code formula. To enhance transparency and reproducibility, all associated R code and\nadditional resources are now publicly available on GitHub: https://github.com/ofordile-star/IHAT_Analysis.\nResponse\nWe thank the reviewer for this important comment. We have now summarized participant demographics, sample\ndistributions, and illness status using 1,389 stool samples from 633 children across three timepoints (Supp. Tables 8–\n9). The cohort spanned 7–37 months, categorized into 7–12 months (n = 88), 13–24 months (n = 282), and 25–37\nmonths (n = 263). Female representation was consistent across groups (44–50%; Supp. Table 9). As anticipated, Ill\nchildren were slightly younger than Not-Ill children (mean age: 22.6 vs. 24.2 months), reflecting the strong age-\ndependence of microbiome maturation.\nTo directly address this potential confounder, we conducted a within-individual Bray–Curtis similarity analysis (D1 vs.\nD85), which:\n1. Stratified children into age groups to capture developmental differences;\n2. Used paired within-individual comparisons to control for baseline composition; and\n3. Found no significant differences in microbiome stability between Ill and Not-Ill children within any age group\n(all FDR q > 0.5).\nThese findings confirm the reviewer’s expectation that younger children experience more pronounced microbiome\nshifts but importantly demonstrate that illness status does not alter short-term microbiome stability after\naccounting for age. We have incorporated this analysis into the Results under the heading: “Participant\nDemographics and Gut Microbiome Stability Across Age Groups and Illness Status (D1 vs D85)”.\nResponse\nWe appreciate the reviewer’s concern regarding repeated measures and the assumption of independent\nobservations. To address this, all alpha diversity comparisons were conducted using age-adjusted generalized linear\nmodels (GLMs) for same-timepoint comparisons, applied separately at D1, D15, and D85, ensuring that within-\nindividual dependencies across time did not confound the results. For cross-timepoint comparisons, we employed\nlinear mixed-effects models (LMMs) including Subject ID as a random effect, which appropriately account for\nrepeated measures while evaluating differences between groups across time. Additionally, comparisons of Ill\nchildren at D1 and D15 versus D85 Not-Ill samples provide a healthy reference for microbiome maturation,\nminimizing pre-study illness effects. This dual approach: GLMs for same-timepoint analyses and mixed-effects\nmodels for cross-timepoint comparisons, captures both baseline microbiome features at D1 and longitudinal\npatterns, robustly addressing repeated-measures dependencies and allowing D1 analyses to specifically reflect the\ngut microbiome in relation to subsequent infections.\nResponse\nWe appreciate the reviewer’s comment regarding age effects in beta diversity. In the revised analyses, we stratified\nPCoA ordinations by the three age groups used throughout the manuscript (7–12 months, 13–24 months, and 25–\n37 months). This revealed that children in the middle age group (13–24 months) showed the most pronounced\ndifferences associated with illness status, with a significant PERMANOVA effect, whereas the youngest (7–\n12 months) and oldest (>2 years) groups mostly did not show significant illness-related clustering. These stratified\nordinations demonstrate that beta diversity differences are most apparent in the middle age group.\nResponse:\nWe have clarified in the manuscript that using D85 Not-Ill children as the reference minimizes potential confounding\nfrom pre-enrolment illness, and this rationale is now introduced at the first relevant analysis (Alpha diversity).\nAdditionally, we have refined the text regarding Prevotella stercorea, specifying that the D15 findings largely reflect\nthe same microbial patterns observed at D1 due to the short interval between sampling, rather than serving as\nindependent validation. This framing ensures that the main conclusion, that early-life microbial differences are\nassociated with future illness risk, is presented clearly and accurately.\nResponse\nComparisons between D1/D15 Not-Ill and D85 Not-Ill were indeed performed and are presented in Table 1\n(Comparisons 2–3). We appreciate that, given the number of comparisons required to fully interrogate this complex\ndataset, it may be difficult to discern all the details. In response to the reviewer’s request for completeness, we have\nnow also included the comparisons of D1/D15 Not-Ill versus D85 Ill (Table 1, Comparisons 12–13), which we had\npreviously considered unnecessary. We thank the reviewer for highlighting the importance of consistency and rigor,\nwhich has improved the clarity of our presentation.\nResponse\nWe thank the reviewer sincerely for this valuable, experience-based advice. A log scale has now been applied, which\nsuccessfully improved separation between timepoints. Pairwise comparisons of Ill versus Not-Ill at D1, D15, and D85\nwere conducted using MaAsLin2; however, only the D85 comparisons remained significant after FDR correction and\nage adjustment (Table 1, Comparisons 7–9). Visually, the clear separation between groups at all timepoints for both\nE. coli and P. stercorea suggests that the lack of statistical significance at earlier timepoints was likely due to limited\npower in these pairwise models. In contrast, ANOVA evaluates temporal and group effects jointly within a single\nframework, thereby leveraging all available data and offering greater statistical power to detect overall differences.\nWe nonetheless employed MaAsLin2 for the pairwise analyses, as it is widely used in microbiome research and\npermits covariate adjustment, ensuring comparability with existing literature. We therefore retained both the\nANOVA and MaAsLin2 results, as they provide complementary perspectives on the temporal and group-level\ndynamics of these taxa.\nResponse\nWe thank the reviewer for this helpful suggestion. While Table 2 originally included only the 10 most abundant taxa\n(>1%), we have now extended the analysis to the 50 most abundant taxa in total. Table 2 has been moved to the\nSupplement, and the figure panel (Fig. 3B) illustrating the mean ± SE relative abundances of these taxa across\ntimepoints and illness status has been incorporated into the main manuscript. This expanded analysis highlighted\none additional taxon, Eubacterium coprostanoligenes, which was significantly enriched in Not-Ill children.\nImportantly, all Prevotella species consistently aligned with the Not-Ill group, while pathogenic taxa clustered with\nthe Ill group, further reinforcing the patterns of microbiome-mediated resilience observed in the main analysis.\nResponse\nWe thank the reviewer for this careful reading. In response, we have removed the “Larger Group” box and the\ncentral blue rectangle from the schematic diagram (previously Fig. 3B). The figure has also been moved ahead of the\nprevious Fig. 2 and is now presented as Fig. 2, aligning with the narrative that first describes the overall taxonomic\nstructure of the microbiome and then focuses on taxa discriminating between Ill and Not-Ill children. The heatmap\nnow clearly highlights the two main antagonistic networks: the Prevotella stercorea group and the Escherichia coli\ngroup, consistent with the six main clusters described in the text. Importantly, while the “Larger Group” has been\nremoved for clarity, the six clusters forming the antagonistic networks remain unchanged, ensuring the figure,\nlegend, and results are fully aligned.\nResponse\nWe thank the reviewer for this thoughtful feedback. In response, the color-coded diagram indicating Soon-, Later-,\nand Much-Later-Ill groups has been moved to the figure legend for improved interpretability. The PCoA and centroid\ndistance boxplot labels have been separated as suggested. Consistent with our concomitant age-stratified sub-\nanalyses for beta diversity, we have also stratified the beta diversity prediction plots by age group, which revealed\nparticularly informative patterns in the middle age group. We appreciate the reviewer’s insightful suggestions, which\nhave strengthened both the clarity and interpretability of our findings.\nResponse\nWe thank the reviewer for this suggestion. In response, we have replaced the ANOVA with the Kruskal–Wallis test\nfor the centroid distance comparisons, which is more appropriate for the observed non-normal data distributions.\nImportantly, this change does not alter the conclusions drawn from these analyses.\nResponse\nWe thank the reviewer for these suggestions. In response, Fig. 4E has been moved to the Supplement, with panels\nappropriately labelled for clarity, and the specified statistical comparison tables (e.g., Table 3) have also been\nrelocated to the Supplement. Key tables such as Table 1 have been retained in the main manuscript due to their\ncentral importance.\nResponse\nWe thank the reviewer for this suggestion. The now Panel B of Fig. 5 now includes explicit “Ill” vs. “Not-Ill” labels for\nPrevotella stercorea abundances, improving clarity. Regarding age-dependent effects, we performed logistic\nregression testing for an interaction between P. stercorea abundance and age group in predicting illness. This\ninteraction was non-significant (Interaction P = 0.757), indicating that the relationship between P. stercorea and\nillness prediction is consistent across age groups. These updates have been incorporated into the revised figure and\ncorresponding figure legend. Furthermore, in the revised manuscript, we have newly stratified all analyses by age\ngroup, which directly clarifies how the relationship between P. stercorea abundance and illness prediction varies\nacross ages. This age-stratified approach provides deeper insight into the protective role of P. stercorea and a clearer\ninterpretation of differences in illness burden."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Minor comment: Please italicized the gene and species names in the manuscript title “Prevotella stecorea-Mediated Protection Against Infection in Rural African Children: Evidence for Microbiome Immunity.”\nAll my comments are addressed particularly the improvements to figure quality, correction of figure references, and clarification of the age-adjusted alpha diversity analyses, have significantly improved the clarity and rigor of the manuscript. I find that all my concerns have been adequately addressed, and I have no further major comments.", "reviewer_2": "Notes:\nThe authors have made many changes to the manuscript and have carefully utilized the reviewer comments. I focussed on reviewing each figure with the main narrative of the study in mind, and provided comments for where I feel a re-working of the data presentation would provide more clear results and be more consistent and clear to the reader. As it stands it the main messages of the paper are lost amidst the many visuals presented, which in some cases still include texts too small to read.\nGeneral note:\n-Gender refers to biological sex in this study, this all instances of “gender” should be changed to “sex”.\nParticipant demographics and gut microbiome stability section:\nIt is great that the authors have added this section, and by closely looking at the age groups, have uncovered that most of the illness burden occurs at 1-2 years of age, at the time when the microbiome is most different between ill and not ill children.\n-Since this section appears first, the tables and figures referenced within should be “Figure 1, Supl. Figure 1, etc…. I see that the authors added the figures in sequence to existing figures in the MS but this would be the clear way to maintain flow of these data.\n-Fig1A/B: For a concise biological narrative, these data can be expressed much more succintly. For example, you can set your x-axis to timepoint and and y axis to richness, and your colour aesthetic to illness status. This would produce a concise condensed plot that would take a small fraction of the space. Similarly for panel B.\nI think the story that you are trying to tell with Fig. 1B is that 1. Alpha diversity differs between ill and not ill children mostly for 1-2 year olds but not for younger or older children. 2. That the difference between ill and not-ill children appears more pronounced over study time. By condensing these data (either as described above) or by using time point as a continuous variable and fitting a smooth line to visualize the overall trajectory of these children over time. Either of these options may produce a concise figure that makes it easier for the reader to draw conclusions and tease apart where the differences vs. similarities lie.\n-Fig. 1C: Aesthetically, all the red dots mostly bury the blue dots. This is perhaps because the “ill” children all appear before the “not ill” children in the data matrix, as these tools generally layer data points in the order that they appear in the matrix. However, here it means that your “Not Ill” group is barely visible. This could be addressed either by randomizing the order in which ill vs. not ill children are ranked in the matrix, or by using different aesthetics (e.g. hollow shapes with the fill set to partial transparency).\n-Kruskal wallis text seems to contain some extra characters.\n-Fig 3A: Why did the authors opt to present count data for P. stercorea and Log Abundance for E. coli? Counts are a fundamentally different metric compared to relative abundance. For 3B, based on the x axis, it cannot be relative abundance (as indicated in the figure legend) in the plot, which have maximum values of either 100% or 1.0. It seems like the raw counts were used, which for compositional data is subject to differences in sequencing depth.\n-Fig. 4: What does the text “PERMANOVA p = 0.001” on top of each plot indicate, since additional p values are given within each facet?\nAlso, the figure legend makes no mention of the “distance to centroid” plots,\n-Fig. 5A: Why do the number of data points change from one correlation plot to the next if all are displaying D85 data among the same participants? Also, the text is tiny and hard to read.\n-Fig 5B: to match the data presented, you should clearly indicate at which age groups P. stercorea differed. The ANOVA results given in the figure title don’t help the reader interpret the differences in the data displayed in the figure. The text is also barely legible.\n-Supl. Fig 1: The various other alpha diversity measures (Pielou, Simpson, Shannon) are not discussed in the text. What does the addition of all these plot add to the narrative of the manuscript if not discussed? The differences vs. similarities are not evident since no further attention is drawn to the significant findings vs. those where there are no differences.\nNote on cohort characteristics.\nFig. 5C sets the stage to help interpret many of the analyses to come (although the axes are barely readable). It clearly shows that infectious burden peaks between 1-2 years of age and makes it clear what sample sizes are going into the analyses. This figure, alone or in combination with a demographics table similar to Supl. Table 9 introduces the cohort typically leads the results section in human studies. I would suggest that, since most of the analyses presented are stratified by age bin, that this Table would combine elements of Supl. Table 8-10 and summarize, as the strata set to age bin + illness status, the variables numbers of children, biological sex, geographical region, adverse events, and age (months here is easier to interpret compared to days). The authors indicate that no further information was collected (e.g. breast feeding), but can note in the methods which demographic variables were collected and analyzed."}, "author_response": "We thank the reviewers for their thoughtful, constructive, and insightful comments on our manuscript, “Gut\nPrevotella stercorea associates with protection against infection in rural African children.” We have revised the\nmanuscript accordingly and believe these changes have substantially improved its clarity, rigor, and overall quality.\nBelow, we provide a detailed point-by-point response to each reviewer’s comments. All modifications are reflected\nin the revised version of the manuscript.\nReviewer #1 (Remarks to the Author)\nComment:\nMinor comment: Please italicize the gene and species names in the manuscript title “Prevotella stecorea-Mediated\nProtection Against Infection in Rural African Children: Evidence for Microbiome Immunity.”\nResponse:\nWe thank the reviewer for identifying this. We have italicized the species name in the title and corrected the\nmisspelling to Prevotella stercorea.\nComment:\nAll my comments are addressed, particularly the improvements to figure quality, correction of figure references, and\nclarification of the age-adjusted alpha diversity analyses, which have significantly improved the clarity and rigor of\nthe manuscript. I find that all my concerns have been adequately addressed, and I have no further major comments.\nResponse:\nWe are deeply grateful for your constructive feedback, which has helped enhance both the clarity and scientific\nstrength of the manuscript. Your insights have contributed meaningfully to improving the quality of this work, and by\nextension, to advancing microbiome science and child health research.\nReviewer #2 (Remarks to the Author)\nGeneral Note:\n“Gender” refers to biological sex in this study. All instances of “gender” should be changed to “sex.”\nResponse:\nWe agree completely and have replaced all instances of “gender” with “sex” throughout the manuscript.\nParticipant Demographics and Gut Microbiome Stability Section\nComment:\nIt is great that the authors have added this section, and by closely looking at the age groups, have uncovered that\nmost of the illness burden occurs at 1–2 years of age, when the microbiome is most different between Ill and Not-Ill\nchildren.\nSince this section appears first, the tables and figures referenced within should be “Figure 1,” “Supplementary Figure\n1,” etc. I see that the authors added the figures in sequence to existing figures in the manuscript, but this would be\nthe clearer way to maintain flow of these data.\nWe thank the reviewer for highlighting this important point regarding clarity and flow. All figures and tables have\nnow been renumbered sequentially from the start of the manuscript, ensuring that those referenced in the\nParticipant Demographics and Gut Microbiome Stability section appear first (e.g., “Figure 1,” “Supplementary Figure\n1,” etc.), thereby improving narrative coherence and readability.\nFigure 1A/B\nComment:\nFor a concise biological narrative, these data can be expressed more succinctly—for example, setting the x-axis to\ntimepoint, y-axis to richness, and color aesthetic to illness status. Similarly for panel B. This would make the figures\nmore compact and informative.\nResponse:\nWe thank the reviewer for this valuable suggestion. Figures 1A and 1B have been redone in this more compact,\nvisually intuitive format, providing a clearer depiction of patterns across timepoints and illness status.\nFigure 1C\nComment:\nAll the red dots mostly bury the blue dots, possibly because “Ill” children appear first in the data matrix.\nRandomizing the order or using hollow and partially transparent shapes could help. Also, the Kruskal–Wallis text\nseems to contain some extra characters.\nResponse:\nWe appreciate the reviewer’s close observation. We have randomized the plotting order and used hollow, semi-\ntransparent points, which substantially improve visibility of both groups. The extra character in the Kruskal–Wallis\ntext has been removed.\nFigure 3A/B\nComment:\nWhy were count data used for P. stercorea and log abundance for E. coli? These are fundamentally different metrics.\nThe figure axis suggests that raw counts rather than relative abundance were used, which could be influenced by\nsequencing depth.\nResponse:\nWe thank the reviewer for identifying this inconsistency. Both P. stercorea and E. coli plots now use relative\nabundance, with y-axes appropriately labeled. While the overall trajectories remained consistent, this change\nensures methodological accuracy and comparability.\nFigure 4\nComment:\nWhat does “PERMANOVA p = 0.001” on top of each plot indicate, given that additional p-values are provided within\neach facet? The legend also does not mention the “distance to centroid” plots.\nWe thank the reviewer for noticing this redundancy. A single “Age-adjusted p” now appears above the relevant beta-\ndiversity plots. We have also updated the figure legend to include statistics for the “distance to centroid” plots.\nFigure 5A\nComment:\nWhy do the number of data points change across correlation plots if all display D85 data among the same participants?\nThe text is also too small to read.\nResponse:\nWe appreciate this observation. The number of data points differs because each plot reflects distinct infectious AE\ntypes. For example, no child had more than four diarrhoeal episodes or more than ten cumulative diarrhoeal days,\nhence the differing x-axis ranges. We have enlarged the font size to improve readability.\nFigure 5B\nComment:\nTo match the data presented, please indicate the specific age groups in which P. stercorea differed. The ANOVA\nresults alone do not allow readers to interpret group differences. The text is also difficult to read.\nResponse:\nWe thank the reviewer for this valuable suggestion. We have now clarified in the figure and legend that post-hoc\nTukey HSD analyses revealed significantly higher P. stercorea abundance in older compared to younger age groups,\nindicating an age-related increase in this taxon and highlighting the relative differences between the groups. Font\nsizes in the plots have also been increased for improved readability.\nSupplementary Figure 1\nComment:\nThe additional alpha-diversity metrics (Pielou, Simpson, Shannon) are not discussed in the text. If included, their\nsignificance should be interpreted to add to the narrative.\nResponse:\nWe thank the reviewer for this thoughtful comment. We have added the following paragraph to the alpha-diversity\nresults section to contextualize these findings:\n“The absence of significant differences in other alpha-diversity metrics (Shannon, Simpson, and Pielou’s evenness)\nsuggests that while the number of taxa and abundance-weighted richness (Fisher’s alpha) are reduced in Ill children,\nthe overall distribution of abundances among remaining taxa is relatively preserved. Thus, Ill children’s microbiomes\nshow a loss of rare taxa without major disruption to evenness, indicating that microbiome maturation is specifically\nimpaired in richness rather than overall diversity structure.”\nNote on Cohort Characteristics\nComment:\nFigure 5C helps interpret many analyses and shows that infectious burden peaks between 1–2 years. This figure, or a\n4\nsimilar demographics table, should introduce the cohort at the start of the results. The table should combine\ndemographic details (sex, age, illness status, region, adverse events, etc.). The methods should also specify which\ndemographic variables were collected.\nResponse:\nWe thank the reviewer for this valuable guidance. We have now introduced “Table 1 | Baseline Characteristics of\nChildren by Sex, Age Group, Illness Status, and Geographic Location”, summarizing key demographic variables. The\nMethods section now includes a subsection titled “Sex and Demographic Considerations”, describing the\ndemographic variables collected and analyzed."}]

Decision letter