Influence of Gut Microbiota on Immune Responses and Protection in Volunteers Receiving the Live Attenuated Oral ETEC Vaccine ACE257 followed by Virulent ETEC H10407 Challenge

Influence of Gut Microbiota on Immune Responses and Protection in Volunteers Receiving the Live Attenuated Oral ETEC Vaccine ACE257 followed by Virulent ETEC H10407 Challenge | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Gut Microbiota on Immune Responses and Protection in Volunteers Receiving the Live Attenuated Oral ETEC Vaccine ACE257 followed by Virulent ETEC H10407 Challenge Ethan Gough, Soumya Basu, Jessica Brubaker, Barbara DeNeraing, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9284363/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Enterotoxigenic Escherichia coli (ETEC) remains a major cause of diarrheal morbidity with no licensed vaccines. Role of gut microbiota in vaccine immunogenicity and protection was investigated using 16S rRNA sequencing from the stool samples of 27 volunteers receiving two doses of the live attenuated oral ETEC vaccine ACE527 followed by virulent ETEC H10407 challenge. Systemic and mucosal IgG and IgA responses to heat-labile toxin-B (LTB) and colonization-factor-antigen-I (CFA/I) were quantified by ELISA in serum and antibody-in-lymphocyte-supernatant (ALS). Microbiome α-diversity, β-diversity, and taxa–immune associations were evaluated using regression models, MiRKAT, and relaxed LASSO. Vaccination increased (~ 25–30%) Eubacterium_brachy_group, Family_XIII_AD3011 and Actinomyces. Higher α-diversity (inverse-Simpson) was associated with reduced ALS anti-LTB and CFA/I IgA responses, whereas β-diversity correlated with increased serum anti-CFA/I IgA. Members of Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, and Veillonellaceae enhanced immune responses and protection against severe diarrhea and ETEC colonization, while Ruminococcaceae, Sutterellaceae, Coriobacteria, Clostridia, and Actinobacteria showed antagonistic associations. ETEC Diarrhea vaccine microbiome immune response sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Enterotoxigenic Escherichia coli (ETEC) diarrhea and associated morbidity and mortality remain a significant global health concern, especially among children and travelers, particularly in low- and middle-income countries 1 , 2 . ETEC causes an estimated 220 million diarrhea episodes globally, with approximately 75 million episodes occurring in children under 5 years of age. The mortality estimates for children under 5 years range from 18,700 to 42,000 deaths annually 3 . The development of effective vaccines against ETEC has been a long-standing priority for the WHO, with the potential to significantly reduce the burden of diarrheal disease 4 , 5 . The human gut microbiota, a complex ecosystem of microorganisms residing in the gastrointestinal tract, plays a crucial role in shaping the host immune response. Recent advances in microbiome research have revealed the intricate relationships between gut bacteria and various aspects of human health, including immune function and vaccine efficacy 6 – 8 . The gut microbiota has been shown to modulate both innate and adaptive immune responses, influencing the development and function of immune cells, cytokines and antibody production, and the host’s overall immune landscape 8 . Vaccines, specifically administered orally, must navigate the complex microbial environment of the gut to elicit an effective immune response. The interplay between the vaccine strains, the resident microbiota, and the host immune system is multifaceted and can impact vaccine efficacy. Recent studies have highlighted the importance of considering gut microbiota in vaccine development and evaluation 9 . For instance, polio and rotavirus vaccines have shown that differences in gut microbiota composition can influence vaccine efficacy 10 . We previously showed in a human ETEC experimental challenge model that specific gut flora is predictive of post-challenge diarrhea outcomes 11 . Understanding these relationships is crucial for optimizing vaccine design and predicting vaccine outcomes in diverse populations. However, evidence for associations among the gut microbiome, immune responses, and the efficacy of enteric vaccines such as ETEC remains limited. In this study (Fig. 1 ), we aimed to determine the changes in gut microbiome following immunization and the potential associations between gut microbiome, vaccine-induced immunity, and vaccine efficacy using a randomized clinical trial where volunteers were immunized with a candidate oral ETEC vaccine ACE527 and subsequently challenged with the virulent ETEC strain H10407. Results Does vaccination change the gut microbiome? Specific microbiome changes following vaccination (between V0 and C-1) McNemar’s test for paired data indicated three taxa differed in presence or absence between V0 and C-1. Eubacterium_brachy_group increased from among 3.7% of participants at V0 to 33.3% of participants at C-1 with a p-value of 0.027, alongside two other groups, Family_XIII_AD3011 and Actinomyces, both detected in 3.7% of participants at V0 and 29.6% at C-1 with p-values of 0.046. Changes in gut microbiome α-diversity between V0 and C-1 The analysis of α-diversity reveals a relatively stable gut microbiome structure between V0 and C-1. The Shannon index, inverse Simpson index, and Pielou’s evenness all showed slight increases (< 10%) in diversity from V0 to C-1, but none of these changes were statistically significant (p-values ranging from 0.335 to 0.757). (Table 1 ). Table 1 αdiversity changes between V0 and C-1 Indices V0 Median [IQR] C-1 Median [IQR] p-value Shannon 2.99 [2.66,3.14] 3.03 [2.87,3.11] 0.693 Inverse Simpson 11.73 [8.79,13.43] 12.57 [9.78,13.83] 0.335 Pielou’s Evenness 0.69 [0.66,0.73] 0.70 [0.67,0.72] 0.757 Changes in gut microbiome β-diversity between V0 and C-1 Within-subject β-diversity between V0 and C-1 was significantly greater than zero. The median Bray-Curtis dissimilarity of 0.28 indicated a moderate level of similarity in genus composition and abundance between V0 to C-1 microbiomes (p < 0.001) with a narrow interquartile range (0.21 to 0.32) (Table 2 ) . On a similar note, the median Jaccard similarity of 0.44 indicated moderate overlap in genus detection between microbiomes, with an average 44% dissimilarity (p < 0.001). Table 2 Within-subject β-diversity from V0 to C-1 β-diversity index Median [IQR] P-value Within-subject Bray Curtis 0.28 [0.21,0.32] < 0.001 Within-subject Jaccard 0.44 [0.35,0.48] < 0.001 How does the microbiome correlate with the vaccine-induced immune responses? The vaccine immune responses associations with the microbiome were depicted by estimating mean of antibody fold changes (normalized to a Gaussian distribution) per log-scale shifts in microbiome diversity indices using linear regression. We analyzed the primary vaccine antigens CFA/I and LTB, which are also present in the challenge strain and have the potential to be protective. LTB antibody fold changes per log 2 increase in microbiome α-diversity The analysis of average fold change in LTB IgA levels in ALS between V0 and V21 per log 2 increase in α-diversity revealed a statistically significant negative association with inverse Simpson diversity, where each log 2 increase in inverse Simpson diversity was associated with a 0.72 (95%CI: -1.37, -0.07, p = 0.037) decrease in ALS LTB IgA V0 to V21 fold change. The Shannon index showed a near statistically significant inverse relationship (-1.97; 95% CI: -4.00, 0.05, p = 0.064) (Fig. 2 ). A similar trend of negative association with α-diversity indices was observed for fold change of LTB IgA (V0 to V28) in ALS, and LTB IgA or IgG fold change in serum (V0 to V21 and V0 to V31) however, they were not statistically significant. CFA/I Antibody fold Changes per log increase in microbiome α-diversity Average fold change in CFA/I IgA from V0 to V21 in ALS per log 2 increase in α-diversity was also negatively associated with the inverse Simpson index (-0.8, 95% CI: -1.4,0.1, p = 0.023) and showed a near statistically significant inverse association with the Shannon index (-2.0, 95%CI: -4.1, -0.03, p = 0.055). Similarly, CFA/I IgA responses from V0 toV21 in serum suggest that a per log 2 increase in α-diversity was associated with reduced IgA fold change (p < 0.05) (Fig. 2 ). Similar trends were observed between V0 and V31 CFA/I antibody fold increase, but the association was not statistically significant. ( Supplementary File 1 ). Fold Change of CFA/I IgG in serum from V0 to V21 and V0 to V31 per log 2 increase in α-diversity was not statistically significant and did not show any distinct trend. LTB antibody fold changes with an increase in β-diversity Fold change from V0 to V21 and V0 to V31 of LTB IgA titers in serum did not show a significant association with β-diversity. The Bray-Curtis and Jaccard indices showed considerable compositional scatter, with no evidence of association with LTB IgA fold changes (p > 0.05) in serum. Similarly, β-diversity analyses of ALS LTB IgA fold change V0 to V21 and V0 to V28 indicated no associations. CFA/I antibody fold changes associated with increase in β-diversity A greater variability in microbiota composition (β-diversity) was associated with larger fold change in CFA/I IgA from V0 to V21 in serum [MiRKAT p = 0.030 with Bray-Curtis and MiRKAT p = 0.029 with Jaccard] (Fig. 3 ). There was no uniformly observable trend or statistically significant association (p > 0.05) between CFA/I IgG fold change from V0 to V21 or V0 to V31 and β-diversity using either Bray Curtis or Jaccard dissimilarity indices. ALS CFA/I IgA fold change also showed no significant associations with β-diversity for either the V0 to V21 or V0 to V28 periods (MiRKAT p > 0.05). Predictors of fold changes in LTB antibody titers following vaccination Between V0 to V21, a group of six bacterial taxa were identified by relaxed LASSO regression that predicted fold increase in LTB IgA in ALS titers, including Erysipelatoclostridium, Lachnospiraceae_FCS020_group, Anaerostipes, Haemophilus, Papillibacter, and Phascolarctobacterium. Presence of the bacterial genus Papillibacter was associated with an increased fold change of 1.01 (95%CI: 0.33,1.69; adjusted p = 0.028) ( Fig. 4 a ) . In contrast, a 0.1% increase in relative abundance of Erysipelatoclostridium was associated with a 0.10 (95% CI:0.01,0.20, p = 0.032) increase of LTB IgA antibody titers in ALS, but it was not statistically significant after correction for multiple testing ( Supplementary File 1 ). Similarly, two bacterial taxa, Slackia and Eubacterium nodatum group, were identified as predictors of LTB IgA fold increase from V0 to V28 in ALS ( Fig. 4 a ) . A 0.1% increase in the relative abundance of Slackia was associated with − 0.71 (95%CI: -1.11, -0.31; adjusted p = 0.002) decrease in fold change, while the presence of Eubacterium_nodatum_group was associated with a 1.37 (95%CI:0.59,2.15; adjusted p = 0.002) increase in fold change ( Supplementary File 1 ). No significant specific taxon associations were found for LTB IgA or IgG fold changes in serum. Predictors of fold change in CFA/I antibody titers following vaccination Twenty-five taxa were selected as predictive of CFA/I IgA V0 to V28 fold change in ALS although Erysipelotrichaceae_UCG_003, Eubacterium_nodatum_group, Merdibacter, Terrisporobacter, Actinomyces, Ruminococcus_gauvreauii_group, Lachnospiraceae_UCG_002 and Sutturella were statistically significant. Each 0.1% increase in Erysipelotrichaceae_UCG_003 and the presence of Merdibacter, Ruminococcus_gauvreauii_group, and Lachnospiraceae_UCG_002 were associated with an increased CFA/I IgA fold change of 0.07 (95%CI: 0.05,0.08; p < 0.001), 0.47 (95%CI: 0.04,0.90;p = 0.033), 0.46 (95%CI: 0.05,0.88; p = 0.031) and 0.43 (95%CI: 0.02,0.85, p = 0.041), respectively, but only Erysipelotrichaceae_UCG_003 remained statistically significant after correction for multiple testing. Conversely, each 0.1% increase in Sutturella, and the presence of Eubacterium_nodatum_group and Actinomyces were associated with a decreased fold change of CFA/I IgA, − 0.19 (95%CI: -0.32,-0.06;p = 0.008), -0.48 (95CI: -0.96,-0.01; p = 0.045) and − 0.47 (95%CI: -0.92,-0.02; p = 0.043), respectively, but none were statistically significant after adjustment of multiple testing ( Supplementary File 1 ). No significant specific taxon associations could be found for CFA/I IgA fold changes in ALS. Eight and fifteen bacterial taxa were selected as predictors of CFA/I IgA fold changes in serum between V0 to V21 and V0 to V31, respectively ( Supplementary File 1 ). Between V0 to V21, presence of DTU089 was associated with a -0.87 (95%CI: -1.36,-0.38; adjusted p = 0.009) decreased fold change, while presence of Faecalitalea and Hungatella were associated with a 0.88 (95%CI: 0.14,1.62, p = 0.021) and 0.88 (95%CI: 0.25,1.51, p = 0.008) fold increase that were not statistically significant after adjustment of multiple testing (adjusted p ≥ 0.05). From V0 to V31, presence of Hungatella was also associated with a 0.65 (95%CI: 0.11.1.18; p = 0.020) increased fold change, but it was not statistically significant after adjustment for multiple testing (adjusted p = 0.264). Other taxa identified as predictors of serum IgA V0 to V31 increased fold change were Akkermansia (0.91, 95%CI:0.41,1.42; p = 0.001), and taxa associated with decreased fold change were Christensenella (-0.63,95%CI: -1.12, -0.15; p = 0.013) and Peptococcus (-0.63, 95% CI:-1.19,-0.08,p = 0.027) ( Fig. 4 b ) . However, only Akkermansia was statistically significant after adjustment for multiple testing (adjusted p = 0.016) ( Supplementary File 1 ). However, Akkermansia, Libanicoccus, and DTU089 were also identified as important predictors of CFA/I IgA fold change in serum in both periods V0 to V21 and V0 to V31 but were not statistically significant as independent predictors. The CFA/I IgG titer fold change in serum between V0 and V21 was inversely associated with Sutturella. A 0.1% increase in relative abundance of Sutterella was associated with a -0.33 (95%CI: -0.51, -0.14, adjusted p = 0.001) decreased fold change. Furthermore, between V0 and V31, Raoultibacter and Sutterella were associated with a lower fold change in CFA/I IgG antibody titers. Presence of Raoultibacter and 0.1% increase in relative abundance of Sutturella in serum were associated with CFA/I IgG fold change of -0.27 (95%CI: -0.45, -0.09, adjusted p = 0.010) and − 1.19 (95%CI: -2.09, -0.29, adjusted p = 0.011) respectively (Fig. 4 c). How does the day before challenge microbiome correlate with protection from diarrhea following challenge with virulent ETEC H10407 strain? Predictors of MSD At C-1, Bacteroides, Eubacterium_eligens_group, Veillonella and Dialister were selected by relaxed LASSO as predictors of MSD (not protective) after challenge with ETEC H10407. However, no individual genus was shown to be significantly associated with MSD, which might be due to the small sample size. Predictors of shedding of the challenge strain following ETEC challenge Prevotella_7, Defluviitaleaceae_UCG_011 and Allisonella were selected as important predictors of shedding of the challenge strain in stool following challenge with ETEC H10407. The presence of Allisonella was associated with a 1.27 log 10 increase in maximum shedding (95%CI: 0.62,1.93, adjusted p = 0.006) compared to the absence of Allisonella, while the presence of Prevotella_7 and Defluviitaleaceae_UCG_011 were associated with − 1.07 (95%CI: -1.85,-0.30, adjusted p = 0.026) and − 0.90 (95%CI:-1.62,-0.18, adjusted p = 0.026) log10 decrease in max shedding. ( Fig. 4 d ) . There were no significant associations between α-diversity or β-diversity measures at C-1 and MSD, maximum shedding, or length of shedding following challenge. No taxa were selected as strong predictors of the length of shedding by relaxed LASSO regression. Phylogenetic analysis reveals beneficial and harmful taxa Visualization of the phylogenetic relationships between taxa associated with various outcomes (diarrhea and shedding) or vaccine antibody responses (Fig. 5 ) shows that despite the diversity of these genera, they belonged to a handful of higher taxonomic groups (family, class, or order). Genera within the families Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, Veillonellaceae, and the class Bacilli were associated with protective effects of the ACE527 ETEC vaccine, as evidenced by their correlations with reduced odds of MSD, decreased challenge ETEC strain colonization (shedding), and enhanced antibody responses following vaccination. Conversely, taxa from the families Ruminococcaceae and Sutterellaceae, the classes Coriobacteria and Clostridia, and the phylum Actinobacteria were linked to failure of the ACE527 ETEC vaccine to protect the host from ETEC colonization and diarrhea, including association with higher odds of MSD, increased ETEC shedding, and diminished vaccine-induced antibody titers. Notably, within the class Clostridia, certain members of the family Lachnospiraceae demonstrated protective associations, underscoring the functional diversity that exists even within taxonomic groups that had detrimental effects. Prediction of potentially significant microbial pathways connected to antibody responses To gain insight into biological mechanisms that may contribute to the ability of taxa to predict outcomes or antibody responses associated with ACE527 vaccine, we identified PICRUSt2-predicted MetaCyc pathways that differentiate harmful (not protective) and protective taxa. We then performed overrepresentation analyses to determine whether pathways that differentiated protective or harmful taxa were more likely to be involved in specific MetaCyc pathway types. Thirty-six pathways were determined to be more or less frequent among protective or harmful taxa. Among these pathways, taxa that predicted protection from MSD were more likely to carry pathways involved in polymeric compound degradation or glycan pathways, and those that predicted increased serum CFA/I IgA antibody titers from V0 to V31 were more likely to carry pathways involved in amino acid biosynthesis. In contrast, taxa that predicted decreased serum CFA/I IgA antibody titers from V0 to V21 or V31 were less likely to be involved in C1 compound utilization and assimilation and were less likely to be involved in carbohydrate biosynthesis. However, only pathways involved in amino acid metabolism among taxa that predicted increased serum CFA/I IgA antibody titers V0 to V31 showed significant overrepresentation after FDR-adjustment [including L-methionine biosynthesis III, superpathway of L-methionine biosynthesis (by sulfhydrylation), superpathway of L-methionine biosynthesis (transsulfuration)]. Conversely harmful taxa that predicted decreased LTB IgA antibody titers in ALS from V0 to V21 or decreased serum CFA/I IgG antibody titers from V0 to V31 were also more likely to carry pathways involved in amino acid biosynthesis (these pathways were different from L-methionine biosynthesis); while those that predicted reduced ALS CFA/I IgA antibody titers from V0 to V28 were more likely to carry pathways involved in cell structure biosynthesis and antibiotic resistance or nucleoside and nucleotide biosynthesis. While harmful taxa, which predicted decreased serum CFA/I IgG V0 to V21 or reduced serum CFA/I IgG antibody titers V0 to V31 were less likely to carry pathways involved in fatty acid and lipid biosynthesis and those that predicted reduced ALS CFA/I IgA V0 to V28 were less likely to carry pathways involved in amino acid metabolism. However, none of these pathways showed statistically significant overrepresentation after FDR-adjustment. For specific pathways that were more or less likely to be carried by protective or harmful taxa, and which genera carried these pathways, see Supplementary File 2 . Discussion This study, using an oral live ETEC vaccine-challenge clinical trial, showed that the microbiota can influence vaccine-induced immunity and vaccine efficacy. Among ACE527 vaccine recipients, although the overall microbiome remained relatively stable, measurable changes were observed that were associated with mucosal and systemic vaccine-induced immunity and protection against MSD. This study also identified taxa that are predictive of vaccine efficacy. In this study, gut microbiome α-diversity remained largely stable between pre-vaccination (V0) and pre-challenge (C-1), with only minor, non-significant changes (median Shannon index: 2.99 to 3.03). This structural stability is notable, as major microbiome disruptions have been linked to altered vaccine responses in prior studies 12 . Despite this overall stability, Eubacterium_brachy_group, Family_XIII_AD3011, and Actinomyces showed marked increases in detection between V0 and C-1, suggesting targeted microbial shifts that are associated with vaccine efficacy or immune modulation 9 , 13 , 14 . α-diversity indices were inversely associated with mucosal antibody responses. Specifically, higher inverse Simpson diversity was significantly associated with lower fold increases in ALS LTB- and CFA/I-specific IgA titers, with a similar trend observed for the Shannon index. These findings suggest that greater microbial diversity may dampen vaccine-induced mucosal antibody responses, consistent with prior reports implicating competitive exclusion of immunostimulatory taxa 15 . Within-subject β-diversity changes between V0 and C-1 was moderate but significant, with a median Bray–Curtis dissimilarity of 0.28 and a median Jaccard similarity of 0.44 (p < 0.001), indicating measurable compositional shifts despite overall stability. Associations between β-diversity and antibody responses were antigen-specific. No significant relationships were observed for LTB-specific IgA responses; however, CFA/I-specific IgA fold changes in serum from V0 to V21 were positively associated with β-diversity, as demonstrated by MiRKAT analyses (Bray–Curtis p = 0.030; Jaccard p = 0.029). This suggests that microbiome community configuration, rather than overall diversity, influenced CFA/I-specific immune responses. Overall, these findings align with broader literature indicating that both α- and β-diversity shape vaccine outcomes, where lower α-diversity may favour stronger responses, while greater β-diversity reflects a dynamic microbiome capable of modulating immune priming and vaccine-induced protection 16 . In our previous study with ETEC H10407 experimental human challenge model, we identified 32 operational taxonomic units (OTUs) that could predict ETEC challenge outcomes, with 12 OTUs associated with resistance to diarrhea, despite individual variations 17 . The findings suggested that certain bacterial taxa, such as Prevotella copri and Bacteroides species, may play a protective role against ETEC colonization, offering potential targets for probiotic development 17 . We found consistent data in the current study that Bacteroides and Prevotella_7 predicted reduced odds of MSD and reduced shedding, respectively, in the vaccinated group, but only the Prevotella_7 results were statistically significant. This study highlights a strong link between gut microbiome composition, its functional capacity, and ACE527 vaccine–induced immunity, underscoring the microbiome’s dual role in promoting or impairing protection against MSD. Taxa associated with protection including Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, Lachnospiraceae, Veillonellaceae, and the class Bacilli are largely obligate anaerobes that produce short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate. SCFAs support gut barrier integrity, suppress inflammation, and modulate immune responses, providing a mechanistic explanation for their association with reduced diarrhea severity and enhanced vaccine-induced antibody responses 14 , 18 – 24 . Consistent with prior studies, Lachnospiraceae abundance correlated with improved responses to oral vaccines 25 . Veillonella species further enhance mucosal immunity by converting lactate into weaker acids that limit pathogen colonization, while Bacillus species exert immunomodulatory effects and promote beneficial microbial interactions 26 , 27 . Together, these taxa create a gut environment favourable for effective immune priming. In contrast, Ruminococcaceae, Sutterellaceae, Coriobacteriia, Clostridia, and Actinobacteria were associated with adverse outcomes, including increased risk of diarrhea or reduced vaccine efficacy 28 . Taxa such as Intestinibacter, Faecalitalea, Erysipelotrichaceae_UCG_003, and Sutterella predicted lower CFA/I-specific IgA and IgG responses. Sutterella, in particular, has been linked to increased pathogen shedding and degradation of secretory IgA, weakening mucosal defences and facilitating pathobiont persistence 29 , 30 . Additional predictors of diarrhea included Bacteroides, Eubacterium_eligens_group, Veillonella, and Dialister. While often commensal, Bacteroides can become pathogenic under dysbiosis, and Veillonella and Dialister are associated with intestinal inflammation 31 – 34 . Notably, Allisonella correlated with increased pathogen shedding, suggesting impaired clearance or enhanced replication. Collectively, these findings emphasize the context-dependent influence of microbiome balance on vaccine responsiveness and disease susceptibility. Functional pathway analyses provided mechanistic insight into these associations. Taxa linked to reduced MSD or enhanced antibody responses exhibited depletion of MetaCyc pathways involved in carbohydrate and cell-surface biosynthesis, including reduced UDP-N-acetyl-D-glucosamine and E. coli O-antigen building block pathways 35 – 37 . This suggests diminished peptidoglycan and surface polysaccharide synthesis, potentially limiting immune evasion and enhancing exposure of innate immunostimulatory ligands such as lipid A 38 . Beneficial taxa were also enriched for amino acid biosynthesis pathways, particularly L-methionine biosynthesis, which correlated with higher CFA/I IgA responses and may support T helper cell activation under conditions of limited dietary methionine 39 , 40 . In contrast, taxa associated with increased MSD or reduced antibody responses showed enrichment of peptidoglycan, amino acid, and lipid biosynthesis pathways, potentially skewing immunity toward NOD1/2-mediated inflammation and suboptimal antigen presentation 41 – 44 . Collectively, these taxonomic and functional patterns indicate that microbiome-encoded variation in bacterial cell wall and membrane biosynthesis shapes mucosal immunity and vaccine responsiveness 45 . This is the first study to analyze the influence of microbiome on vaccine-induced immune responses and vaccine efficacy of an oral ETEC vaccine. The study population is relatively homogeneous, healthy American volunteers with limited or no prior exposure to ETEC. The vaccine efficacy was tested against a well-characterized ETEC challenge strain in a controlled environment within an inpatient unit, thereby reducing confounding factors that could bias the results. This study also has limitations, including a small sample size and reliance on 16S rRNA gene sequencing, which limits taxonomic resolution and precludes direct conclusions about strain-level variation. Functional insights were derived from predictive metagenomics (PICRUSt2) and therefore should be interpreted as hypothesis-generating. The comparative analysis focused on two timepoints for microbiome profiling, which missed any short-term changes in dynamics. Future studies could be carried out with serial sampling and in geographically diverse population incorporating multi-omics approaches (metagenomics, metatranscriptomics, and metabolomics). This study highlights the intricate relationship between gut microbial ecology and vaccine immunogenicity and protection from colonization and diarrhea. We demonstrate that higher gut microbial α-diversity, while generally considered a hallmark of gut health, was paradoxically associated with reduced mucosal IgA responses to key ETEC antigens, suggesting a potential immunoregulatory effect. In contrast, increased β-diversity reflecting shifts in community structure was positively associated with systemic IgA responses, emphasizing the role of microbial composition in shaping immune priming. Additionally, specific bacterial taxa were found to either enhance or impair vaccine responses and shedding dynamics. These findings underscore the potential for microbiota-informed strategies to enhance the efficacy of oral vaccines. Methods ETEC vaccine clinical trial and samples ETEC vaccine and challenge strain : ACE527 is a live oral ETEC vaccine composed of three attenuated ETEC strains designed to express a range of ETEC colonization factor antigens (CFA) comprising coli-surface antigens (CS), namely CFA/I, CFA/II (CS1, CS2, CS3), and CFA/IV (CS5, CS6) and the B-subunit of heat-labile enterotoxin (LTB) 46 – 47 . Studies have demonstrated that the ACE527 vaccine is safe and can elicit significant immune responses to key ETEC antigens 46 – 48 H10407 is a wild-type virulent ETEC, serotype O78:H11 that produces both heat-labile (LT) and heat-stable (ST) toxins and expresses colonization factor CFA/I 46,48 . ACE527 vaccine clinical trial design and samples As reported previously, in a placebo-controlled, double-blind, phase 2b study, 70 healthy American volunteers were enrolled and randomized to receive either the placebo [CeraVacx Buffer (Cera Products, Columbia, MD)] or two doses of ACE527 in CeraVax (each dose approximately 10 11 CFU of ACE527), 3 weeks apart 46 . Four weeks after their second dose, a total of 27 ACE527 recipients and 27 placebo recipients were challenged with 2 × 10 7 CFU of H10407 virulent ETEC strain in the inpatient unit of the Centre for Immunization Research (CIR), JHU (Fig. 1 ). The vaccine was well tolerated and induced robust immune responses to key antigens. Although only a 27% reduction was observed in the primary endpoint, moderate-to-severe diarrhea (MSD), the vaccine significantly reduced intestinal colonization by the challenge strain, as measured by quantitative fecal culture 2 days after challenge, indicating the induction of a functional immune response to the CFA/I antigen. In a post-hoc analysis of additional exploratory diarrhea endpoints, the vaccine protected (41% p = 0.03) against severe diarrhea based on stool volume (> 800 grams of grade 3–5 stools over the inpatient surveillance period). Of note, a later phase 2b trial with three doses of lyophilized ACE527 (∼3 × 10 9 of each strain per dose) administered orally with dmLT adjuvant increased the PE to 65.9% (95% confidence interval [CI] 5.4–87.7, p = 0.003) 49 . Samples and data analyzed in the present study In the present study, since our primary aim was to elicit the association between microbiota with immune responses after vaccination and challenge, we focused only on the ACE527 vaccinees. Samples and data were used from the 27 volunteers who completed two doses of the ACE527 vaccine and were challenged with ETEC H10407 as shown in Fig. 1 . For sequencing and analysis of the gut microbiome, stool samples from 27 volunteers at V0 (before the first vaccine dose) and C-1 (day before challenge) were used. Specific microbial taxa associations with immune responses between V0 to V21 (day of the second dose), V0 to V28 (7 days after the second dose) and V0 to V31 (10 days after the second dose) were determined through statistical analysis mentioned in later sections. Stool samples were collected up to three times daily after challenge to monitor shedding of the H10407 challenge strain, with quantitative cultures performed for ACE527 recipients on days 2 and 4 to measure bacterial load as CFU/g stool. In ACE527 vaccinees, a significant reduction was seen in the level of shedding of H10407 in stool on day 2 after challenge, whereas by day 4 their level of shedding approximated that in placebo recipients 46 . Taxon association post-challenge with outcome (MSD), maximum shedding, and length of shedding of challenge strain were determined statistically as described later. Vaccine-induced antibody titers data The immunology methods and data have been reported previously 46 . The data from the systemic and mucosal antibody responses to potential protective vaccine antigens (LTB, CFA/I), which are also present in the challenge strain, measured by ELISA in serum and ALS were used in this study 47 , 48 . Serum IgG and IgA responses were evaluated on V0, V21, and V31. Mucosal immune responses were assessed using the ALS assay (for days V0, V21, and V28), which measures IgA secreted by peripheral blood mononuclear cells (PBMC) circulating to mucosal inductive sites, peaking at 7 to 10 days following oral immunization. DNA extraction and sequencing Gut microbiome composition and functional profiles were ascertained from 300mg of fecal specimen collected on V0 and C-1. Total DNA was extracted using the DNeasy® PowerSoil® Pro Kit with bead beating 50 . The V3-V4 region of the 16S rRNA gene was amplified by PCR, barcoded and dual-indexed according to established protocols 51 . DNA was quantified using an Agilent Technologies 2100 Bioanalyzer before pooling. High-throughput next-generation sequencing of the V3-V4 hypervariable region of the 16S rRNA gene was performed using 319 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) universal primers containing a linker sequence required for Illumina MiSeq 250 bp paired-end sequencing. Negative controls were included (n = 2). Genome processing, quality control and in silico functional analyses Sequenced reads were trimmed from forward and reverse adaptor sequences using cutadapt with default settings 52 . Reads were quality-filtered, deduplicated, chimeras removed and amplicon sequence variants (ASV) inferred using DADA2 53 . Forward and reverse reads were merged with 20bp overlap, and taxonomies were assigned using DADA2 against the Silva database 53 , 54 . Contaminants were identified and removed using decontam 55 . ASV abundances were aggregated to the genus level and normalized to relative abundances for statistical analyses. Taxon relative abundances were dichotomized as present or absent if the relative abundance distribution fitted a zero-inflated beta-distribution better than a beta distribution using generalized additive models for location, scale and shape. We inferred functional profiles from ASV abundances using PICRUSt2 56 . Predicted bacterial metabolic pathway abundances were derived using the MetaCyc database. Pathway abundance contributions were stratified by ASV and aggregated up to the genus level. Statistical analysis Antibody titers against two ETEC antigens viz ., LTB and CFA/I were transformed into fold changes from V0 to V21, V0 to V28 or V0 to V31, and normalized to a Gaussian distribution prior to analysis 46 – 48 . We determined the association between diarrhea and microbiota α-diversity (inverse Simpson diversity, Shannon diversity, and Pielou’s evenness) by bias reduced simple logistic regression to accommodate data separation due to the small sample size 57 . The dependent variable was MSD versus mild or no diarrhea. α-diversity indices were log 2 transformed. One model was fit for each index. We investigated β-diversity using Bray-Curtis and Jaccard dissimilarity matrices visualized by non-metric multidimensional scaling (NMDS) and compositional associations with diarrhea were tested by microbiome regression-based kernel association test (MiRKAT) 58 . Finally, we identified taxa associated with diarrhea using the relaxed lasso penalized logistic regression, with 10-fold cross validation 59 . A multivariable logistic regression model was fitted using the selected taxa as covariates to estimate odds ratios (OR) and 95% confidence intervals (95%CI) using biased reduced logistic regression. Analyses were repeated as described, but replacing maximum shedding, length of shedding, and antibody titer separately, as the dependent variable, and using linear regression. Maximum shedding was log 10 transformed prior to analyses. Individual taxa selected by relaxed LASSO regression as predictors of MSD, shedding or antibody fold change were classified as protective if they predicted decreased odds of diarrhea, reduced shedding, or increase antibody titer fold change; harmful if they predicted increased diarrhea, shedding, or decreased antibody fold change; or as having no associations otherwise. Two-sided Fisher’s Exact tests were performed to identify MetaCyc pathways (predicted by PICRUSt2) that differentiated protective or harmful taxa from others, with adjustment for multiple hypothesis testing to preserve the false discovery rate (FDR) 60 . Thirty-six MetaCyc pathways were differentially present between these three groups of taxa after FDR-adjustment. We performed overrepresentation analyses using one-sided Fisher’s Exact tests to determine whether the differentially present pathways were more likely to have particular biological functions defined by MetaCyc pathway types 61 – 62 . Changes in α-diversity or individual taxa pre- to post-vaccination were determined by Wilcoxon signed-rank sum test or McNemar’s test for continuous or dichotomous microbiome features as appropriate. Statistical significance was determined at α = 0.05. Taxa present in < 10% of specimens were removed prior to statistical analyses. Relative abundances were fitted to beta and zero-inflated beta distributions using gamlss 63 . α-diversity indices were calculated and NMDS was performed using vegan 64 . MiRKAT was performed using MiRKAT . glmnet was used to perform relaxed lasso penalized regression. Biased reduced logistic regression was performed using brglm2 65 . Normalization of antibody titer fold changes to a Gaussian distribution was performed using best-Normalize . Phylogenic relationships between taxa were visualized using ggtree 66 . All analyses were conducted using R version 4.2.2. Declarations Conflict of Interest None to Declare Ethical Statement The study protocol, stool sample collection, microbiome sequencing, and associated laboratory analyses, was reviewed and approved by the independent Institutional Review Board of Johns Hopkins University [IRB 20100221]. Written informed consent was obtained from all participants prior to enrollment. Stool specimens were collected under approved clinical protocols, anonymized prior to laboratory processing, and handled in compliance with institutional biosafety regulations. Microbiome sequencing and genomic analyses were performed on de-identified samples in accordance with institutional data governance policies and ethical guidelines for genomic research. The vaccine trial was registered under ClinicalTrials.gov ID: NCT01060748. Funding This study was funded by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health (NIH), R01AI153399 (SC), and partially funded by R56AI168316 (SC). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIAID. The clinical trial was supported by the PATH GAT.1371-05689-CTA (00374518-CTA) (CDH). Author Contribution Ethan Gough: Formal analysis, Investigation, Software, Visualization, Writing – original draft ; Soumya Basu: Formal analysis, Investigation, Visualization, Writing – original draft ; Jessica Brubaker: Investigation (Immunology) ; Barbara DeNearing: Investigation (Clinical Trial) ; David A Sack: Investigation (Clinical Trial); Writing – review & editing ; A Louis Bourgeois: Investigation (Clinical Trial); Writing – review & editing ; Richard Walker: Investigation (Clinical Trial); Writing – review & editing; Clayton D. Harro: Investigation (Clinical Trial) ; Subhra Chakraborty: Conceptualization, Project administration, Fund acquisition, Supervision, Writing – review & editing. Acknowledgement We thank the volunteers who participated in the clinical trial. The contributions of members of the clinic and Enteric Vaccine laboratory teams at the JHU Bloomberg School of Public Health are gratefully acknowledged. Data Availability The dataset supporting the findings of this study are available in the ‘Supplementary Files’ accompanying this article. The corresponding author is working with the Johns Hopkins Institutional Review Board (IRB) to determine the extent to which further anonymized individual-level data from human volunteers may be deposited in an appropriate open-access repository in compliance with ethical and regulatory requirements. Pending IRB approval, the data will be made available through a recognized public repository with a persistent identifier. References Khalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, Brewer TG, et al. Morbidity and mortality due to Shigella and enterotoxigenic Escherichia coli diarrhoea: the Global Burden of Disease Study 1990–2016. Lancet Infect Dis. 2018;18(11):1229–40. Tacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, Pulcini C, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis. 2018;18(3):318–27. World Health Organization. Immunization, vaccines and biologicals: Enterotoxigenic Escherichia coli (ETEC). Geneva: WHO; 2022. Pajuelo MJ, Noazin S, Cabrera L, Toledo A, Velagic M, Arias L, Ochoa M, et al. Epidemiology of enterotoxigenic Escherichia coli and impact on the growth of children in the first two years of life in Lima, Peru. Front Public Health. 2024;12:1332319. https://doi.org/10.3389/fpubh.2024.1332319 . Zhang Y, Tan P, Zhao Y, Ma X. Enterotoxigenic Escherichia coli: intestinal pathogenesis mechanisms and colonization resistance by gut microbiota. Gut Microbes. 2022;14(1):2055943. https://doi.org/10.1080/19490976.2022.2055943 . Lynn DJ, Benson SC, Lynn MA, Pulendran B. Modulation of immune responses to vaccination by the microbiota: implications and potential mechanisms. Nat Rev Immunol. 2022;22(1):33–46. https://doi.org/10.1038/s41577-021-00554-7 . Ogunrinola GA, Oyewale JO, Oshamika OO, Olasehinde GI. The human microbiome and its impacts on health. Int J Microbiol. 2020;2020:8045646. https://doi.org/10.1155/2020/8045646 . Selber-Hnatiw S, Rukundo B, Ahmadi M, Akoubi H, Al-Bizri H, Aliu AF, Ambeaghen TU, et al. Human gut microbiota: toward an ecology of disease. Front Microbiol. 2017;8:1265. https://doi.org/10.3389/fmicb.2017.01265 . Rossouw C, Ryan FJ, Lynn DJ. The role of the gut microbiota in regulating responses to vaccination: current knowledge and future directions. FEBS J. 2024. https://doi.org/10.1111/febs.17241 . Kwong KW-Y, Xin Y, Lai NC-Y, Sung JC-C, Wu K-C, Hamied YK, Sze ET-P, Lam DM-K. Oral vaccines: a better future of immunization. Vaccines. 2023;11(7):1232. https://doi.org/10.3390/vaccines11071232 . Lindsay BR, Chakraborty S, Harro C, Li S, Nataro JP, Sommerfelt H, Sack DA, Stine OC. Quantitative PCR and culture evaluation for enterotoxigenic Escherichia coli (ETEC)-associated diarrhea in volunteers. FEMS Microbiol Lett. 2014;352(1):25–31. https://doi.org/10.1111/1574-6968.12362 . Huang B, Wang J, Li L. Recent five-year progress in the impact of gut microbiota on vaccination and possible mechanisms. Gut Pathogens. 2023;15:27. Ciabattini A, Olivieri R, Lazzeri E, Medaglini D. Role of the microbiota in the modulation of vaccine immune responses. Front Microbiol. 2019;10:1305. Ray S, Narayanan A, Vesterbacka J, Blennow O, Chen P, Gao Y, Gabarrini G, et al. Impact of the gut microbiome on immunological responses to COVID-19 vaccination. npj Biofilms Microbiomes. 2023;9:104. Ardura-Garcia C, Curtis N, Zimmermann P. Systematic review of the impact of intestinal microbiota on vaccine responses. npj Vaccines. 2024;9:254. Pop M, Paulson JN, Chakraborty S, Astrovskaya I, Lindsay BR, Li S, Bravo HC, et al. Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli. BMC Genomics. 2016;17:440. Zhang W, Xu X, Cai L, Cai X. Dysbiosis of the gut microbiome in elderly patients with hepatocellular carcinoma. Sci Rep. 2023;13:7797. Gophna U, Konikoff T, Nielsen HB. Oscillospira and related bacteria: from metagenomic species to metabolic features. Environ Microbiol. 2017;19(3):835–41. Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion. Curr Opin Microbiol. 2023;75:102363. Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19(1):29–41. Peng L, Li Z-R, Green RS, Holzman IR, Lin J. Butyrate enhances the intestinal barrier via activation of AMP-activated protein kinase. J Nutr. 2009;139(9):1619–25. Ríos-Covián D, Ruas-Madiedo P, Margolles A, Gueimonde M, De Los Reyes-Gavilán CG, Salazar N. Intestinal short-chain fatty acids and human health. Front Microbiol. 2016;7:185. Atarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, Cheng G, et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science. 2011;331(6015):337–41. Huda MN, Lewis Z, Kalanetra KM, Rashid M, Ahmad SM, Raqib R, Qadri F, et al. Stool microbiota and vaccine responses of infants. Pediatrics. 2014;134(2):e362–72. Zhou P, Manoil D, Belibasakis GN, Kotsakis GA. Veillonellae: beyond bridging species in oral biofilm ecology. Front Oral Health. 2021;2:774115. Mandic-Mulec I, Stefanic P, van Elsas JD. (2015) Ecology of Bacillaceae. Microbiol Spectr 3(2). Rusling M, Karim A, Kaye A, Lee CMJ, Wegman-Points L, Mathis V, Lampeter T, Yuan L-L. Influences of Ruminococcus bromii and Peptostreptococcaceae on voluntary exercise behavior. Front Microbiomes. 2024;3:1389103. Hiippala K, Kainulainen V, Kalliomäki M, Arkkila P, Satokari R. Commensalism of Sutterella spp. Front Microbiol. 2016;7:1706. Kaakoush NO. Sutterella species, IgA-degrading bacteria in ulcerative colitis. Trends Microbiol. 2020;28(7):519–22. Wang S, Yin F, Sun W, Li R, Guo Z, Wang Y, Zhang Y, Sun C, Sun D. Causal relationships between gut microbiota and infectious diseases. Front Immunol. 2024;15:1304973. Lowe SE, Jain MK, Zeikus JG. Biology and ecology of anaerobic bacteria adapted to environmental stress. Microbiol Rev. 1993;57(2):451–509. Shen Y, Fan N, Ma S-X, Cheng X, Yang X, Wang G. Gut microbiota dysbiosis: pathogenesis, diseases, prevention, and therapy. MedComm. 2025;6(5):e70168. Zhu X, Hong G, Li Y, Yang P, Cheng M, Zhang L, Li Y, et al. Site-specific microbial patterns in diarrhea-predominant IBS. Microbiol Spectr. 2021;9(3):e01255–21. Raetz CRH, Whitfield C. Lipopolysaccharide endotoxins. Annu Rev Biochem. 2002;71:635–700. Whitfield C, Trent MS. Biosynthesis and export of bacterial lipopolysaccharides. Annu Rev Biochem. 2014;83:99–128. Lerouge I, Vanderleyden J. O-antigen structural variation: mechanisms and possible roles in animal/plant–microbe interactions. FEMS Microbiol Rev. 2002;26(1):17–47. Matsuura M. Structural modifications of bacterial lipopolysaccharide that facilitate Gram-negative bacteria evasion of host innate immunity. Front Immunol. 2013;4:109. Roy DG, Chen J, Mamane V, Ma EH, Muhire BM, Sheldon RD, Shorstova T, et al. Methionine metabolism shapes T helper cell responses. Cell Metabol. 2020;31(2):250–66. Wu X, Han Z, Liu B, Yu D, Sun J, Ge L, Tang W, Liu S. Gut microbiota contributes to methionine metabolism in host. Front Microbiol. 2022;13:1065668. Alexander C, Rietschel ET. Bacterial lipopolysaccharides and innate immunity. J Endotoxin Res. 2001;7(3):167–202. Pulendran B, Arunachalam PS, O’Hagan DT. Emerging concepts in the science of vaccine adjuvants. Nat Rev Drug Discov. 2021;20(6):454–75. Fritz JH, Le Bourhis L, Sellge G, et al. Nod1-mediated innate immune recognition of peptidoglycan contributes to the onset of adaptive immunity. Immunity. 2007;26(4):445–59. Clarke TB, Davis KM, Lysenko ES, et al. Recognition of peptidoglycan from the microbiota by Nod1 enhances systemic innate immunity. Nat Med. 2010;16(2):228–31. Mantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11(8):519–31. Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009;9(5):313–23. Darsley MJ, Chakraborty S, DeNearing B, Sack DA, Feller A, Buchwaldt C, Bourgeois AL, et al. The oral, live attenuated enterotoxigenic Escherichia coli vaccine ACE527 reduces the incidence and severity of diarrhea in a human challenge model. Clin Vaccine Immunol. 2012;19(12):1921–31. https://doi.org/10.1128/CVI.00364-12 . Harro C, Chakraborty S, Sack D, et al. A combination vaccine consisting of three live attenuated enterotoxigenic Escherichia coli strains expressing colonization factors and heat-labile toxin subunit B is well tolerated and immunogenic. Clin Vaccine Immunol. 2011;18:2118–27. Chakraborty S, Randall A, Vickers TJ, Molina D, Harro CD, DeNearing B, Brubaker J, et al. Interrogation of a live-attenuated enterotoxigenic Escherichia coli vaccine highlights features unique to wild-type infection. npj Vaccines. 2019;4:37. https://doi.org/10.1038/s41541-019-0131-7 . Harro C, Bourgeois AL, Sack D, Walker R, DeNearing B, Brubaker J, Maier N, Fix A, Dally L, Chakraborty S, Clements JD, Saunders I, Darsley MJ. Live attenuated enterotoxigenic Escherichia coli (ETEC) vaccine with dmLT adjuvant protects human volunteers against virulent experimental ETEC challenge. Vaccine. 2019;37(14):1978–86. https://doi.org/10.1016/j.vaccine.2019.02.025 . Carelli M, Griggio F, Mingoia M, Garofalo C, Milanović V, Pozzato N, Leoni F, et al. Detecting carbapenemases in animal and food samples by droplet digital PCR. Antibiotics. 2022;11(12):1696. https://doi.org/10.3390/antibiotics11121696 . Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, Ravel J. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome. 2014;2:6. https://doi.org/10.1186/2049-2618-2-6 . Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–2. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. Glöckner FO. The SILVA database project: an ELIXIR core data resource for high-quality ribosomal RNA sequences. Biodivers Inform Sci Stand. 2019;3:e36125. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226. Douglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8. https://doi.org/10.1038/s41587-020-0548-6 . Smith B, Wilson JB. A consumer’s guide to evenness indices. Oikos. 1996;76:70–82. Wilson N, Zhao N, Zhan X, Koh H, Fu W, Chen J, Li H, Wu MC, Plantinga AM. MiRKAT: kernel machine regression-based global association tests for the microbiome. Bioinformatics. 2021;37(11):1595–7. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J Roy Stat Soc B. 2011;73(3):273–82. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B. 1995;57(1):289–300. Wieder C, Frainay C, Poupin N, Rodríguez-Mier P, Vinson F, Cooke J, Lai RPJ, et al. Pathway analysis in metabolomics: recommendations for the use of over-representation analysis. PLoS Comput Biol. 2021;17(9):e1009105. Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46(D1):D633–9. Stasinopoulos DM, Rigby RA. Generalized additive models for location, scale and shape (GAMLSS) in R. J Stat Softw. 2008;23:1–46. Holland SM. Non-metric multidimensional scaling (NMDS). R Documentation; 2008. Kosmidis I. (2017) Multinomial logistic regression using brglm2. Available at: https://cran.r-project.org/package=brglm2 Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. ggtree: an R package for visualization and annotation of phylogenetic trees. Methods Ecol Evol. 2017;8(1):28–36. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.docx SupplementaryFile2.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviews received at journal 14 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9284363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633672602,"identity":"483dccaa-3cca-4fa4-b227-dd765e87993c","order_by":0,"name":"Ethan Gough","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Ethan","middleName":"","lastName":"Gough","suffix":""},{"id":633672603,"identity":"6d80816f-0f20-4d1b-9fdd-5082c4bc0193","order_by":1,"name":"Soumya Basu","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Soumya","middleName":"","lastName":"Basu","suffix":""},{"id":633672604,"identity":"1b8be5cf-33a0-4784-8376-be4617d5feb4","order_by":2,"name":"Jessica Brubaker","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Brubaker","suffix":""},{"id":633672605,"identity":"84955378-4e7f-4934-ba92-012a14b2c508","order_by":3,"name":"Barbara DeNeraing","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"DeNeraing","suffix":""},{"id":633672606,"identity":"cc450515-9c2d-41b4-bdeb-df187e238d55","order_by":4,"name":"David Sack","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Sack","suffix":""},{"id":633672607,"identity":"5c8c39f4-c9b5-4258-82e6-72708507d38c","order_by":5,"name":"A Louis Bourgeois","email":"","orcid":"","institution":"PATH","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"Louis","lastName":"Bourgeois","suffix":""},{"id":633672608,"identity":"2b7d43ae-05f9-41bf-a192-244f2c0e575d","order_by":6,"name":"Richard Walker","email":"","orcid":"","institution":"PATH","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Walker","suffix":""},{"id":633672609,"identity":"9de37533-a434-4025-9a4d-517be3a3ead8","order_by":7,"name":"Clayton D. Harro","email":"","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Clayton","middleName":"D.","lastName":"Harro","suffix":""},{"id":633672610,"identity":"ee376a73-0d17-41ec-9307-8d5a9cbe3998","order_by":8,"name":"Subhra Chakraborty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIie3OsQrCMBCA4Suuga4XUPoKkUBQKvgqFcEu4txBJJNdfID6Fn0AhxShU+pcNydnHV3EZnFNugnm3w7uOw7A5/vJCAQSkISgzTRwJpMhlT1JNmOKOJIob6rn8YSEV02NkMULaSNMb5a0vCMR6rJC0KkDQcLoTRmiBQb7s51EBeEvQ7g05O1AoCWClh1hYIh0IEyvxbToCCrNJ0mdcvtjuebXg9rNw0KP28c2Htkf+4YKIHFfN4U9zvt8Pt9/9QHUQz6FyN9JawAAAABJRU5ErkJggg==","orcid":"","institution":"Bloomberg School of Public Health, Johns Hopkins University","correspondingAuthor":true,"prefix":"","firstName":"Subhra","middleName":"","lastName":"Chakraborty","suffix":""}],"badges":[],"createdAt":"2026-03-31 21:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9284363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9284363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108394988,"identity":"dfe8df06-3f35-4519-aae9-04df3a557051","added_by":"auto","created_at":"2026-05-04 07:42:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":936211,"visible":true,"origin":"","legend":"\u003cp\u003eOverall scheme of the vaccine trial with focus on the microbiome study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/233b29ea6fb090fb7f4fc7be.png"},{"id":108394990,"identity":"7a8a26b0-4f43-48ff-bfbd-8fea73c7bd14","added_by":"auto","created_at":"2026-05-04 07:42:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241592,"visible":true,"origin":"","legend":"\u003cp\u003eALS LTB, ALS CFA/I and Serum CFA/I IgA Fold Changes per log\u003csub\u003e2 \u003c/sub\u003eincrease in α-diversity (V0 to V21) [antibody titers between two timepoints were transformed into fold changes and normalized to a Gaussian distribution].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/0b7a42bd48de5bd2bfd7c202.png"},{"id":108493092,"identity":"cd63110a-3c42-40e2-ae1a-90759261ea38","added_by":"auto","created_at":"2026-05-05 09:59:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eBray-Curtis β-diversity analyses by fold increase of CFA/I IgA antibody titers from V0 to V21 in serum, \u003cstrong\u003e(b) \u003c/strong\u003eJaccard β-diversity analyses by fold increase of CFA/I IgA from V0 to V21 in serum. Arrows parallel to the non-metric multidimensional scaling (NMDS) axis are perfectly correlated with that axis scores, indicating that variability in microbiome composition represented by that axis is associated with antibody fold change [antibody titers between two timepoints were transformed into fold changes and normalized to a Gaussian distribution]. Colors, yellow to dark blue, indicate an increasing antibody fold change. p-values were produced using MiRKAT.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/b352a468ae7b2edb79717177.png"},{"id":108493373,"identity":"3149cf1b-ec8e-4856-8451-37ab66f19872","added_by":"auto","created_at":"2026-05-05 10:00:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1357584,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between antibody fold change and selected taxa estimated by linear regression. Selected taxa were\u003cstrong\u003e \u003c/strong\u003echosen using the relaxed LASSO to identify key predictors. The normalized average antibody fold change associated with the ‘presence’ and/or ‘per 0.1% increase’ in relative abundance of the genus are indicated in parentheses below taxon. \u003cstrong\u003e(a)\u003c/strong\u003e Key predictors of ALS LTB IgA fold change (V0 to V21 \u0026amp; V0 to V28)\u003cstrong\u003e (b) \u003c/strong\u003eKey predictors of serum CFA/I IgA fold change (V0 to V21 \u0026amp; V0 to V31) \u003cstrong\u003e(c) \u003c/strong\u003eKey predictors of serum CFA/I IgG fold change (V0 to V21 \u0026amp; V0 to V31) \u003cstrong\u003e(d) \u003c/strong\u003eKey predictors for moderate-to-severe-diarrhea (MSD), and max shedding\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/991818d80ac65306a4ddeaeb.png"},{"id":108394993,"identity":"72a46ae4-c51c-44e3-ab59-b58f31f0e6e0","added_by":"auto","created_at":"2026-05-04 07:42:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":844225,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree summarizing the specific taxa selected by the relaxed LASSO as predictors of diarrhea, max shedding, or antibody fold change. Tree tips are genus-level taxa. Shaded areas are phylogenic clades. Rings around the tree, from innermost to outermost ring, indicate whether a genus was associated with diarrhea (inner most), max shedding, ALS LTB IgA V0 to V21 and V0 to V28, serum CFA/I IgA V0 to V21 and V0 to V31, serum CFA/I IgG V0 to V21, and V0 to V31 or ALS CFA/I IgA V0 to V28 (outermost circle). Colored tiles in each ring range from light (adjusted p=1.000) to dark (adjusted p≤0.001). Colors of tree tips and shaded areas are green if taxa predicted decreased MSD, reduced max shedding or increased antibody fold change (protective associations) and are red if taxa predicted increased MSD, increased max shedding or length of shedding or decreased antibody fold change (harmful associations).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/6c82fe6e1e2a9522a3354507.png"},{"id":108804177,"identity":"ab12bdd2-5118-4dbe-ae52-f2286cff9376","added_by":"auto","created_at":"2026-05-08 15:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3909782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/7d094384-1393-4d83-a8cb-48707c37ba46.pdf"},{"id":108394987,"identity":"e6032a3d-ecdc-46a3-aeda-320272363ab4","added_by":"auto","created_at":"2026-05-04 07:42:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":34888,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/9320a681f48d618f0f592ce0.docx"},{"id":108493160,"identity":"e3f6370a-e7a0-417b-a919-da2506cf666a","added_by":"auto","created_at":"2026-05-05 09:59:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":129451,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9284363/v1/ceacf15565d019f60d0ebc74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of Gut Microbiota on Immune Responses and Protection in Volunteers Receiving the Live Attenuated Oral ETEC Vaccine ACE257 followed by Virulent ETEC H10407 Challenge","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnterotoxigenic \u003cem\u003eEscherichia coli\u003c/em\u003e (ETEC) diarrhea and associated morbidity and mortality remain a significant global health concern, especially among children and travelers, particularly in low- and middle-income countries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. ETEC causes an estimated 220\u0026nbsp;million diarrhea episodes globally, with approximately 75\u0026nbsp;million episodes occurring in children under 5 years of age. The mortality estimates for children under 5 years range from 18,700 to 42,000 deaths annually\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The development of effective vaccines against ETEC has been a long-standing priority for the WHO, with the potential to significantly reduce the burden of diarrheal disease\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe human gut microbiota, a complex ecosystem of microorganisms residing in the gastrointestinal tract, plays a crucial role in shaping the host immune response. Recent advances in microbiome research have revealed the intricate relationships between gut bacteria and various aspects of human health, including immune function and vaccine efficacy\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The gut microbiota has been shown to modulate both innate and adaptive immune responses, influencing the development and function of immune cells, cytokines and antibody production, and the host\u0026rsquo;s overall immune landscape\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVaccines, specifically administered orally, must navigate the complex microbial environment of the gut to elicit an effective immune response. The interplay between the vaccine strains, the resident microbiota, and the host immune system is multifaceted and can impact vaccine efficacy. Recent studies have highlighted the importance of considering gut microbiota in vaccine development and evaluation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. For instance, polio and rotavirus vaccines have shown that differences in gut microbiota composition can influence vaccine efficacy\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. We previously showed in a human ETEC experimental challenge model that specific gut flora is predictive of post-challenge diarrhea outcomes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Understanding these relationships is crucial for optimizing vaccine design and predicting vaccine outcomes in diverse populations. However, evidence for associations among the gut microbiome, immune responses, and the efficacy of enteric vaccines such as ETEC remains limited.\u003c/p\u003e \u003cp\u003eIn this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we aimed to determine the changes in gut microbiome following immunization and the potential associations between gut microbiome, vaccine-induced immunity, and vaccine efficacy using a randomized clinical trial where volunteers were immunized with a candidate oral ETEC vaccine ACE527 and subsequently challenged with the virulent ETEC strain H10407.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDoes vaccination change the gut microbiome?\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSpecific microbiome changes following vaccination (between V0 and C-1)\u003c/h2\u003e \u003cp\u003eMcNemar\u0026rsquo;s test for paired data indicated three taxa differed in presence or absence between V0 and C-1. Eubacterium_brachy_group increased from among 3.7% of participants at V0 to 33.3% of participants at C-1 with a p-value of 0.027, alongside two other groups, Family_XIII_AD3011 and Actinomyces, both detected in 3.7% of participants at V0 and 29.6% at C-1 with p-values of 0.046.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eChanges in gut microbiome α-diversity between V0 and C-1\u003c/h3\u003e\n\u003cp\u003eThe analysis of α-diversity reveals a relatively stable gut microbiome structure between V0 and C-1. The Shannon index, inverse Simpson index, and Pielou\u0026rsquo;s evenness all showed slight increases (\u0026lt;\u0026thinsp;10%) in diversity from V0 to C-1, but none of these changes were statistically significant (p-values ranging from 0.335 to 0.757). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eαdiversity changes between V0 and C-1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV0 Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-1 Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.99 [2.66,3.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.03 [2.87,3.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInverse Simpson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.73 [8.79,13.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.57 [9.78,13.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePielou\u0026rsquo;s Evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 [0.66,0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 [0.67,0.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eChanges in gut microbiome β-diversity between V0 and C-1\u003c/h3\u003e\n\u003cp\u003eWithin-subject β-diversity between V0 and C-1 was significantly greater than zero. The median Bray-Curtis dissimilarity of 0.28 indicated a moderate level of similarity in genus composition and abundance between V0 to C-1 microbiomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a narrow interquartile range (0.21 to 0.32) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. On a similar note, the median Jaccard similarity of 0.44 indicated moderate overlap in genus detection between microbiomes, with an average 44% dissimilarity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWithin-subject β-diversity from V0 to C-1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-diversity index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin-subject Bray Curtis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28 [0.21,0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin-subject Jaccard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44 [0.35,0.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHow does the microbiome correlate with the vaccine-induced immune responses?\u003c/h3\u003e\n\u003cp\u003eThe vaccine immune responses associations with the microbiome were depicted by estimating mean of antibody fold changes (normalized to a Gaussian distribution) per log-scale shifts in microbiome diversity indices using linear regression. We analyzed the primary vaccine antigens CFA/I and LTB, which are also present in the challenge strain and have the potential to be protective.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLTB antibody fold changes per log\u003csub\u003e2\u003c/sub\u003e increase in microbiome α-diversity\u003c/h2\u003e \u003cp\u003eThe analysis of average fold change in LTB IgA levels in ALS between V0 and V21 per log\u003csub\u003e2\u003c/sub\u003e increase in α-diversity revealed a statistically significant negative association with inverse Simpson diversity, where each log\u003csub\u003e2\u003c/sub\u003e increase in inverse Simpson diversity was associated with a 0.72 (95%CI: -1.37, -0.07, p\u0026thinsp;=\u0026thinsp;0.037) decrease in ALS LTB IgA V0 to V21 fold change. The Shannon index showed a near statistically significant inverse relationship (-1.97; 95% CI: -4.00, 0.05, p\u0026thinsp;=\u0026thinsp;0.064) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A similar trend of negative association with α-diversity indices was observed for fold change of LTB IgA (V0 to V28) in ALS, and LTB IgA or IgG fold change in serum (V0 to V21 and V0 to V31) however, they were not statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCFA/I Antibody fold Changes per log increase in microbiome α-diversity\u003c/h3\u003e\n\u003cp\u003eAverage fold change in CFA/I IgA from V0 to V21 in ALS per log\u003csub\u003e2\u003c/sub\u003e increase in α-diversity was also negatively associated with the inverse Simpson index (-0.8, 95% CI: -1.4,0.1, p\u0026thinsp;=\u0026thinsp;0.023) and showed a near statistically significant inverse association with the Shannon index (-2.0, 95%CI: -4.1, -0.03, p\u0026thinsp;=\u0026thinsp;0.055). Similarly, CFA/I IgA responses from V0 toV21 in serum suggest that a per log\u003csub\u003e2\u003c/sub\u003e increase in α-diversity was associated with reduced IgA fold change (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar trends were observed between V0 and V31 CFA/I antibody fold increase, but the association was not statistically significant. (\u003cb\u003eSupplementary File 1\u003c/b\u003e). Fold Change of CFA/I IgG in serum from V0 to V21 and V0 to V31 per log\u003csub\u003e2\u003c/sub\u003e increase in α-diversity was not statistically significant and did not show any distinct trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLTB antibody fold changes with an increase in β-diversity\u003c/h3\u003e\n\u003cp\u003eFold change from V0 to V21 and V0 to V31 of LTB IgA titers in serum did not show a significant association with β-diversity. The Bray-Curtis and Jaccard indices showed considerable compositional scatter, with no evidence of association with LTB IgA fold changes (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in serum. Similarly, β-diversity analyses of ALS LTB IgA fold change V0 to V21 and V0 to V28 indicated no associations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCFA/I antibody fold changes associated with increase in β-diversity\u003c/h2\u003e \u003cp\u003eA greater variability in microbiota composition (β-diversity) was associated with larger fold change in CFA/I IgA from V0 to V21 in serum [MiRKAT p\u0026thinsp;=\u0026thinsp;0.030 with Bray-Curtis and MiRKAT p\u0026thinsp;=\u0026thinsp;0.029 with Jaccard] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was no uniformly observable trend or statistically significant association (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between CFA/I IgG fold change from V0 to V21 or V0 to V31 and β-diversity using either Bray Curtis or Jaccard dissimilarity indices. ALS CFA/I IgA fold change also showed no significant associations with β-diversity for either the V0 to V21 or V0 to V28 periods (MiRKAT p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of fold changes in LTB antibody titers following vaccination\u003c/h2\u003e \u003cp\u003eBetween V0 to V21, a group of six bacterial taxa were identified by relaxed LASSO regression that predicted fold increase in LTB IgA in ALS titers, including Erysipelatoclostridium, Lachnospiraceae_FCS020_group, Anaerostipes, Haemophilus, Papillibacter, and Phascolarctobacterium. Presence of the bacterial genus Papillibacter was associated with an increased fold change of 1.01 (95%CI: 0.33,1.69; adjusted p\u0026thinsp;=\u0026thinsp;0.028) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. In contrast, a 0.1% increase in relative abundance of Erysipelatoclostridium was associated with a 0.10 (95% CI:0.01,0.20, p\u0026thinsp;=\u0026thinsp;0.032) increase of LTB IgA antibody titers in ALS, but it was not statistically significant after correction for multiple testing (\u003cb\u003eSupplementary File 1\u003c/b\u003e). Similarly, two bacterial taxa, Slackia and Eubacterium nodatum group, were identified as predictors of LTB IgA fold increase from V0 to V28 in ALS \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. A 0.1% increase in the relative abundance of Slackia was associated with \u0026minus;\u0026thinsp;0.71 (95%CI: -1.11, -0.31; adjusted p\u0026thinsp;=\u0026thinsp;0.002) decrease in fold change, while the presence of Eubacterium_nodatum_group was associated with a 1.37 (95%CI:0.59,2.15; adjusted p\u0026thinsp;=\u0026thinsp;0.002) increase in fold change (\u003cb\u003eSupplementary File 1\u003c/b\u003e). No significant specific taxon associations were found for LTB IgA or IgG fold changes in serum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of fold change in CFA/I antibody titers following vaccination\u003c/h2\u003e \u003cp\u003eTwenty-five taxa were selected as predictive of CFA/I IgA V0 to V28 fold change in ALS although Erysipelotrichaceae_UCG_003, Eubacterium_nodatum_group, Merdibacter, Terrisporobacter, Actinomyces, Ruminococcus_gauvreauii_group, Lachnospiraceae_UCG_002 and Sutturella were statistically significant. Each 0.1% increase in Erysipelotrichaceae_UCG_003 and the presence of Merdibacter, Ruminococcus_gauvreauii_group, and Lachnospiraceae_UCG_002 were associated with an increased CFA/I IgA fold change of 0.07 (95%CI: 0.05,0.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.47 (95%CI: 0.04,0.90;p\u0026thinsp;=\u0026thinsp;0.033), 0.46 (95%CI: 0.05,0.88; p\u0026thinsp;=\u0026thinsp;0.031) and 0.43 (95%CI: 0.02,0.85, p\u0026thinsp;=\u0026thinsp;0.041), respectively, but only Erysipelotrichaceae_UCG_003 remained statistically significant after correction for multiple testing. Conversely, each 0.1% increase in Sutturella, and the presence of Eubacterium_nodatum_group and Actinomyces were associated with a decreased fold change of CFA/I IgA, \u0026minus;\u0026thinsp;0.19 (95%CI: -0.32,-0.06;p\u0026thinsp;=\u0026thinsp;0.008), -0.48 (95CI: -0.96,-0.01; p\u0026thinsp;=\u0026thinsp;0.045) and \u0026minus;\u0026thinsp;0.47 (95%CI: -0.92,-0.02; p\u0026thinsp;=\u0026thinsp;0.043), respectively, but none were statistically significant after adjustment of multiple testing (\u003cb\u003eSupplementary File 1\u003c/b\u003e). No significant specific taxon associations could be found for CFA/I IgA fold changes in ALS.\u003c/p\u003e \u003cp\u003eEight and fifteen bacterial taxa were selected as predictors of CFA/I IgA fold changes in serum between V0 to V21 and V0 to V31, respectively (\u003cb\u003eSupplementary File 1\u003c/b\u003e). Between V0 to V21, presence of DTU089 was associated with a -0.87 (95%CI: -1.36,-0.38; adjusted p\u0026thinsp;=\u0026thinsp;0.009) decreased fold change, while presence of Faecalitalea and Hungatella were associated with a 0.88 (95%CI: 0.14,1.62, p\u0026thinsp;=\u0026thinsp;0.021) and 0.88 (95%CI: 0.25,1.51, p\u0026thinsp;=\u0026thinsp;0.008) fold increase that were not statistically significant after adjustment of multiple testing (adjusted p\u0026thinsp;\u0026ge;\u0026thinsp;0.05). From V0 to V31, presence of Hungatella was also associated with a 0.65 (95%CI: 0.11.1.18; p\u0026thinsp;=\u0026thinsp;0.020) increased fold change, but it was not statistically significant after adjustment for multiple testing (adjusted p\u0026thinsp;=\u0026thinsp;0.264). Other taxa identified as predictors of serum IgA V0 to V31 increased fold change were Akkermansia (0.91, 95%CI:0.41,1.42; p\u0026thinsp;=\u0026thinsp;0.001), and taxa associated with decreased fold change were Christensenella (-0.63,95%CI: -1.12, -0.15; p\u0026thinsp;=\u0026thinsp;0.013) and Peptococcus (-0.63, 95% CI:-1.19,-0.08,p\u0026thinsp;=\u0026thinsp;0.027) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. However, only Akkermansia was statistically significant after adjustment for multiple testing (adjusted p\u0026thinsp;=\u0026thinsp;0.016) (\u003cb\u003eSupplementary File 1\u003c/b\u003e). However, Akkermansia, Libanicoccus, and DTU089 were also identified as important predictors of CFA/I IgA fold change in serum in both periods V0 to V21 and V0 to V31 but were not statistically significant as independent predictors.\u003c/p\u003e \u003cp\u003eThe CFA/I IgG titer fold change in serum between V0 and V21 was inversely associated with Sutturella. A 0.1% increase in relative abundance of Sutterella was associated with a -0.33 (95%CI: -0.51, -0.14, adjusted p\u0026thinsp;=\u0026thinsp;0.001) decreased fold change. Furthermore, between V0 and V31, Raoultibacter and Sutterella were associated with a lower fold change in CFA/I IgG antibody titers. Presence of Raoultibacter and 0.1% increase in relative abundance of Sutturella in serum were associated with CFA/I IgG fold change of -0.27 (95%CI: -0.45, -0.09, adjusted p\u0026thinsp;=\u0026thinsp;0.010) and \u0026minus;\u0026thinsp;1.19 (95%CI: -2.09, -0.29, adjusted p\u0026thinsp;=\u0026thinsp;0.011) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHow does the day before challenge microbiome correlate with protection from diarrhea following challenge with virulent ETEC H10407 strain?\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of MSD\u003c/h2\u003e \u003cp\u003eAt C-1, Bacteroides, Eubacterium_eligens_group, Veillonella and Dialister were selected by relaxed LASSO as predictors of MSD (not protective) after challenge with ETEC H10407. However, no individual genus was shown to be significantly associated with MSD, which might be due to the small sample size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of shedding of the challenge strain following ETEC challenge\u003c/h2\u003e \u003cp\u003ePrevotella_7, Defluviitaleaceae_UCG_011 and Allisonella were selected as important predictors of shedding of the challenge strain in stool following challenge with ETEC H10407. The presence of Allisonella was associated with a 1.27 log\u003csub\u003e10\u003c/sub\u003e increase in maximum shedding (95%CI: 0.62,1.93, adjusted p\u0026thinsp;=\u0026thinsp;0.006) compared to the absence of Allisonella, while the presence of Prevotella_7 and Defluviitaleaceae_UCG_011 were associated with \u0026minus;\u0026thinsp;1.07 (95%CI: -1.85,-0.30, adjusted p\u0026thinsp;=\u0026thinsp;0.026) and \u0026minus;\u0026thinsp;0.90 (95%CI:-1.62,-0.18, adjusted p\u0026thinsp;=\u0026thinsp;0.026) log10 decrease in max shedding. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThere were no significant associations between α-diversity or β-diversity measures at C-1 and MSD, maximum shedding, or length of shedding following challenge. No taxa were selected as strong predictors of the length of shedding by relaxed LASSO regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic analysis reveals beneficial and harmful taxa\u003c/h2\u003e \u003cp\u003eVisualization of the phylogenetic relationships between taxa associated with various outcomes (diarrhea and shedding) or vaccine antibody responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e shows that despite the diversity of these genera, they belonged to a handful of higher taxonomic groups (family, class, or order). Genera within the families Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, Veillonellaceae, and the class Bacilli were associated with protective effects of the ACE527 ETEC vaccine, as evidenced by their correlations with reduced odds of MSD, decreased challenge ETEC strain colonization (shedding), and enhanced antibody responses following vaccination. Conversely, taxa from the families Ruminococcaceae and Sutterellaceae, the classes Coriobacteria and Clostridia, and the phylum Actinobacteria were linked to failure of the ACE527 ETEC vaccine to protect the host from ETEC colonization and diarrhea, including association with higher odds of MSD, increased ETEC shedding, and diminished vaccine-induced antibody titers. Notably, within the class Clostridia, certain members of the family Lachnospiraceae demonstrated protective associations, underscoring the functional diversity that exists even within taxonomic groups that had detrimental effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of potentially significant microbial pathways connected to antibody responses\u003c/h2\u003e \u003cp\u003eTo gain insight into biological mechanisms that may contribute to the ability of taxa to predict outcomes or antibody responses associated with ACE527 vaccine, we identified PICRUSt2-predicted MetaCyc pathways that differentiate harmful (not protective) and protective taxa. We then performed overrepresentation analyses to determine whether pathways that differentiated protective or harmful taxa were more likely to be involved in specific MetaCyc pathway types. Thirty-six pathways were determined to be more or less frequent among protective or harmful taxa. Among these pathways, taxa that predicted protection from MSD were more likely to carry pathways involved in polymeric compound degradation or glycan pathways, and those that predicted increased serum CFA/I IgA antibody titers from V0 to V31 were more likely to carry pathways involved in amino acid biosynthesis. In contrast, taxa that predicted decreased serum CFA/I IgA antibody titers from V0 to V21 or V31 were less likely to be involved in C1 compound utilization and assimilation and were less likely to be involved in carbohydrate biosynthesis. However, only pathways involved in amino acid metabolism among taxa that predicted increased serum CFA/I IgA antibody titers V0 to V31 showed significant overrepresentation after FDR-adjustment [including L-methionine biosynthesis III, superpathway of L-methionine biosynthesis (by sulfhydrylation), superpathway of L-methionine biosynthesis (transsulfuration)].\u003c/p\u003e \u003cp\u003eConversely harmful taxa that predicted decreased LTB IgA antibody titers in ALS from V0 to V21 or decreased serum CFA/I IgG antibody titers from V0 to V31 were also more likely to carry pathways involved in amino acid biosynthesis (these pathways were different from L-methionine biosynthesis); while those that predicted reduced ALS CFA/I IgA antibody titers from V0 to V28 were more likely to carry pathways involved in cell structure biosynthesis and antibiotic resistance or nucleoside and nucleotide biosynthesis. While harmful taxa, which predicted decreased serum CFA/I IgG V0 to V21 or reduced serum CFA/I IgG antibody titers V0 to V31 were less likely to carry pathways involved in fatty acid and lipid biosynthesis and those that predicted reduced ALS CFA/I IgA V0 to V28 were less likely to carry pathways involved in amino acid metabolism. However, none of these pathways showed statistically significant overrepresentation after FDR-adjustment. For specific pathways that were more or less likely to be carried by protective or harmful taxa, and which genera carried these pathways, see \u003cb\u003eSupplementary File 2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, using an oral live ETEC vaccine-challenge clinical trial, showed that the microbiota can influence vaccine-induced immunity and vaccine efficacy. Among ACE527 vaccine recipients, although the overall microbiome remained relatively stable, measurable changes were observed that were associated with mucosal and systemic vaccine-induced immunity and protection against MSD. This study also identified taxa that are predictive of vaccine efficacy.\u003c/p\u003e \u003cp\u003eIn this study, gut microbiome α-diversity remained largely stable between pre-vaccination (V0) and pre-challenge (C-1), with only minor, non-significant changes (median Shannon index: 2.99 to 3.03). This structural stability is notable, as major microbiome disruptions have been linked to altered vaccine responses in prior studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite this overall stability, Eubacterium_brachy_group, Family_XIII_AD3011, and Actinomyces showed marked increases in detection between V0 and C-1, suggesting targeted microbial shifts that are associated with vaccine efficacy or immune modulation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. α-diversity indices were inversely associated with mucosal antibody responses. Specifically, higher inverse Simpson diversity was significantly associated with lower fold increases in ALS LTB- and CFA/I-specific IgA titers, with a similar trend observed for the Shannon index. These findings suggest that greater microbial diversity may dampen vaccine-induced mucosal antibody responses, consistent with prior reports implicating competitive exclusion of immunostimulatory taxa\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Within-subject β-diversity changes between V0 and C-1 was moderate but significant, with a median Bray–Curtis dissimilarity of 0.28 and a median Jaccard similarity of 0.44 (p \u0026lt; 0.001), indicating measurable compositional shifts despite overall stability. Associations between β-diversity and antibody responses were antigen-specific. No significant relationships were observed for LTB-specific IgA responses; however, CFA/I-specific IgA fold changes in serum from V0 to V21 were positively associated with β-diversity, as demonstrated by MiRKAT analyses (Bray–Curtis p = 0.030; Jaccard p = 0.029). This suggests that microbiome community configuration, rather than overall diversity, influenced CFA/I-specific immune responses. Overall, these findings align with broader literature indicating that both α- and β-diversity shape vaccine outcomes, where lower α-diversity may favour stronger responses, while greater β-diversity reflects a dynamic microbiome capable of modulating immune priming and vaccine-induced protection\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In our previous study with ETEC H10407 experimental human challenge model, we identified 32 operational taxonomic units (OTUs) that could predict ETEC challenge outcomes, with 12 OTUs associated with resistance to diarrhea, despite individual variations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The findings suggested that certain bacterial taxa, such as \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e species, may play a protective role against ETEC colonization, offering potential targets for probiotic development\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. We found consistent data in the current study that Bacteroides and Prevotella_7 predicted reduced odds of MSD and reduced shedding, respectively, in the vaccinated group, but only the Prevotella_7 results were statistically significant.\u003c/p\u003e \u003cp\u003eThis study highlights a strong link between gut microbiome composition, its functional capacity, and ACE527 vaccine–induced immunity, underscoring the microbiome’s dual role in promoting or impairing protection against MSD. Taxa associated with protection including Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, Lachnospiraceae, Veillonellaceae, and the class Bacilli are largely obligate anaerobes that produce short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate. SCFAs support gut barrier integrity, suppress inflammation, and modulate immune responses, providing a mechanistic explanation for their association with reduced diarrhea severity and enhanced vaccine-induced antibody responses\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Consistent with prior studies, Lachnospiraceae abundance correlated with improved responses to oral vaccines\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Veillonella species further enhance mucosal immunity by converting lactate into weaker acids that limit pathogen colonization, while Bacillus species exert immunomodulatory effects and promote beneficial microbial interactions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Together, these taxa create a gut environment favourable for effective immune priming. In contrast, Ruminococcaceae, Sutterellaceae, Coriobacteriia, Clostridia, and Actinobacteria were associated with adverse outcomes, including increased risk of diarrhea or reduced vaccine efficacy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Taxa such as Intestinibacter, Faecalitalea, Erysipelotrichaceae_UCG_003, and Sutterella predicted lower CFA/I-specific IgA and IgG responses. Sutterella, in particular, has been linked to increased pathogen shedding and degradation of secretory IgA, weakening mucosal defences and facilitating pathobiont persistence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditional predictors of diarrhea included Bacteroides, Eubacterium_eligens_group, Veillonella, and Dialister. While often commensal, Bacteroides can become pathogenic under dysbiosis, and Veillonella and Dialister are associated with intestinal inflammation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Notably, Allisonella correlated with increased pathogen shedding, suggesting impaired clearance or enhanced replication. Collectively, these findings emphasize the context-dependent influence of microbiome balance on vaccine responsiveness and disease susceptibility.\u003c/p\u003e \u003cp\u003eFunctional pathway analyses provided mechanistic insight into these associations. Taxa linked to reduced MSD or enhanced antibody responses exhibited depletion of MetaCyc pathways involved in carbohydrate and cell-surface biosynthesis, including reduced UDP-N-acetyl-D-glucosamine and \u003cem\u003eE. coli\u003c/em\u003e O-antigen building block pathways\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This suggests diminished peptidoglycan and surface polysaccharide synthesis, potentially limiting immune evasion and enhancing exposure of innate immunostimulatory ligands such as lipid A\u003csup\u003e38\u003c/sup\u003e. Beneficial taxa were also enriched for amino acid biosynthesis pathways, particularly L-methionine biosynthesis, which correlated with higher CFA/I IgA responses and may support T helper cell activation under conditions of limited dietary methionine\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In contrast, taxa associated with increased MSD or reduced antibody responses showed enrichment of peptidoglycan, amino acid, and lipid biosynthesis pathways, potentially skewing immunity toward NOD1/2-mediated inflammation and suboptimal antigen presentation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Collectively, these taxonomic and functional patterns indicate that microbiome-encoded variation in bacterial cell wall and membrane biosynthesis shapes mucosal immunity and vaccine responsiveness\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis is the first study to analyze the influence of microbiome on vaccine-induced immune responses and vaccine efficacy of an oral ETEC vaccine. The study population is relatively homogeneous, healthy American volunteers with limited or no prior exposure to ETEC. The vaccine efficacy was tested against a well-characterized ETEC challenge strain in a controlled environment within an inpatient unit, thereby reducing confounding factors that could bias the results. This study also has limitations, including a small sample size and reliance on 16S rRNA gene sequencing, which limits taxonomic resolution and precludes direct conclusions about strain-level variation. Functional insights were derived from predictive metagenomics (PICRUSt2) and therefore should be interpreted as hypothesis-generating. The comparative analysis focused on two timepoints for microbiome profiling, which missed any short-term changes in dynamics. Future studies could be carried out with serial sampling and in geographically diverse population incorporating multi-omics approaches (metagenomics, metatranscriptomics, and metabolomics).\u003c/p\u003e \u003cp\u003eThis study highlights the intricate relationship between gut microbial ecology and vaccine immunogenicity and protection from colonization and diarrhea. We demonstrate that higher gut microbial α-diversity, while generally considered a hallmark of gut health, was paradoxically associated with reduced mucosal IgA responses to key ETEC antigens, suggesting a potential immunoregulatory effect. In contrast, increased β-diversity reflecting shifts in community structure was positively associated with systemic IgA responses, emphasizing the role of microbial composition in shaping immune priming. Additionally, specific bacterial taxa were found to either enhance or impair vaccine responses and shedding dynamics. These findings underscore the potential for microbiota-informed strategies to enhance the efficacy of oral vaccines.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eETEC vaccine clinical trial and samples\u003c/h2\u003e\u003cp\u003e \u003cem\u003eETEC vaccine and challenge strain\u003c/em\u003e: ACE527 is a live oral ETEC vaccine composed of three attenuated ETEC strains designed to express a range of ETEC colonization factor antigens (CFA) comprising coli-surface antigens (CS), namely CFA/I, CFA/II (CS1, CS2, CS3), and CFA/IV (CS5, CS6) and the B-subunit of heat-labile enterotoxin (LTB)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated that the ACE527 vaccine is safe and can elicit significant immune responses to key ETEC antigens\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e H10407 is a wild-type virulent ETEC, serotype O78:H11 that produces both heat-labile (LT) and heat-stable (ST) toxins and expresses colonization factor CFA/I \u003csup\u003e46,48\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eACE527 vaccine clinical trial design and samples\u003c/h2\u003e\u003cp\u003eAs reported previously, in a placebo-controlled, double-blind, phase 2b study, 70 healthy American volunteers were enrolled and randomized to receive either the placebo [CeraVacx Buffer (Cera Products, Columbia, MD)] or two doses of ACE527 in CeraVax (each dose approximately 10\u003csup\u003e11\u003c/sup\u003e CFU of ACE527), 3 weeks apart\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Four weeks after their second dose, a total of 27 ACE527 recipients and 27 placebo recipients were challenged with 2 × 10\u003csup\u003e7\u003c/sup\u003e CFU of H10407 virulent ETEC strain in the inpatient unit of the Centre for Immunization Research (CIR), JHU (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The vaccine was well tolerated and induced robust immune responses to key antigens. Although only a 27% reduction was observed in the primary endpoint, moderate-to-severe diarrhea (MSD), the vaccine significantly reduced intestinal colonization by the challenge strain, as measured by quantitative fecal culture 2 days after challenge, indicating the induction of a functional immune response to the CFA/I antigen. In a post-hoc analysis of additional exploratory diarrhea endpoints, the vaccine protected (41% p = 0.03) against severe diarrhea based on stool volume (\u0026gt; 800 grams of grade 3–5 stools over the inpatient surveillance period). Of note, a later phase 2b trial with three doses of lyophilized ACE527 (∼3 × 10\u003csup\u003e9\u003c/sup\u003e of each strain per dose) administered orally with dmLT adjuvant increased the PE to 65.9% (95% confidence interval [CI] 5.4–87.7, p = 0.003)\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eSamples and data analyzed in the present study\u003c/h2\u003e\u003cp\u003eIn the present study, since our primary aim was to elicit the association between microbiota with immune responses after vaccination and challenge, we focused only on the ACE527 vaccinees. Samples and data were used from the 27 volunteers who completed two doses of the ACE527 vaccine and were challenged with ETEC H10407 as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. For sequencing and analysis of the gut microbiome, stool samples from 27 volunteers at V0 (before the first vaccine dose) and C-1 (day before challenge) were used. Specific microbial taxa associations with immune responses between V0 to V21 (day of the second dose), V0 to V28 (7 days after the second dose) and V0 to V31 (10 days after the second dose) were determined through statistical analysis mentioned in later sections. Stool samples were collected up to three times daily after challenge to monitor shedding of the H10407 challenge strain, with quantitative cultures performed for ACE527 recipients on days 2 and 4 to measure bacterial load as CFU/g stool. In ACE527 vaccinees, a significant reduction was seen in the level of shedding of H10407 in stool on day 2 after challenge, whereas by day 4 their level of shedding approximated that in placebo recipients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Taxon association post-challenge with outcome (MSD), maximum shedding, and length of shedding of challenge strain were determined statistically as described later.\u003c/p\u003e\u003ch2\u003eVaccine-induced antibody titers data\u003c/h2\u003e\u003cp\u003eThe immunology methods and data have been reported previously \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The data from the systemic and mucosal antibody responses to potential protective vaccine antigens (LTB, CFA/I), which are also present in the challenge strain, measured by ELISA in serum and ALS were used in this study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Serum IgG and IgA responses were evaluated on V0, V21, and V31. Mucosal immune responses were assessed using the ALS assay (for days V0, V21, and V28), which measures IgA secreted by peripheral blood mononuclear cells (PBMC) circulating to mucosal inductive sites, peaking at 7 to 10 days following oral immunization.\u003c/p\u003e\u003ch2\u003eDNA extraction and sequencing\u003c/h2\u003e\u003cp\u003eGut microbiome composition and functional profiles were ascertained from 300mg of fecal specimen collected on V0 and C-1. Total DNA was extracted using the DNeasy® PowerSoil® Pro Kit with bead beating\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The V3-V4 region of the 16S rRNA gene was amplified by PCR, barcoded and dual-indexed according to established protocols\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. DNA was quantified using an Agilent Technologies 2100 Bioanalyzer before pooling. High-throughput next-generation sequencing of the V3-V4 hypervariable region of the 16S rRNA gene was performed using 319 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) universal primers containing a linker sequence required for Illumina MiSeq 250 bp paired-end sequencing. Negative controls were included (n = 2).\u003c/p\u003e\u003ch2\u003eGenome processing, quality control and in silico functional analyses\u003c/h2\u003e\u003cp\u003eSequenced reads were trimmed from forward and reverse adaptor sequences using cutadapt with default settings\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Reads were quality-filtered, deduplicated, chimeras removed and amplicon sequence variants (ASV) inferred using \u003cem\u003eDADA2\u003c/em\u003e \u003csup\u003e53\u003c/sup\u003e. Forward and reverse reads were merged with 20bp overlap, and taxonomies were assigned using \u003cem\u003eDADA2\u003c/em\u003e against the Silva database \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Contaminants were identified and removed using \u003cem\u003edecontam\u003c/em\u003e \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. ASV abundances were aggregated to the genus level and normalized to relative abundances for statistical analyses. Taxon relative abundances were dichotomized as present or absent if the relative abundance distribution fitted a zero-inflated beta-distribution better than a beta distribution using generalized additive models for location, scale and shape.\u003c/p\u003e\u003cp\u003eWe inferred functional profiles from ASV abundances using PICRUSt2\u003csup\u003e56\u003c/sup\u003e. Predicted bacterial metabolic pathway abundances were derived using the MetaCyc database. Pathway abundance contributions were stratified by ASV and aggregated up to the genus level.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAntibody titers against two ETEC antigens \u003cem\u003eviz\u003c/em\u003e., LTB and CFA/I were transformed into fold changes from V0 to V21, V0 to V28 or V0 to V31, and normalized to a Gaussian distribution prior to analysis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. We determined the association between diarrhea and microbiota α-diversity (inverse Simpson diversity, Shannon diversity, and Pielou’s evenness) by bias reduced simple logistic regression to accommodate data separation due to the small sample size \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The dependent variable was MSD versus mild or no diarrhea. α-diversity indices were log\u003csub\u003e2\u003c/sub\u003e transformed. One model was fit for each index. We investigated β-diversity using Bray-Curtis and Jaccard dissimilarity matrices visualized by non-metric multidimensional scaling (NMDS) and compositional associations with diarrhea were tested by microbiome regression-based kernel association test (MiRKAT)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Finally, we identified taxa associated with diarrhea using the relaxed lasso penalized logistic regression, with 10-fold cross validation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. A multivariable logistic regression model was fitted using the selected taxa as covariates to estimate odds ratios (OR) and 95% confidence intervals (95%CI) using biased reduced logistic regression. Analyses were repeated as described, but replacing maximum shedding, length of shedding, and antibody titer separately, as the dependent variable, and using linear regression. Maximum shedding was log\u003csub\u003e10\u003c/sub\u003e transformed prior to analyses.\u003c/p\u003e\u003cp\u003eIndividual taxa selected by relaxed LASSO regression as predictors of MSD, shedding or antibody fold change were classified as protective if they predicted decreased odds of diarrhea, reduced shedding, or increase antibody titer fold change; harmful if they predicted increased diarrhea, shedding, or decreased antibody fold change; or as having no associations otherwise. Two-sided Fisher’s Exact tests were performed to identify MetaCyc pathways (predicted by PICRUSt2) that differentiated protective or harmful taxa from others, with adjustment for multiple hypothesis testing to preserve the false discovery rate (FDR)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Thirty-six MetaCyc pathways were differentially present between these three groups of taxa after FDR-adjustment. We performed overrepresentation analyses using one-sided Fisher’s Exact tests to determine whether the differentially present pathways were more likely to have particular biological functions defined by MetaCyc pathway types\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eChanges in α-diversity or individual taxa pre- to post-vaccination were determined by Wilcoxon signed-rank sum test or McNemar’s test for continuous or dichotomous microbiome features as appropriate.\u003c/p\u003e\u003cp\u003eStatistical significance was determined at α = 0.05. Taxa present in \u0026lt; 10% of specimens were removed prior to statistical analyses. Relative abundances were fitted to beta and zero-inflated beta distributions using \u003cem\u003egamlss\u003c/em\u003e \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. α-diversity indices were calculated and NMDS was performed using \u003cem\u003evegan\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. MiRKAT was performed using \u003cem\u003eMiRKAT\u003c/em\u003e. \u003cem\u003eglmnet\u003c/em\u003e was used to perform relaxed lasso penalized regression. Biased reduced logistic regression was performed using \u003cem\u003ebrglm2\u003c/em\u003e\u003csup\u003e65\u003c/sup\u003e. Normalization of antibody titer fold changes to a Gaussian distribution was performed using \u003cem\u003ebest-Normalize\u003c/em\u003e. Phylogenic relationships between taxa were visualized using \u003cem\u003eggtree\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. All analyses were conducted using R version 4.2.2.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eNone to Declare\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eThe study protocol, stool sample collection, microbiome sequencing, and associated laboratory analyses, was reviewed and approved by the independent Institutional Review Board of Johns Hopkins University [IRB 20100221]. Written informed consent was obtained from all participants prior to enrollment. Stool specimens were collected under approved clinical protocols, anonymized prior to laboratory processing, and handled in compliance with institutional biosafety regulations. Microbiome sequencing and genomic analyses were performed on de-identified samples in accordance with institutional data governance policies and ethical guidelines for genomic research. The vaccine trial was registered under \u003cb\u003eClinicalTrials.gov\u003c/b\u003e ID: NCT01060748.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health (NIH), R01AI153399 (SC), and partially funded by R56AI168316 (SC). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIAID. The clinical trial was supported by the PATH GAT.1371-05689-CTA (00374518-CTA) (CDH).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEthan Gough: Formal analysis, Investigation, Software, Visualization, Writing \u0026ndash; original draft ; Soumya Basu: Formal analysis, Investigation, Visualization, Writing \u0026ndash; original draft ; Jessica Brubaker: Investigation (Immunology) ; Barbara DeNearing: Investigation (Clinical Trial) ; David A Sack: Investigation (Clinical Trial); Writing \u0026ndash; review \u0026amp; editing ; A Louis Bourgeois: Investigation (Clinical Trial); Writing \u0026ndash; review \u0026amp; editing ; Richard Walker: Investigation (Clinical Trial); Writing \u0026ndash; review \u0026amp; editing; Clayton D. Harro: Investigation (Clinical Trial) ; Subhra Chakraborty: Conceptualization, Project administration, Fund acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the volunteers who participated in the clinical trial. The contributions of members of the clinic and Enteric Vaccine laboratory teams at the JHU Bloomberg School of Public Health are gratefully acknowledged.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset supporting the findings of this study are available in the \u0026lsquo;Supplementary Files\u0026rsquo; accompanying this article. The corresponding author is working with the Johns Hopkins Institutional Review Board (IRB) to determine the extent to which further anonymized individual-level data from human volunteers may be deposited in an appropriate open-access repository in compliance with ethical and regulatory requirements. Pending IRB approval, the data will be made available through a recognized public repository with a persistent identifier.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, Brewer TG, et al. Morbidity and mortality due to Shigella and enterotoxigenic Escherichia coli diarrhoea: the Global Burden of Disease Study 1990\u0026ndash;2016. Lancet Infect Dis. 2018;18(11):1229\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, Pulcini C, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis. 2018;18(3):318\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Immunization, vaccines and biologicals: Enterotoxigenic Escherichia coli (ETEC). Geneva: WHO; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePajuelo MJ, Noazin S, Cabrera L, Toledo A, Velagic M, Arias L, Ochoa M, et al. Epidemiology of enterotoxigenic Escherichia coli and impact on the growth of children in the first two years of life in Lima, Peru. Front Public Health. 2024;12:1332319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2024.1332319\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1332319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Tan P, Zhao Y, Ma X. Enterotoxigenic Escherichia coli: intestinal pathogenesis mechanisms and colonization resistance by gut microbiota. Gut Microbes. 2022;14(1):2055943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19490976.2022.2055943\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2022.2055943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynn DJ, Benson SC, Lynn MA, Pulendran B. Modulation of immune responses to vaccination by the microbiota: implications and potential mechanisms. Nat Rev Immunol. 2022;22(1):33\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41577-021-00554-7\u003c/span\u003e\u003cspan address=\"10.1038/s41577-021-00554-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunrinola GA, Oyewale JO, Oshamika OO, Olasehinde GI. The human microbiome and its impacts on health. Int J Microbiol. 2020;2020:8045646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/8045646\u003c/span\u003e\u003cspan address=\"10.1155/2020/8045646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelber-Hnatiw S, Rukundo B, Ahmadi M, Akoubi H, Al-Bizri H, Aliu AF, Ambeaghen TU, et al. Human gut microbiota: toward an ecology of disease. Front Microbiol. 2017;8:1265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2017.01265\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2017.01265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossouw C, Ryan FJ, Lynn DJ. The role of the gut microbiota in regulating responses to vaccination: current knowledge and future directions. FEBS J. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/febs.17241\u003c/span\u003e\u003cspan address=\"10.1111/febs.17241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwong KW-Y, Xin Y, Lai NC-Y, Sung JC-C, Wu K-C, Hamied YK, Sze ET-P, Lam DM-K. Oral vaccines: a better future of immunization. Vaccines. 2023;11(7):1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/vaccines11071232\u003c/span\u003e\u003cspan address=\"10.3390/vaccines11071232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindsay BR, Chakraborty S, Harro C, Li S, Nataro JP, Sommerfelt H, Sack DA, Stine OC. Quantitative PCR and culture evaluation for enterotoxigenic Escherichia coli (ETEC)-associated diarrhea in volunteers. FEMS Microbiol Lett. 2014;352(1):25\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1574-6968.12362\u003c/span\u003e\u003cspan address=\"10.1111/1574-6968.12362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B, Wang J, Li L. Recent five-year progress in the impact of gut microbiota on vaccination and possible mechanisms. Gut Pathogens. 2023;15:27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiabattini A, Olivieri R, Lazzeri E, Medaglini D. Role of the microbiota in the modulation of vaccine immune responses. Front Microbiol. 2019;10:1305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay S, Narayanan A, Vesterbacka J, Blennow O, Chen P, Gao Y, Gabarrini G, et al. Impact of the gut microbiome on immunological responses to COVID-19 vaccination. npj Biofilms Microbiomes. 2023;9:104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArdura-Garcia C, Curtis N, Zimmermann P. Systematic review of the impact of intestinal microbiota on vaccine responses. npj Vaccines. 2024;9:254.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePop M, Paulson JN, Chakraborty S, Astrovskaya I, Lindsay BR, Li S, Bravo HC, et al. Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli. BMC Genomics. 2016;17:440.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Xu X, Cai L, Cai X. Dysbiosis of the gut microbiome in elderly patients with hepatocellular carcinoma. Sci Rep. 2023;13:7797.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGophna U, Konikoff T, Nielsen HB. Oscillospira and related bacteria: from metagenomic species to metabolic features. Environ Microbiol. 2017;19(3):835\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion. Curr Opin Microbiol. 2023;75:102363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19(1):29\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng L, Li Z-R, Green RS, Holzman IR, Lin J. Butyrate enhances the intestinal barrier via activation of AMP-activated protein kinase. J Nutr. 2009;139(9):1619\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026iacute;os-Covi\u0026aacute;n D, Ruas-Madiedo P, Margolles A, Gueimonde M, De Los Reyes-Gavil\u0026aacute;n CG, Salazar N. Intestinal short-chain fatty acids and human health. Front Microbiol. 2016;7:185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, Cheng G, et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science. 2011;331(6015):337\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuda MN, Lewis Z, Kalanetra KM, Rashid M, Ahmad SM, Raqib R, Qadri F, et al. Stool microbiota and vaccine responses of infants. Pediatrics. 2014;134(2):e362\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou P, Manoil D, Belibasakis GN, Kotsakis GA. Veillonellae: beyond bridging species in oral biofilm ecology. Front Oral Health. 2021;2:774115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandic-Mulec I, Stefanic P, van Elsas JD. (2015) Ecology of Bacillaceae. Microbiol Spectr 3(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusling M, Karim A, Kaye A, Lee CMJ, Wegman-Points L, Mathis V, Lampeter T, Yuan L-L. Influences of Ruminococcus bromii and Peptostreptococcaceae on voluntary exercise behavior. Front Microbiomes. 2024;3:1389103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiippala K, Kainulainen V, Kalliom\u0026auml;ki M, Arkkila P, Satokari R. Commensalism of Sutterella spp. Front Microbiol. 2016;7:1706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaakoush NO. Sutterella species, IgA-degrading bacteria in ulcerative colitis. Trends Microbiol. 2020;28(7):519\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Yin F, Sun W, Li R, Guo Z, Wang Y, Zhang Y, Sun C, Sun D. Causal relationships between gut microbiota and infectious diseases. Front Immunol. 2024;15:1304973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLowe SE, Jain MK, Zeikus JG. Biology and ecology of anaerobic bacteria adapted to environmental stress. Microbiol Rev. 1993;57(2):451\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen Y, Fan N, Ma S-X, Cheng X, Yang X, Wang G. Gut microbiota dysbiosis: pathogenesis, diseases, prevention, and therapy. MedComm. 2025;6(5):e70168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Hong G, Li Y, Yang P, Cheng M, Zhang L, Li Y, et al. Site-specific microbial patterns in diarrhea-predominant IBS. Microbiol Spectr. 2021;9(3):e01255\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaetz CRH, Whitfield C. Lipopolysaccharide endotoxins. Annu Rev Biochem. 2002;71:635\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitfield C, Trent MS. Biosynthesis and export of bacterial lipopolysaccharides. Annu Rev Biochem. 2014;83:99\u0026ndash;128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLerouge I, Vanderleyden J. O-antigen structural variation: mechanisms and possible roles in animal/plant\u0026ndash;microbe interactions. FEMS Microbiol Rev. 2002;26(1):17\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuura M. Structural modifications of bacterial lipopolysaccharide that facilitate Gram-negative bacteria evasion of host innate immunity. Front Immunol. 2013;4:109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy DG, Chen J, Mamane V, Ma EH, Muhire BM, Sheldon RD, Shorstova T, et al. Methionine metabolism shapes T helper cell responses. Cell Metabol. 2020;31(2):250\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Han Z, Liu B, Yu D, Sun J, Ge L, Tang W, Liu S. Gut microbiota contributes to methionine metabolism in host. Front Microbiol. 2022;13:1065668.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander C, Rietschel ET. Bacterial lipopolysaccharides and innate immunity. J Endotoxin Res. 2001;7(3):167\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePulendran B, Arunachalam PS, O\u0026rsquo;Hagan DT. Emerging concepts in the science of vaccine adjuvants. Nat Rev Drug Discov. 2021;20(6):454\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz JH, Le Bourhis L, Sellge G, et al. Nod1-mediated innate immune recognition of peptidoglycan contributes to the onset of adaptive immunity. Immunity. 2007;26(4):445\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke TB, Davis KM, Lysenko ES, et al. Recognition of peptidoglycan from the microbiota by Nod1 enhances systemic innate immunity. Nat Med. 2010;16(2):228\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11(8):519\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRound JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009;9(5):313\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarsley MJ, Chakraborty S, DeNearing B, Sack DA, Feller A, Buchwaldt C, Bourgeois AL, et al. The oral, live attenuated enterotoxigenic Escherichia coli vaccine ACE527 reduces the incidence and severity of diarrhea in a human challenge model. Clin Vaccine Immunol. 2012;19(12):1921\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/CVI.00364-12\u003c/span\u003e\u003cspan address=\"10.1128/CVI.00364-12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarro C, Chakraborty S, Sack D, et al. A combination vaccine consisting of three live attenuated enterotoxigenic Escherichia coli strains expressing colonization factors and heat-labile toxin subunit B is well tolerated and immunogenic. Clin Vaccine Immunol. 2011;18:2118\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty S, Randall A, Vickers TJ, Molina D, Harro CD, DeNearing B, Brubaker J, et al. Interrogation of a live-attenuated enterotoxigenic Escherichia coli vaccine highlights features unique to wild-type infection. npj Vaccines. 2019;4:37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41541-019-0131-7\u003c/span\u003e\u003cspan address=\"10.1038/s41541-019-0131-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarro C, Bourgeois AL, Sack D, Walker R, DeNearing B, Brubaker J, Maier N, Fix A, Dally L, Chakraborty S, Clements JD, Saunders I, Darsley MJ. Live attenuated enterotoxigenic Escherichia coli (ETEC) vaccine with dmLT adjuvant protects human volunteers against virulent experimental ETEC challenge. Vaccine. 2019;37(14):1978\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.vaccine.2019.02.025\u003c/span\u003e\u003cspan address=\"10.1016/j.vaccine.2019.02.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarelli M, Griggio F, Mingoia M, Garofalo C, Milanović V, Pozzato N, Leoni F, et al. Detecting carbapenemases in animal and food samples by droplet digital PCR. Antibiotics. 2022;11(12):1696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antibiotics11121696\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics11121696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, Ravel J. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome. 2014;2:6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2049-2618-2-6\u003c/span\u003e\u003cspan address=\"10.1186/2049-2618-2-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGl\u0026ouml;ckner FO. The SILVA database project: an ELIXIR core data resource for high-quality ribosomal RNA sequences. Biodivers Inform Sci Stand. 2019;3:e36125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41587-020-0548-6\u003c/span\u003e\u003cspan address=\"10.1038/s41587-020-0548-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith B, Wilson JB. A consumer\u0026rsquo;s guide to evenness indices. Oikos. 1996;76:70\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson N, Zhao N, Zhan X, Koh H, Fu W, Chen J, Li H, Wu MC, Plantinga AM. MiRKAT: kernel machine regression-based global association tests for the microbiome. Bioinformatics. 2021;37(11):1595\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J Roy Stat Soc B. 2011;73(3):273\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B. 1995;57(1):289\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieder C, Frainay C, Poupin N, Rodr\u0026iacute;guez-Mier P, Vinson F, Cooke J, Lai RPJ, et al. Pathway analysis in metabolomics: recommendations for the use of over-representation analysis. PLoS Comput Biol. 2021;17(9):e1009105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46(D1):D633\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStasinopoulos DM, Rigby RA. Generalized additive models for location, scale and shape (GAMLSS) in R. J Stat Softw. 2008;23:1\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolland SM. Non-metric multidimensional scaling (NMDS). R Documentation; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosmidis I. (2017) Multinomial logistic regression using brglm2. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package=brglm2\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/package=brglm2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. ggtree: an R package for visualization and annotation of phylogenetic trees. Methods Ecol Evol. 2017;8(1):28\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"gut-pathogens","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gutp","sideBox":"Learn more about [Gut Pathogens](http://gutpathogens.biomedcentral.com/)","snPcode":"13099","submissionUrl":"https://submission.nature.com/new-submission/13099/3","title":"Gut Pathogens","twitterHandle":"@GutPathogens","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ETEC, Diarrhea, vaccine, microbiome, immune response, sequencing","lastPublishedDoi":"10.21203/rs.3.rs-9284363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9284363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnterotoxigenic Escherichia coli (ETEC) remains a major cause of diarrheal morbidity with no licensed vaccines. Role of gut microbiota in vaccine immunogenicity and protection was investigated using 16S rRNA sequencing from the stool samples of 27 volunteers receiving two doses of the live attenuated oral ETEC vaccine ACE527 followed by virulent ETEC H10407 challenge. Systemic and mucosal IgG and IgA responses to heat-labile toxin-B (LTB) and colonization-factor-antigen-I (CFA/I) were quantified by ELISA in serum and antibody-in-lymphocyte-supernatant (ALS). Microbiome α-diversity, β-diversity, and taxa\u0026ndash;immune associations were evaluated using regression models, MiRKAT, and relaxed LASSO. Vaccination increased (~\u0026thinsp;25\u0026ndash;30%) Eubacterium_brachy_group, Family_XIII_AD3011 and Actinomyces. Higher α-diversity (inverse-Simpson) was associated with reduced ALS anti-LTB and CFA/I IgA responses, whereas β-diversity correlated with increased serum anti-CFA/I IgA. Members of Anaerovoraceae, Peptostreptococcaceae, Oscillospiraceae, and Veillonellaceae enhanced immune responses and protection against severe diarrhea and ETEC colonization, while Ruminococcaceae, Sutterellaceae, Coriobacteria, Clostridia, and Actinobacteria showed antagonistic associations.\u003c/p\u003e","manuscriptTitle":"Influence of Gut Microbiota on Immune Responses and Protection in Volunteers Receiving the Live Attenuated Oral ETEC Vaccine ACE257 followed by Virulent ETEC H10407 Challenge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 07:42:09","doi":"10.21203/rs.3.rs-9284363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T02:51:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T01:37:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221191869107609325778928458796167493482","date":"2026-05-05T19:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248505574324900371642548154333757113536","date":"2026-05-03T16:32:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134623119427002286789138890032965200600","date":"2026-05-01T14:52:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80658353282350316590698070620335092093","date":"2026-05-01T13:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T10:09:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T17:50:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T17:50:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Gut Pathogens","date":"2026-03-31T21:19:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"gut-pathogens","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gutp","sideBox":"Learn more about [Gut Pathogens](http://gutpathogens.biomedcentral.com/)","snPcode":"13099","submissionUrl":"https://submission.nature.com/new-submission/13099/3","title":"Gut Pathogens","twitterHandle":"@GutPathogens","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6aea0591-a4e7-455a-aecc-d24c1a59d700","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T02:51:46+00:00","index":38,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T01:37:23+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"221191869107609325778928458796167493482","date":"2026-05-05T19:12:54+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"248505574324900371642548154333757113536","date":"2026-05-03T16:32:09+00:00","index":35,"fulltext":""},{"type":"reviewerAgreed","content":"134623119427002286789138890032965200600","date":"2026-05-01T14:52:59+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"80658353282350316590698070620335092093","date":"2026-05-01T13:26:39+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T07:42:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 07:42:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9284363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9284363","identity":"rs-9284363","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}