CoChat
Sign Up
Cited by 572Gut microbiota and healthImmune responses and vaccinationsImmune Response and Inflammation

Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans

Open access
Hagan, Thomas, Cortese, Mario, Rouphael, Nadine, Boudreau, Carolyn, Linde, Caitlin, Maddur, Mohan S., Das, Jishnu, Wang, Hong +21 more·2019·Cell
DOI 10.1016/j.cell.2019.08.010PMC PMC6750738
Open this paper in CoChat — free accountOpen PDF

Abstract

Emerging evidence indicates a central role for the microbiome in immunity. However, causal evidence in humans is sparse. Here, we administered broad-spectrum antibiotics to healthy adults prior and subsequent to seasonal influenza vaccination. Despite a 10,000-fold reduction in gut bacterial load and long-lasting diminution in bacterial diversity, antibody responses were not significantly affected. However, in a second trial of subjects with low pre-existing antibody titers, there was significant impairment in H1N1-specific neutralization and binding IgG1 and IgA responses. In addition, in both studies antibiotics treatment resulted in (1) enhanced inflammatory signatures (including AP-1/NR4A expression), observed previously in the elderly, and increased dendritic cell activation; (2) divergent metabolic trajectories, with a 1,000-fold reduction in serum secondary bile acids, which was highly correlated with AP-1/NR4A signaling and inflammasome activation. Multi-omics integration revealed significant associations between bacterial species and metabolic phenotypes, highlighting a key role for the microbiome in modulating human immunity.

AI snapshot

Written from the paper’s full text.

TL;DR

In two human influenza-vaccination trials, broad-spectrum antibiotics sharply depleted and durably altered the gut microbiome, but overall antibody responses were largely preserved in subjects with pre-existing immunity. In subjects with low baseline influenza titers, antibiotic treatment impaired H1N1-specific neutralization and IgG1/IgA responses, while also inducing inflammatory transcriptional and metabolic changes linked to loss of secondary bile acids.

Key findings

  • Antibiotics caused a roughly 10,000-fold drop in gut bacterial load and reduced bacterial diversity, with incomplete recovery of richness and community composition at six months.
  • In phase 1, antibody responses to trivalent influenza vaccine were not significantly changed; in phase 2, subjects with low pre-existing titers showed reduced H1N1 A/California neutralization at days 90 and 180 and lower H1N1-specific IgG1 and IgA by ELISA, Luminex, and SPR.
  • Antibiotics alone increased inflammatory blood transcriptional modules, including AP-1/NR4A signaling, and raised CD80+ CD16+ mDC and CD123++ pDC frequencies.
  • Serum secondary bile acids fell about 1,000-fold, with lithocholic acid showing the strongest inverse association with inflammatory modules and inflammasome/IL-1B-related signals; multi-omics network analysis linked bacterial taxa, especially Lachnospiraceae and Ruminococcaceae, to metabolic and antibody phenotypes.

Methods

  • Two randomized controlled influenza-vaccination studies in healthy adults: phase 1 enrolled 22 subjects (11 antibiotics, 11 control) during 2014–2015; phase 2 enrolled 11 subjects (5 antibiotics, 6 control) during 2015–2016, selected for low pre-vaccination microneutralization titers and no recent influenza vaccination.
  • Antibiotic-treated participants received a five-day oral cocktail of neomycin, vancomycin, and metronidazole starting three days before vaccination and ending one day after; all participants received trivalent inactivated influenza vaccine.
  • Analyses included 16S rRNA sequencing and qPCR, fecal flagellin/LPS assays, microneutralization, ELISA and Luminex antibody measurements, surface plasmon resonance, flow cytometry, microarray transcriptomics, untargeted and targeted LC-MS metabolomics, and multiscale multifactorial response network integration.

Limitations

  • The paper states that the reason the antibody effect was limited to H1N1 rather than the other vaccine strains is unclear, and it notes that two antibiotic-treated subjects appeared to resist microbiome disturbance, likely due to questionable compliance.
  • The paper states that future work should extend these analyses to infants, whose immature immune systems may be more susceptible to microbiome perturbation.
  • Evident from the text: the phase 2 antibiotic arm was very small (n=5), the study population was healthy adults aged 18–40/45, and the antibiotic exposure was short, so generalizability to other populations, longer perturbations, and clinical vaccine outcomes is uncertain.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.cell.2019.08.010

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 24 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    In phase 2, antibiotic-treated subjects had significantly reduced vaccine-induced IgG1 antibodies specific to H1N1 A/California on day 7 by ELISA.

    “Remarkably, we also noticed that the antibiotics-treated subjects in phase 2 exhibited significantly reduced concentrations of vaccine-induced IgG1 antibodies specific to the H1N1 A/California strain (Figure 2C, right panel) on day 7, as measured by ELISA.”
    Quote in the paperSubstantial claim
  • Findingown data

    In phase 2, H1N1-specific IgA antibody responses were lower in the antibiotic group before and after vaccination by SPR.

    “Using surface plasmon resonance (SPR), we detected lower H1N1-specific IgA antibody responses in the phase 2 antibiotic group before and after vaccination (Figure 2G).”
    Quote in the paperSubstantial claim
  • Findingown data

    In phase 1, the magnitude of IgG1 response was not strongly affected by antibiotic treatment.

    “This was in contrast with phase 1, where the magnitude of IgG1 response was not strongly affected by antibiotic treatment (Figure 2C, left panel).”
    Quote in the paperNot a substantial claim
  • Findingown data

    By day 7, Enterobacteriaceae remained elevated in several antibiotic-treated subjects, though at lower proportions.

    “By day 7, Enterobacteriaceae remained elevated in several subjects, albeit in lower proportions.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Enterobacteriaceae dominated the gut microbiota of antibiotic-treated phase 1 subjects on days 0, 1, and 3.

    “Enterobacteriaceae dominated the gut microbiota of treated subjects at days 0, 1, and 3”
    Quote in the paperNot a substantial claim
  • May combine two claims. 39
  • Classified as probably not a substantial claim. 24

  • May combine two claims.

    Findingown data

    The IgA differences were confirmed by Luminex and not observed in phase 1.

    “These differences were again confirmed by Luminex (Figure 2H-I) and not observed in the phase 1 antibiotic group (Figure S2D).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    On day 0, the antibiotic cohort's samples diverged from controls in beta diversity, with the largest between-group distance on day 3.

    “On day 0, samples from the antibiotic cohort had diverged from controls, with the largest distance between the two groups measured on day 3.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Alpha diversity measures were heavily compromised after antibiotics, with the smallest number of different OTUs on day 3.

    “Both measures were heavily compromised after the use of antibiotics, with the smallest number of different OTUs recorded on day 3 (Figure 1G).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    In phase 2, seroconversion rates were much higher than phase 1 and reached 100% in both control and antibiotics groups for two of three vaccine strains.

    “Predictably, given the lower baseline titers, seroconversion rates were much higher in the second cohort and reached 100% in both control and antibiotics groups for two out of three vaccine strains (Figure S2B).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Fecal flagellin and LPS concentrations were significantly reduced at early time points after antibiotic administration, with kinetics similar to the bacterial load reduction.

    “we found that fecal concentrations of both flagellin and LPS were also significantly reduced at early time points after antibiotic administration with similar kinetics (Figure 1C, D).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In phase 1, total bacterial content in stool reached its lowest point on day 1 and then recovered to baseline between days 7 and 30.

    “On day 1 (the last day of antibiotics) the total bacterial content in the antibiotic administered cohort reached its nadir, before gradually recovering to baseline levels between days 7 and 30.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Antibiotic treatment had no significant effect on IgG2 or IgG3 antibody concentrations.

    “we asked whether the concentration of other IgG subclasses would be affected by antibiotic treatment, but found no significant effect on IgG2 (Figure S2C) or IgG3 antibodies (data not shown).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Antibiotics caused striking family-level changes in microbial community composition, with relative abundances significantly altered for up to one month after treatment.

    “Family-level analysis revealed striking changes in microbial community composition, with relative abundances significantly altered up to one month post antibiotics use.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In phase 2, antibiotic-treated subjects had impaired H1N1 A/California-specific neutralization on days 90 and 180 post-vaccination, while no differences were observed for the other two influenza strains.

    “However, in this second experiment we also found an impairment in the H1N1 A/California-specific neutralization on day 90 and day 180 post vaccination for the subjects in the antibiotic group, while no differences were observed for the other two influenza strains (Figure 2B).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    By day 30, Lachnospiraceae, Enterobacteriaceae, and Ruminococcaceae remained perturbed, but returned to baseline by day 90.

    “By day 30, relative levels of Lachnospiraceae, Enterobacteriaceae and Ruminococcaceae remained perturbed but eventually returned to baseline by day 90 (Figure 1E and Table S1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    These AP-1/NR4A modules remained elevated in antibiotic-treated subjects on day 1 post-vaccination, whereas they were downregulated in control subjects following vaccination.

    “These modules remained elevated in antibiotics-treated subjects on day 1 post-vaccination, whereas they were downregulated in control subjects following vaccination (Figure 4C).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    GSEA between screening and day 0 in antibiotic-treated subjects identified expression changes in a substantial number of modules following antibiotics use, including increases in inflammatory signaling and dendritic cell activation, and decreases in B cell and cell cycle-related expression, largely consistent in both phases.

    “We identified expression changes in a substantial number of modules following antibiotics use, including increases in inflammatory signaling and activation of dendritic cells, and decreases in B cell and cell cycle-related expression, which were largely consistent in both phases of the study (data not shown).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Vaccine-induced transcriptional response kinetics were the same in both groups: changes peaked at day 1, decreased by day 3, and had a second increase at day 7.

    “The response kinetics were the same in both groups: vaccine-induced changes in gene expression peaked at day 1, then decreased by day 3, and had a second increase at day 7.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Two phase 1 antibiotic-treated individuals showed little change in their microbiota numbers or composition.

    “although two individuals in this cohort appeared to resist disturbance and did not show evident signs of alteration in numbers or composition of their microbiota overall (Figure 1E)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Compared with young and elderly vaccinees from 2010-2011, young subjects showed decreased expression of these modules on day 1 like controls, while antibiotic-treated subjects behaved similarly to elderly vaccinees, who exhibited upregulation post-vaccination.

    “we observed that while young subjects showed decreased expression of these modules on day 1 as in the control subjects, antibiotics-treated subjects behaved similarly to elderly vaccinees, who exhibited upregulation of these pathways postvaccination.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Nakaya et al., 2015 source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Streptococcaceae were more abundant at early time points, while Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, and Veillonellaceae diminished between days 0 and 3.

    “Streptococcaceae were also more abundant at early time points, while Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, and Veillonellaceae diminished between days 0 and 3.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In both studies, antibiotics treatment resulted in divergent metabolic trajectories, with a 1000-fold reduction in serum secondary bile acids that was highly correlated with AP-1/NR4A signaling and inflammasome activation.

    “Divergent metabolic trajectories, with a 1000-fold reduction in serum secondary bile acids which was highly correlated with AP-1/NR4A signaling and inflammasome activation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    The antibiotic effect on antibody response appeared for H1N1 only, not for the other vaccine strains.

    “this effect was only observed with the response to H1N1 and not against the other strains.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Two metabolic clusters, M26 and M58, were strongly linked to the IgG1 response and were highly enriched for fatty acid metabolism.

    “among the clusters associated uniquely with IgG1 response were two clusters highly enriched in fatty acid metabolism (M26 and M58)”
    • Also stated in: § Discussion · plain
      “the IgG1 response was significantly associated with two metabolic clusters highly enriched in fatty acid metabolism”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    LC-MS analysis showed elevated primary bile acids and a dramatic reduction in secondary bile acid levels in serum of antibiotic-treated subjects.

    “Indeed, there was an elevation of primary and dramatic reduction in secondary bile acid levels in the serum of antibiotics-treated subjects (Figure 5C).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In both studies, antibiotics treatment resulted in enhanced inflammatory signatures including AP-1/NR4A expression and increased dendritic cell activation.

    “In addition, in both studies antibiotics treatment resulted in: (i) Enhanced inflammatory signatures (including AP-1/NR4A expression), observed previously in the elderly, and increased dendritic cell activation;”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Antibiotic-treated subjects had similar magnitude of metabolic response at day 1, but maintained a large shift beyond controls and still differed from day 0 at day 7 post-vaccination.

    “Antibiotics-treated subjects had similar magnitude of response at day 1, however they maintained a large shift in their metabolome beyond control subjects, and still exhibited considerable differences compared to day 0 at day 7 post-vaccination.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Bacteria dominated the features shared by both parameters; three bacterial clusters were positively linked to both LCA and H1N1-specific IgG1 abundance.

    “commonly associated features were dominated by bacteria, with three bacterial clusters positively associated with both parameters.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    LCA had the strongest association with inflammatory responses, including AP-1 signaling module M35.0, which was upregulated post-vaccination in antibiotic-treated subjects but downregulated in controls.

    “had the strongest association with inflammatory responses, including modules involved in AP-1 signaling such as M35.0, which was upregulated post-vaccination in antibiotics-treated subjects (Figure 6C) but downregulated in control subjects (Figure 6D).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The control group had a modest increase in differentially expressed genes at all time points relative to the antibiotics group, potentially due to a slightly higher number of subjects (17 versus 15).

    “The control group had a modest increase in differentially expressed genes at all time points relative to the antibiotics group, potentially due to a slightly higher number of subjects (17 versus 15).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In phase 1, participants were randomized to receive either a five-day oral regimen containing neomycin, vancomycin, and metronidazole or control.

    “We randomized 11 subjects to a five-day broad-spectrum antibiotic regimen consisting of an oral cocktail of neomycin, vancomycin, and metronidazole”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The lack of microbiome disturbance in those two phase 1 subjects was likely due to questionable compliance with antibiotics.

    “likely related to questionable compliance with antibiotics.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    At day 180, the microbial composition of the antibiotic and control groups still showed substantial dissimilarity, indicating only partial recovery of the original composition in the antibiotic group.

    “Interestingly, at day 180 the microbial composition of the two groups still showed substantial dissimilarity, suggesting only partial recovery of the original microbial composition in the antibiotic group.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationown data

    Overall seroconversion percentage was generally low, possibly due to high baseline titers in some subjects.

    “However, we noticed a generally low percentage of seroconversion overall, possibly due to high baseline titers observed for some subjects.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    In phase 1, both cohorts had similar microneutralization titers for each of the three influenza strains at all time points, suggesting antibiotics did not significantly alter neutralization of homologous vaccine strains in vitro.

    “We found that both cohorts had similar MN titers for each of the three influenza strains included in the vaccine at all time points, suggesting that antibiotics-induced perturbation of the gut ecosystem did not significantly alter the ability of TIV-specific antibodies to neutralize homologous vaccine strains in vitro (Figure 2A).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Both dendritic cell populations were elevated throughout antibiotics (days 0 and 1) and began returning to control levels after cessation, suggesting the changes were driven by antibiotics-induced gut microbiome perturbations.

    “Both populations were elevated in antibiotics-treated subjects throughout the course of antibiotics (days 0 and 1) and began to return to control levels following the cessation of antibiotics, further suggesting that these changes were driven by the antibiotics-induced perturbations in the gut microbiome.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In phase 2, five participants were randomized to receive the same antibiotic regimen as in phase 1.

    “Five of these individuals were randomized to receive the same antibiotics regimen as in phase 1.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The two antibiotic-treated subjects whose metabolic trajectories grouped with controls were the same subjects who displayed little change in their microbiome, suggesting poor compliance with the antibiotic regimen.

    “Importantly, we note that the two antibiotics-treated subjects whose trajectories grouped with control subjects were the same subjects who displayed little change in their microbiome (Figure 1), suggesting poor compliance with the antibiotic regimen.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Aging alters microbiome composition, reducing Firmicutes and overall diversity.

    “The composition of the microbiome is known to change with age, with decreases in the phylum Firmicutes and overall diversity (Salazar et al., 2017).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Salazar et al., 2017 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In mice, gut permeability rises with age.

    “gut permeability has been shown to increase with age in mice (Thevaranjan et al., 2017).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Thevaranjan et al., 2017 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Serum concentrations of flagellin and anti-LPS antibodies did not differ between control and antibiotic-treated subjects, arguing against translocation of microbial products to the periphery.

    “We thus measured the concentrations of flagellin and anti-LPS antibodies in the serum (Figure S1D-F), but could find no differences between control and antibiotics-treated subjects, arguing against translocation of microbial products to the periphery.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Multi-omics integration revealed significant associations between bacterial species and metabolic phenotypes, highlighting a key role for the microbiome in modulating human immunity.

    “Multi-omics integration revealed significant associations between bacterial species and metabolic phenotypes, highlighting a key role for the microbiome in modulating human immunity.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    These findings support the idea that adult immune responses depend heavily on prior immune history and withstand short-lived microbiome shifts.

    “The results of this study are consistent with the concept of immune responses in adults being largely determined by immune history and resilient to transient changes in the microbiome.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Elderly gut microbiota have a reduced capacity to make other immunomodulatory metabolites, including short-chain fatty acids.

    “there is evidence that the gut microbiota of elderly subjects are less capable of producing other immunomodulatory metabolites, such as short-chain fatty acids (Rampelli et al., 2013).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Rampelli et al., 2013

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Healthy adult participants were assigned randomly to receive antibiotics or control.

    “we enrolled a total of 33 healthy adults who were randomized into antibiotics-treated (n=16) and control (n=17) groups.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Environmental influences, especially previous immune exposure, are the main source of immune variation among humans, not genetic differences.

    “Previous work has shown that immune variation within the human population is driven primarily by environmental factors such as prior immune exposure, rather than genetic factors (Brodin et al., 2015).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Brodin et al., 2015 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fatty acid metabolism is increasingly recognized as an important controller of immune responses.

    “which is emerging as a key regulator of immune function (Ganeshan and Chawla, 2014; Sinclair et al., 2017).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Ganeshan and Chawla, 2014

      Not checked in the free run.

    • cites Sinclair et al., 2017 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Each serum sample was assayed three times for influenza microneutralization, and the geometric mean was analyzed.

    “All sera were tested in triplicate, and the geometric mean value was used for analysis.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The microbiome can influence immunity in multiple ways, including indirect control of systemic metabolite availability in addition to direct gut immune cell interactions.

    “These results highlight the capability of the microbiome to exert diverse effects on immune function, not only through direct interaction with immune cells in the gut, but also through indirect mechanisms such as regulating the systemic availability of critical metabolites.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AP-1 is known to play a critical role in the IL-1B-mediated induction of pro-inflammatory cytokines such as IL-6.

    “Furthermore, AP-1 is known to play a critical role in the IL-1B-mediated induction of pro-inflammatory cytokines such as IL-6 (Cahill and Rogers, 2008).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cahill and Rogers, 2008

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Bacteria-metabolite associations dominated the most significant associations in the MMRN network, demonstrating the substantial capability of the gut microbiome to shape host metabolism.

    “Bacteria-metabolite associations dominated the most significant associations in the network (Figure 7A), demonstrating the substantial capability of the gut microbiome to shape host metabolism.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Initial transcriptomics data quality was assessed using background level, 3' labeling bias, and pairwise correlation among samples.

    “Initial data quality was assessed by background level, 3’ labeling bias, and pairwise correlation among samples.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    In the metabolomics analysis, samples were removed if the pairwise correlation among three technical replicates was below 0.7; otherwise, the average of technical replicates was used.

    “Pearson correlation coefficient R between the three technical replicates was used as an indicator of analytical quality. Samples were removed if pairwise correlation between 3 technical replicates was R<0.7. Otherwise, average value of technical replicates was used.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    In the metabolomics analysis, only peaks that were present in more than 50% of samples were retained for further analysis.

    “Only peaks that were present in > 50% samples were used for further analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Two separate SPR runs provided the antibody off-rate constants.

    “Off-rate constants were determined from two independent SPR runs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The significance of associations between feature clusters in the MMRN was assessed by comparison with permutation data, resampling both features and sample labels.

    “The significance of associations was computed by comparing with permutation data, resampling both features and sample labels.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Strain quantification by qPCR used three replicates per sample, with the mean taken.

    “Each sample was run in triplicate and the mean value was used.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study required a minimum difference of 0.25% between the identity of the best strain hit and the second best hit to ensure specificity of strain OTU assignments.

    “To ensure specificity of the strain hits, a difference of >=0.25% between the identity of the best hit and the second best hit was required (e.g. 99.75 versus 99.5).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    In the transcriptomics analysis, probes that mapped to multiple genes were discarded, and the remaining probes were collapsed to gene level by selecting the probe with the highest mean expression across all subjects in both trials.

    “Probes mapping to multiple genes were discarded, and the remaining probes were collapsed to gene level by selecting the probe for each gene with the highest mean expression across all subjects in both trials.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    In the metabolomics analysis, if a single technical replicate had a correlation below 0.7 with the other two, it was removed and the other two replicates were averaged.

    “If a single technical replicate had R<0.7 with the other two, it was removed and other two were averaged.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    In the transcriptomics analysis, CEL files from outlier samples were excluded, and the remaining CEL files from the same trial were grouped and normalized together using RMA.

    “CEL files from outlier samples were excluded and the remaining CEL files of all the samples belonging to the same trial were grouped and normalized in Bioconductor by RMA (Irizarry et al., 2003), which includes global background adjustment and quantile normalization.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Irizarry et al., 2003 source

      Not checked in the free run.

56 of 56

  • The paper relies on this without support.

    Virtually all humans have been previously exposed to influenza.

    “whereas virtually all humans have been previously exposed”

  • The paper relies on this without support.

    AP-1 and NR4A signaling pathways are triggered by bacterial LPS.

    “These upregulated pathways (AP-1 and NR4A signaling) are also known to be triggered by bacterial LPS”

  • The paper relies on this without support.

    The effects of the antibiotic cocktail on immune responses are mediated by changes in the gut microbiome rather than by direct effects of antibiotics on the immune system.

    “Together, these results suggest that secondary bile acid-associated inflammatory signaling and impaired IgG1 responses to influenza vaccination arise as independent effects mediated by antibiotics-driven loss of key bacterial communities in the gut.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In humans with pre-existing immune system impairment, microbiome depletion through broad-spectrum antibiotics results in a diminished antibody response to seasonal influenza vaccination, augmented circulatory inflammatory signatures and altered plasma metabolome profiles.

    “In humans with pre-existing immune system impairment, microbiome depletion through broad-spectrum antibiotics not only results in a diminished antibody response to seasonal influenza vaccination, but also leads to augmented circulatory inflammatory signatures and altered plasma metabolome profiles.135”

    From Interaction between microbiota and immunity in health and disease (2020) · cites this paper as [135]

  • Antibiotic-induced gut dysbiosis impaired response to H1N1 vaccination with enhanced inflammation.

    “For Influenza, antibiotic-induced gut dysbiosis impaired response to H1N1 vaccination (Hagan et al., 2019) with enhanced inflammation.”

    From Viral Infections, the Microbiome, and Probiotics (2021) · cites this paper as Hagan et al., 2019

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conflicting age ranges for participants

    Minor

    The Results describe the first cohort as aged 18-45, while the study's eligibility description gives an age range of 18-40. The stated upper age limit is therefore inconsistent.

1 of 1

Cites

1 retracted

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Hagan, T., Cortese, M., Rouphael, N., Boudreau, C., Linde, C., Maddur, M. S., Das, J., Wang, H., Guthmiller, J., Zheng, N.-Y., Huang, M., Uphadhyay, A. A., Gardinassi, L., Petitdemange, C., McCullough, M. P., Johnson, S. J., Gill, K., Cervasi, B., Zou, J., Bretin, A., Hahn, M., Gewirtz, A. T., Bosinger, S. E., Wilson, P. C., Li, S., Alter, G., Khurana, S., Golding, H., & Pulendran, B.
Affiliations
  • Stanford University (US)
  • Ragon Institute of MGH, MIT and Harvard (US)
  • Emory National Primate Research Center (US)
  • University of Chicago (US)
  • Emory University (US)
  • Georgia State University (US)
  • Center for Biologics Evaluation and Research (US)
  • Stanford Medicine (US)
Corresponding author
Pulendran, Bali
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2019-09
Volume
178
Issue
6
Pages
1313-1328.e13
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2019.08.010
PMC
PMC6750738
OpenAlex
W2971999614
Cited by
572
References
81
Citations by year
2026: 41 · 2025: 70 · 2024: 74 · 2023: 87 · 2022: 105 · 2021: 96 · 2020: 86 · 2019: 13
FWCI
24.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (U19AI057266, HIPC U19AI090023, HHSN272201400004C, T32AI07290, U24AI120134, UH2AI132345)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Immunology and Microbiology › Immunology › Immune responses and vaccinations
  • Life Sciences › Immunology and Microbiology › Immunology › Immune Response and Inflammation
Keywords
gut microbiota, broad-spectrum antibiotics, seasonal influenza vaccination, vaccine immunity, H1N1 influenza, antibody response, IgG1, IgA, neutralizing antibodies, bacterial diversity, secondary bile acids, inflammasome activation, dendritic cell activation, inflammation, multi-omics integration
MeSH terms
Gastrointestinal Microbiome, Immunogenicity, Vaccine, Adolescent, Adult, Anti-Bacterial Agents, Antibodies, Viral, Antibody Formation, Female, Humans, Immunity, Influenza, Human, Influenza Vaccines, Male, Influenza A Virus, H1N1 Subtype, Young Adult, Healthy Volunteers

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review