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The establishment of the gut microbiome plays a key symbiotic role in the developing immune system; however, its influence on vaccine response is yet uncertain. We prospectively investigated the composition and diversity of the early-life gut microbiome in relation to infant antibody response to two routinely administered vaccines.Eighty-three infants enrolled in the New Hampshire Birth Cohort Study were included in the analysis. We collected blood samples at 12 months of age and assayed the isolated serum to quantify total IgG and measured antibody to pneumococcal capsular polysaccharide and tetanus toxoid. Stool samples were collected from infants at 6 weeks of age and sequenced using 16S rRNA, and a subset of 61 samples were sequenced using shotgun metagenomics sequencing.We observed differences in beta diversity for 16S 6-week stool microbiota and pneumococcal and tetanus IgG antibody responses. Metagenomics analyses identified species and metabolic pathways in 6-week stool associated with tetanus antibody response, in particular, negative associations with the relative abundance of Aeriscardovia aeriphila species and positive associations with the relative abundance of species associated with CDP-diacylglycerol biosynthesis pathways.The early gut microbiome composition may influence an infant's vaccine response.Early intestinal microbiome acquisition plays a critical role in immune maturation and in both adaptive and innate immune response in infancy. We identified associations between early life microbiome composition and response to two routinely administered vaccines-pneumococcal capsular polysaccharide and tetanus toxoid-measured at approximately 1 year of age. Our findings highlight the potential impact of the gut microbiome on infant immune response that may open up opportunities for future interventions.

Additional file 2: Supplementary figures S1, S2, S3 and S4. Fig. S1. Timeline of stool samples collected from FMT study. Each circle represents a stool sample collected from either an FMT donor or FMT recipient. The thicker, red vertical line at day 0 represents the FMT event for each recipient. FMT method (pill or colonoscopy) and FMT recipient health and disease state (C. diff - chronic recurrent Clostridium difficile infection, UC - ulcerative colitis) are indicated on the right. Fig. S2. Nonmetric multidimensional scaling (NMDS) ordination of the taxonomic composition of donor, recipient, and Canadian gut metagenomes at the genus level based on Morisita-Horn dissimilarity. Samples from the same participant are joined by lines with the earliest time point labeled. CAN: Canadian gut metagenomes, DA: donor A, DB: donor B, POST: recipients post-FMT, PRE: recipients pre-FMT. Fig. S3. Nonmetric multidimensional scaling (NMDS) ordination of the taxonomic composition of the donor and recipient metagenomes at genus level based on Morisita-Horn dissimilarity. Samples from the same participant are joined by lines with the earliest time point labeled. DA_POST: donor A recipients post-FMT, DA_PRE: donor A recipients pre-FMT, DA: donor A, DB_POST: donor B recipients post-FMT, DB_PRE: donor B recipients pre-FMT, DB: donor B. Fig. S4. A flowchart outlining our method to assign successful colonization, failed colonization, or undetermined colonization phenotypes to donor-derived populations in the recipients of that donor’s stool.

Additional file 6. Additional discussion and supplementary figures for A adaptive ecological forces are the primary drivers of microbial colonization; B considerations of annotation bias that reduce the number of genes with functional annotations for microbial populations that typically occur in healthy individuals.

Additional file 11. Review history.

Additional file 1: Contains supplementary notes (Notes 1–3), supplementary figures (Figures S1-S11), supplementary Table 1 and 2. Figure S1. Inter-omics Procrustes biplots comparing PCoA ordinations of untargeted metabolite profiles and taxonomic relative abundances for 6 weeks (left panels) (n = 158) and 12 months (right panels) (n = 262). Top panels present analyses based on ordinations from Euclidean distances of genus level abundances after centered log ratio transformation and Euclidean distances of arcsine square root transformed metabolite relative abundances. Bottom panel presents analyses based on generalized Unifrac distance of amplicon sequence variant (ASV) relative abundances and Euclidean distances of arcsine square root transformed metabolite relative abundances. Figure S2. Pairwise Spearman correlation of metabolite bins and genus-level taxonomic abundances for 6-weeks (panel A, N = 158) and 12-months (panel B, N = 282) infants. Left panel displays the overall correlation pattern, where non-significant correlations are not colored (false discovery rate (FDR) controlled q-value 0.05) are not colored. Table S1. Metabolites selected for targeted analysis and their potential biological functions. Table S2. Primers used for bacterial 16S rRNA gene sequencing.

Additional file 1 Fig. S1: Relative abundance and distribution of number of unique antimicrobial resistance gene markers by sample age. Fig. S2: Longitudinal change in relative abundance of ARG markers colored by mechanism of antibiotic resistance (antibiotic efflux or not). Fig. S3: Heat map showing the most prevalent antibiotic resistance genes (present in at least 60% of samples) by sample. Fig. S4: Differential unique antimicrobial resistance genes and E. coli relative abundance between paired samples by delivery mode. Fig. S5: E. coli strains do not have differential genes based on early-life exposures. Fig. S6: Four gene families related to mobile genetic elements are associated with sample age and specific taxa. Fig. S7: Overview of Analytic Steps and Main Statistical Methods. Fig. S8: Quality control to assess the validity of the sequencing and downstream analysis tools. Supplementary Methods: Infant Feeding Mode up to the Time of Stool Sample Collection.

Additional file 8. Additional discussion and supplementary figures for (A) taxonomy based on metagenome-assembled genomes, metagenomics short reads, and 16S rRNA gene amplicons; (B) Average Nucleotide Identity (ANI) of oral TM7; (C) occurrence of TM7 across additional oral sample types, other than supragingival plaque and tongue dorsum, and including samples from patients with periodontitis; (D) mobile elements and prophages in TM7 genomes; (E) novel non-CPR MAGs; (F) a novel MAG that represents a member of the Mollicutes; (G) novel Clostridiales MAGs represent prevalent tongue-associated populations; and (H) novel Bacteroidia MAGs that include a tongue-specialist and a subgingival plaque specialist.

Additional file 13. Review history.

Additional file 4. Supplementary figures S1, S2, S3, S4, S5, S6, and S7.

We report the genome sequence of Thermococcus sp. EXT12c isolated from a deep-sea hydrothermal vent at the East Pacific Rise 9°N. Microbes in the genus Thermococcus are able to grow anaerobically at high temperature, around neutral pH, and some of them under high hydrostatic pressure.

Deep sequencing of PCR amplicon libraries facilitates the detection of low-abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low-frequency, error-prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete-linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single-linkage preclustering methodology followed by an average-linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30-60% but does not reduce the fraction of OTUs in long-tailed rank abundance curves that defines the rare biosphere.