Using 16S rRNA gene sequencing and targeted culture, we compared microbiota in fecal samples from infants with necrotizing enterocolitis (NEC) and controls. Two significant signatures were associated with NEC: 1 with dominant Clostridium perfringens and 1 with dominant Enterobacteriaceae.

The 16S rRNA gene is conserved across all bacteria and as such is routinely targeted in PCR surveys of bacterial diversity. PCR primer design aims to amplify as many different 16S rRNA gene sequences from as wide a range of organisms as possible, though there are no suitable 100% conserved regions of the gene, leading to bias. In the gastrointestinal tract, bifidobacteria are a key genus, but are often under-represented in 16S rRNA surveys of diversity. We have designed modified, ‘bifidobacteria-optimised’ universal primers, which we have demonstrated detection of bifidobacterial sequence present in DNA mixtures at 2% abundance, the lowest proportion tested. Optimisation did not compromise the detection of other organisms in infant faecal samples. Separate validation using fluorescence in situ hybridisation (FISH) shows that the proportions of bifidobacteria detected in faecal samples were in agreement with those obtained using 16S rRNA based pyrosequencing. For future studies looking at faecal microbiota, careful selection of primers will be key in order to ensure effective detection of bifidobacteria.

Summary Supplementation with members of the early-life microbiota as “probiotics” is increasingly used in attempts to beneficially manipulate the preterm infant gut microbiota. We performed a large observational longitudinal study comprising two preterm groups: 101 infants orally supplemented with Bifidobacterium and Lactobacillus (Bif/Lacto) and 133 infants non-supplemented (control) matched by age, sex, and delivery method. 16S rRNA gene profiling on fecal samples (n = 592) showed a predominance of Bifidobacterium and a lower abundance of pathobionts in the Bif/Lacto group. Metabolomic analysis showed higher fecal acetate and lactate and a lower fecal pH in the Bif/Lacto group compared to the control group. Fecal acetate positively correlated with relative abundance of Bifidobacterium, consistent with the ability of the supplemented Bifidobacterium strain to metabolize human milk oligosaccharides into acetate. This study demonstrates that microbiota supplementation is associated with a Bifidobacterium -dominated preterm microbiota and gastrointestinal environment more closely resembling that of full-term infants.

We showed that a partially hydrolyzed protein infant formula with specific prebiotics modulated the gut microbiota closer to that of breast-fed infants. Additionally, we identified a potential link between microbial activity and onset of eczema, which might reflect a suboptimal implementation of gut microbiota at specific developmental stages in infants at high risk for allergy.

Estimation of immunological and microbiological diversity is vital to our understanding of infection and the immune response. For instance, what is the diversity of the T cell repertoire? These questions are partially addressed by high-throughput sequencing techniques that enable identification of immunological and microbiological “species” in a sample. Estimators of the number of unseen species are needed to estimate population diversity from sample diversity. Here we test five widely used non-parametric estimators, and develop and validate a novel method, DivE, to estimate species richness and distribution. We used three independent datasets: (i) viral populations from subjects infected with human T-lymphotropic virus type 1; (ii) T cell antigen receptor clonotype repertoires; and (iii) microbial data from infant faecal samples. When applied to datasets with rarefaction curves that did not plateau, existing estimators systematically increased with sample size. In contrast, DivE consistently and accurately estimated diversity for all datasets. We identify conditions that limit the application of DivE. We also show that DivE can be used to accurately estimate the underlying population frequency distribution. We have developed a novel method that is significantly more accurate than commonly used biodiversity estimators in microbiological and immunological populations.