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Summary: Mother’s milk contains diverse bacterial communities, although their impact on microbial colonization in very-low-birth-weight (VLBW,

Abstract Background The emergence of antimicrobial resistance is a major threat to global health and has placed pressure on the livestock industry to eliminate the use of antibiotic growth promotants (AGPs) as feed additives. To mitigate their removal, efficacious alternatives are required. AGPs are thought to operate through modulating the gut microbiome to limit opportunities for colonization by pathogens, increase nutrient utilization, and reduce inflammation. However, little is known concerning the underlying mechanisms. Previous studies investigating the effects of AGPs on the poultry gut microbiome have largely focused on 16S rDNA surveys based on a single gastrointestinal (GI) site, diet, and/or timepoint, resulting in an inconsistent view of their impact on community composition. Methods In this study, we perform a systematic investigation of both the composition and function of the chicken gut microbiome, in response to AGPs. Birds were raised under two different diets and AGP treatments, and 16S rDNA surveys applied to six GI sites sampled at three key timepoints of the poultry life cycle. Functional investigations were performed through metatranscriptomics analyses and metabolomics. Results Our study reveals a more nuanced view of the impact of AGPs, dependent on age of bird, diet, and intestinal site sampled. Although AGPs have a limited impact on taxonomic abundances, they do appear to redefine influential taxa that may promote the exclusion of other taxa. Microbiome expression profiles further reveal a complex landscape in both the expression and taxonomic representation of multiple pathways including cell wall biogenesis, antimicrobial resistance, and several involved in energy, amino acid, and nucleotide metabolism. Many AGP-induced changes in metabolic enzyme expression likely serve to redirect metabolic flux with the potential to regulate bacterial growth or produce metabolites that impact the host. Conclusions As alternative feed additives are developed to mimic the action of AGPs, our study highlights the need to ensure such alternatives result in functional changes that are consistent with site-, age-, and diet-associated taxa. The genes and pathways identified in this study are therefore expected to drive future studies, applying tools such as community-based metabolic modeling, focusing on the mechanistic impact of different dietary regimes on the microbiome. Consequently, the data generated in this study will be crucial for the development of next-generation feed additives targeting gut health and poultry production. Video Abstract

Abstract Background Few studies have examined how maternal body mass index (BMI), mode of delivery and ethnicity affect the microbial composition of human milk and none have examined associations with maternal metabolic status. Given the high prevalence of maternal adiposity and impaired glucose metabolism, we systematically investigated the associations between these maternal factors in women ≥20 years and milk microbial composition and predicted functionality by V4-16S ribosomal RNA gene sequencing (NCT01405547; https://clinicaltrials.gov/ct2/show/NCT01405547 ). Demographic data, weight, height, and a 3-h oral glucose tolerance test were gathered at 30 (95% CI: 25–33) weeks gestation, and milk samples were collected at 3 months post-partum (n = 113). Results Multivariable linear regression analyses demonstrated no significant associations between maternal characteristics (maternal BMI [pre-pregnancy, 3 months post-partum], glucose tolerance, mode of delivery and ethnicity) and milk microbiota alpha-diversity; however, pre-pregnancy BMI was associated with human milk microbiota beta-diversity (Bray-Curtis R2 = 0.037). Women with a pre-pregnancy BMI > 30 kg/m2 (obese) had a greater incidence of Bacteroidetes (incidence rate ratio [IRR]: 3.70 [95% CI: 1.61–8.48]) and a reduced incidence of Proteobacteria (0.62 [0.43–0.90]) in their milk, compared to women with an overweight BMI (25.0–29.9 kg/m2) as assessed by multivariable Poisson regression. An increased incidence of Gemella was observed among mothers with gestational diabetes who had an overweight BMI versus healthy range BMI (5.96 [1.85–19.21]). An increased incidence of Gemella was also observed among mothers with impaired glucose tolerance with an obese BMI versus mothers with a healthy range BMI (4.04 [1.63–10.01]). An increased incidence of Brevundimonas (16.70 [5.99–46.57]) was found in the milk of women who underwent an unscheduled C-section versus vaginal delivery. Lastly, functional gene inference demonstrated that pre-pregnancy obesity was associated with an increased abundance of genes encoding for the biosynthesis of secondary metabolites pathway in milk (coefficient = 0.0024, P FDR

The effect of diet-induced obesity on intestinal B cell populations is not well understood despite emerging evidence of a critical role for the intestinal immune system in contributing to insulin resistance. Here, the authors show important functions of IgA in regulating metabolic disease and for intestinal immunity in modulating systemic glucose metabolism.

South Asian (SA) Canadian immigrants have a higher risk of developing certain immune-mediated inflammatory diseases compared to non-migrant SAs. We sought to investigate the effect of migration on the gut metagenome and to identify microbiological associations between migration and conditions that may influence the development of immune-mediated inflammatory diseases. Metagenomic analysis of 58 first-generation (GEN1) SA immigrants and 38 unrelated Canadian born children-of-immigrants (GEN2) determined that the time lived in Canada was associated with continued changes in gut microbial communities. Migration of GEN1 to Canada early in life results in a gut community with similarities to GEN2 SA Canadians and non-SA North Americans. Conversely, GEN1 immigrants who arrived recently to Canada exhibited pronounced differences from GEN2, while displaying microbial similarities to a non-migrating SA cohort. Multivariate analysis identified that community composition was primarily influenced by high abundance taxa. Prevotella copri dominated in GEN1 and non-migrant SAs. Clostridia and functionally related Bacteroidia spp. replaced P. copri dominance over generations in Canada. Mutually exclusive Dialister species occurred at differing relative abundances over time and generations in Canada. This shift in species composition is accompanied by a change in genes associated with carbohydrate utilization and short-chain fatty acid production. Total energy derived from carbohydrates compared to protein consumption was significantly higher for GEN1 recent immigrants, which may influence the functional requirements of the gut community. This study demonstrates the associations between migration and the gut microbiome, which may be further associated with the altered risk of immune-mediated inflammatory diseases observed for SA Canadians.

BackgroundIn studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories.MethodsTo evaluate the effect of technical variability, we used human intestinal biopsy samples resected from individuals diagnosed with an inflammatory bowel disease (IBD), including Crohn’s disease (n = 12) and ulcerative colitis (n = 10), and those without IBD (n = 10). Matched samples from each participant were sent to three laboratories and studied using independent protocols for DNA extraction, library preparation, targeted-amplicon sequencing of a 16S rRNA gene hypervariable region, and processing of sequence data. We looked at two measures of interest – Bray–Curtis PERMANOVA R2 values and log2 fold-change estimates of the 25 most-abundant taxa – to assess variation in the results produced by each laboratory, as well the relative contribution to variation from the different extraction, sequencing, and analysis steps used to generate these measures.ResultsThe R2 values and estimated differential abundance associated with diagnosis were consistent across datasets that used different DNA extraction and sequencing protocols, and within datasets that pooled samples from multiple protocols; however, variability in bioinformatic processing of sequence data led to changes in R2 values and inconsistencies in taxonomic assignment and abundance estimates.ConclusionAlthough the contribution of DNA extraction and sequencing methods to variability were observable, we find that results can be robust to the various extraction and sequencing approaches used in our study. Differences in data processing methods have a larger impact on results, making comparison among studies less reliable and the combined analysis of bioinformatically processed samples nearly impossible. Our results highlight the importance of making raw sequence data available to facilitate combined and comparative analyses of published studies using common data processing protocols. Study methodologies should provide detailed data processing methods for validation, interpretability, reproducibility, and comparability.

Invasive fungal infections are an increasingly important cause of human morbidity and mortality. We generated a next-generation sequencing (NGS)-based method designed to detect a wide range of fungi and applied it to analysis of the fungal microbiome (mycobiome) of the lung during fungal infection. Internal transcribed spacer 1 (ITS1) amplicon sequencing and a custom analysis pipeline detected 96% of species from three mock communities comprised of potential fungal lung pathogens with good recapitulation of the expected species distributions (Pearson correlation coefficients r = 0.63, p = 0.004; r = 0.71, p < 0.001; r = 0.62, p = 0.002). We used this pipeline to analyze mycobiomes of bronchoalveolar lavage (BAL) specimens classified as culture-negative (n = 50) or culture-positive (n = 39) for Blastomyces dermatitidis/gilchristii, the causative agent of North America blastomycosis. Detected in 91.4% of the culture-positive samples, Blastomyces dominated (>50% relative abundance) the mycobiome in 68.6% of these culture-positive samples but was absent in culture-negative samples. To overcome any bias in relative abundance due to between-sample variation in fungal biomass, an abundance-weighting calculation was used to normalize the data by accounting for sample-specific PCR cycle number and PCR product concentration data utilized during sample preparation. After normalization, there was a statistically significant greater overall abundance of ITS1 amplicon in the Blastomyces-culture-positive samples versus culture-negative samples. Moreover, the normalization revealed a greater biomass of yeast and environmental fungi in several Blastomyces-culture-positive samples than in the culture-negative samples. Successful detection of Coccidioides, Scedosporium, Phaeoacremonium, and Aspergillus in 6 additional culture-positive BALs by ITS1 amplicon sequencing demonstrates the ability of this method to detect a broad range of fungi from clinical specimens, suggesting that it may be a potentially useful adjunct to traditional fungal microbiological testing for the diagnosis of respiratory mycoses.

The leaf microbiome is influenced by both biotic and abiotic factors. Currently, we know little about the relative importance of these factors in determining microbiota composition and dynamics. To explore this issue, we collected weekly leaf samples over a 98-day growing season from multiple cultivars of common bean, soybean, and canola planted at three locations in Ontario, Canada, and performed Illumina-based microbiome analysis. We find that the leaf microbiota at the beginning of the season is very strongly influenced by the soil microbiota but, as the season progresses, it differentiates, becomes significantly less diverse, and transitions to having a greater proportion of leaf-specific taxa that are shared among all samples. A phylogenetic investigation of communities by reconstruction of unobserved states imputation of microbiome function inferred from the taxonomic data found significant differences between the soil and leaf microbiome, with a significant enrichment of motility gene categories in the former and metabolic gene categories in the latter. A network co-occurrence analysis identified two highly connected clusters as well as subclusters of putative pathogens and growth-promoting bacteria. These data reveal some of the complex ecological dynamics that occur in microbial communities over the course of a growing season and highlight the importance of community succession.

Invariant Natural Killer T (iNKT) cells are unconventional T cells that respond to glycolipid antigens found in microbes in a CD1d-dependent manner. iNKT cells exert innate-like functions and produce copious amounts of cytokines, chemokines and cytotoxic molecules within only minutes of activation. As such, iNKT cells can fuel or dampen inflammation in a context-dependent manner. In addition, iNKT cells provide potent immunity against bacteria, viruses, parasites and fungi. Although microbiota-iNKT cell interactions are not well-characterized, mounting evidence suggests that microbiota colonization early in life impacts iNKT cell homeostasis and functions in disease. In this study, we showed that CD1d−/− and Vα14 Tg mice, which lack and have increased numbers of iNKT cells, respectively, had no significant alterations in gut microbiota composition compared to their littermate controls. Furthermore, specific iNKT cell activation by glycolipid antigens only resulted in a transient and minimal shift in microbiota composition when compared to the natural drift found in our colony. Our findings demonstrate that iNKT cells have little to no influence in regulating commensal bacteria at steady state.Abbreviations: iNKT: invariant Natural Killer T cell; αGC: α-galactosylceramide

The microbiome shapes diverse facets of human biology and disease, with the importance of fungi only beginning to be appreciated. Microbial communities infiltrate diverse anatomical sites as with the respiratory tract of healthy humans and those with diseases such as cystic fibrosis, where chronic colonization and infection lead to clinical decline. Although fungi are frequently recovered from cystic fibrosis patient sputum samples and have been associated with deterioration of lung function, understanding of species and population dynamics remains in its infancy. Here, we coupled high-throughput sequencing of the ribosomal RNA internal transcribed spacer 1 (ITS1) with phenotypic and genotypic analyses of fungi from 89 sputum samples from 28 cystic fibrosis patients. Fungal communities defined by sequencing were concordant with those defined by culture-based analyses of 1,603 isolates from the same samples. Different patients harbored distinct fungal communities. There were detectable trends, however, including colonization with Candida and Aspergillus species, which was not perturbed by clinical exacerbation or treatment. We identified considerable inter- and intra-species phenotypic variation in traits important for host adaptation, including antifungal drug resistance and morphogenesis. While variation in drug resistance was largely between species, striking variation in morphogenesis emerged within Candida species. Filamentation was uncoupled from inducing cues in 28 Candida isolates recovered from six patients. The filamentous isolates were resistant to the filamentation-repressive effects of Pseudomonas aeruginosa, implicating inter-kingdom interactions as the selective force. Genome sequencing revealed that all but one of the filamentous isolates harbored mutations in the transcriptional repressor NRG1; such mutations were necessary and sufficient for the filamentous phenotype. Six independent nrg1 mutations arose in Candida isolates from different patients, providing a poignant example of parallel evolution. Together, this combined clinical-genomic approach provides a high-resolution portrait of the fungal microbiome of cystic fibrosis patient lungs and identifies a genetic basis of pathogen adaptation.