Author: Sylva Donaldson

Lauren LeMay-Nedjelski, Chloe Yonemitsu, Michelle R Asbury, James Butcher, Sylvia H Ley, Anthony J Hanley, Alex Kiss, Sharon Unger, Julia K Copeland, Pauline W Wang, Alain Stintzi, Lars Bode, and Deborah L O’Connor

J Nutr 2021 Nov 2;151(11):3431-3441. doi: 10.1093/jn/nxab270.

PMID: 34510198

Abstract

Background: Human milk is a rich source of human milk oligosaccharides (HMOs) and bacteria. It is unclear how these components interact within the breast microenvironment.

Objectives: The objectives were first, to investigate the association between maternal characteristics and HMOs, and second, to assess the association between HMOs and microbial community composition and predicted function in milk from women with high rates of gestational glucose intolerance.

Methods: This was an exploratory analysis of a previously completed prospective cohort study (NCT01405547) where milk samples (n = 107) were collected at 3 mo postpartum. Milk microbiota composition was analyzed by V4-16S ribosomal RNA gene sequencing and HMOs by rapid high-throughput HPLC. Data were stratified and analyzed by maternal secretor status phenotype and associations between HMOs and microbiota were determined using linear regression models (ɑ-diversity), Adonis (B-diversity), Poisson regression models (differential abundance), and general linear models (predicted microbial function).

Results: Prepregnancy BMI, race, and frequency of direct breastfeeding, but not gestational glucose intolerance, were found to be significantly associated with a number of HMOs among secretors and non-secretors. Fucosyllacto-N-hexaose was negatively associated with microbial richness (Chao1) among secretors [B-estimate (SE): -9.3 × 102 (3.4 × 102); P = 0.0082] and difucosyllacto-N-hexaose was negatively associated with microbiota diversity (Shannon index) [-1.7 (0.78); P = 0.029] among secretors. Lacto-N-neotetraose (LNnT) was associated with both microbial B-diversity (weighted UniFrac R2 = 0.040, P = 0.036) and KEGG ortholog B-diversity (Bray-Curtis R2 = 0.039, P = 0.043) in secretors. Additionally, difucosyllactose in secretors and disialyllacto-N-hexaose and LNnT in non-secretors were associated with enrichment of predicted microbial genes encoding for metabolism- and infection-related pathways (P-false discovery rate < 0.1).

Conclusions: HMOs are associated with the microbial composition and predicted microbial functions in human milk at 3 mo postpartum. Further research is needed to investigate the role these relations play in maternal and infant health.

Julia K. Copeland, Gary Chao, Shelley Vanderhout, Erica Acton, Pauline W. Wang, Eric I. Benchimol, Ahmed El-Sohemy, Ken Croitoru, Jennifer L. Gommerman, David S. Guttman & the GEMINI Research Team

Gut Microbes 2021 Jan-Dec;13(1):1-29. doi: 10.1080/19490976.2021.1902705.

PMID: 33794735

Abstract

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.

Szamosi Jake C., Forbes Jessica D., Copeland Julia K., Knox Natalie C., Shekarriz Shahrokh, Rossi Laura, Graham Morag, Bonner Christine, Guttman David S., Van Domselaar Gary, Surette Michael G., Bernstein Charles N.

Front. Microbiol., 21 August 2020 Sec. Systems Microbiology Volume 11 – 2020. doi: 10.3389/fmicb.2020.02028

PMID: 32973734

Background: In 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.

Methods: To 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.

Results: The 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.

Conclusion: Although 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.

Lisa R. McTaggart, Julia K. Copeland, Anuradha Surendra, Pauline W. Wang, Shahid Husain, Bryan Coburn, David S. Guttman and Julianne V. Kus

Front Microbiol. 2019 Mar 15;10:512. doi: 10.3389/fmicb.2019.00512. eCollection 2019.

PMID: 30930884

Abstract

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.