Browsing by Author "Moossavi, Shirin"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota
    (2020-09-18) Moossavi, Shirin; Atakora, Faisal; Fehr, Kelsey; Khafipour, Ehsan
    Abstract Background In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear. Results Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition. Conclusion Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.
  • Thumbnail Image
    ItemOpen Access
    Divergent maturational patterns of the infant bacterial and fungal gut microbiome in the first year of life are associated with inter-kingdom community dynamics and infant nutrition
    (2024-02-07) Mercer, Emily M.; Ramay, Hena R.; Moossavi, Shirin; Laforest-Lapointe, Isabelle; Reyna, Myrtha E.; Becker, Allan B.; Simons, Elinor; Mandhane, Piush J.; Turvey, Stuart E.; Moraes, Theo J.; Sears, Malcolm R.; Subbarao, Padmaja; Azad, Meghan B.; Arrieta, Marie-Claire
    Abstract Background The gut microbiome undergoes primary ecological succession over the course of early life before achieving ecosystem stability around 3 years of age. These maturational patterns have been well-characterized for bacteria, but limited descriptions exist for other microbiota members, such as fungi. Further, our current understanding of the prevalence of different patterns of bacterial and fungal microbiome maturation and how inter-kingdom dynamics influence early-life microbiome establishment is limited. Results We examined individual shifts in bacterial and fungal alpha diversity from 3 to 12 months of age in 100 infants from the CHILD Cohort Study. We identified divergent patterns of gut bacterial or fungal microbiome maturation in over 40% of infants, which were characterized by differences in community composition, inter-kingdom dynamics, and microbe-derived metabolites in urine, suggestive of alterations in the timing of ecosystem transitions. Known microbiome-modifying factors, such as formula feeding and delivery by C-section, were associated with atypical bacterial, but not fungal, microbiome maturation patterns. Instead, fungal microbiome maturation was influenced by prenatal exposure to artificially sweetened beverages and the bacterial microbiome, emphasizing the importance of inter-kingdom dynamics in early-life colonization patterns. Conclusions These findings highlight the ecological and environmental factors underlying atypical patterns of microbiome maturation in infants, and the need to incorporate multi-kingdom and individual-level perspectives in microbiome research to improve our understandings of gut microbiome maturation patterns in early life and how they relate to host health. Video Abstract
  • Thumbnail Image
    ItemOpen Access
    Divergent maturational patterns of the infant bacterial and fungal gut microbiome in the first year of life are associated with inter-kingdom community dynamics and infant nutrition
    (2024-02-07) Mercer, Emily M.; Ramay, Hena R.; Moossavi, Shirin; Laforest-Lapointe, Isabelle; Reyna, Myrtha E.; Becker, Allan B.; Simons, Elinor; Mandhane, Piush J.; Turvey, Stuart E.; Moraes, Theo J.; Sears, Malcolm R.; Subbarao, Padmaja; Azad, Meghan B.; Arrieta, Marie-Claire
    Abstract Background The gut microbiome undergoes primary ecological succession over the course of early life before achieving ecosystem stability around 3 years of age. These maturational patterns have been well-characterized for bacteria, but limited descriptions exist for other microbiota members, such as fungi. Further, our current understanding of the prevalence of different patterns of bacterial and fungal microbiome maturation and how inter-kingdom dynamics influence early-life microbiome establishment is limited. Results We examined individual shifts in bacterial and fungal alpha diversity from 3 to 12 months of age in 100 infants from the CHILD Cohort Study. We identified divergent patterns of gut bacterial or fungal microbiome maturation in over 40% of infants, which were characterized by differences in community composition, inter-kingdom dynamics, and microbe-derived metabolites in urine, suggestive of alterations in the timing of ecosystem transitions. Known microbiome-modifying factors, such as formula feeding and delivery by C-section, were associated with atypical bacterial, but not fungal, microbiome maturation patterns. Instead, fungal microbiome maturation was influenced by prenatal exposure to artificially sweetened beverages and the bacterial microbiome, emphasizing the importance of inter-kingdom dynamics in early-life colonization patterns. Conclusions These findings highlight the ecological and environmental factors underlying atypical patterns of microbiome maturation in infants, and the need to incorporate multi-kingdom and individual-level perspectives in microbiome research to improve our understandings of gut microbiome maturation patterns in early life and how they relate to host health. Video Abstract
  • Thumbnail Image
    ItemOpen Access
    Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota
    (2021-02-10) Moossavi, Shirin; Fehr, Kelsey; Khafipour, Ehsan; Azad, Meghan B
    Abstract Background Quality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established. Results In this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis. Conclusion This study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field. Video abstract