Initial microbial community of the neonatal stomach immediately after birth

Bajorek, Sarah; Parker, Leslie A.; Li, Nan; Winglee, Kathryn; Weaver, Michael T.; Johnson, James E.; Sioda, Michael; Gauthier, Josée; Lemas, Dominick J.; Jobin, Christian; Lorca, Graciela L.; Neu, Josef; Fodor, Anthony A.

doi:10.1080/19490976.2018.1520578

Cited by 13 publications

(10 citation statements)

References 21 publications

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“…Bifidobacterium and Bacteroides ) [ 49 , 50 ]. For example, gastric aspirates of new-born infants collected immediately after birth do not contain the microbes reported to be present in the amniotic fluid (which would be expected if it were colonized since the fetus swallows amniotic fluid) [ 51 ]. Instead, aspirates from vaginal-born infants contain exactly the Lactobacillus species that also dominate the microbiota of the vagina ( L. iners and L. crispatus ), while most samples from cesarean deliveries cluster with negative controls [ 51 ].…”

Section: Inference To the Best Explanation (Ibe)mentioning

confidence: 99%

A philosophical perspective on the prenatal in utero microbiome debate

Walter

Hornef

2021

Microbiome

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Within the last 6 years, a research field has emerged that focuses on the characterization of microbial communities in the prenatal intrauterine environment of humans and their putative role in human health. However, there is considerable controversy around the existence of such microbial populations. The often contentious debate is primarily focused on technical aspects of the research, such as difficulties to assure aseptic sampling and to differentiate legitimate signals in the data from contamination. Although such discussions are clearly important, we feel that the problems with the prenatal microbiome field go deeper. In this commentary, we apply a philosophical framework to evaluate the foundations, experimental approaches, and interpretations used by scientists on both sides of the debate. We argue that the evidence for a “sterile womb” is based on a scientific approach that aligns well with important principles of the philosophy of science as genuine tests of the hypothesis and multiple angles of explanatory considerations were applied. In contrast, research in support of the “in utero colonization hypothesis” is solely based on descriptive verifications that do not provide explanatory insight, which weakens the evidence for a prenatal intrauterine microbiome. We propose that a reflection on philosophical principles can inform not only the debate on the prenatal intrauterine microbiome but also other disciplines that attempt to study low-biomass microbial communities.

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Section: Inference To the Best Explanation (Ibe)mentioning

confidence: 99%

A philosophical perspective on the prenatal in utero microbiome debate

Walter

Hornef

2021

Microbiome

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“…Many of the articles, however, make little to no specification of the conditions for which a gut ecology can be considered balanced, well-functioning, highly diverse, or normal (Cong et al, 2017; Fourie et al, 2016; Heitkemper et al, 2013; Lacy & Moreau, 2016; Muls et al, 2017; Samuel et al, 2014; Slykerman et al, 2017). Furthermore, several articles also provide no explicit definition of dysbiosis or essential related terms such as microbiome or microbiota (Bajorek et al, 2019; Cong et al, 2017; Kelsey et al, 2019; Lacy & Moreau, 2016; Muls et al, 2017; Slykerman et al, 2017). While Cong et al (2015) actually provide an uncommon level of detail in their description of the gut microbiome, they italicize dysbiosis and offer dysbacteroisis as an equivalent term, which suggests a tentativeness in defining a quickly evolving term.…”

Section: Resultsmentioning

confidence: 99%

Dysbiosis of the Gut Microbiome: A Concept Analysis

2019

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Background:Gut microbes influence the development several chronic conditions marking them as targets for holistic care, prevention strategies, and potential treatments. Microbiome studies are relatively new to health research and present unfamiliar terms to clinicians and researchers. “Dysbiosis” often refers to an alteration in the gut microbiome, but conceptual clarification is rarely provided. Purpose: The purpose of this study is to refine a conceptual definition of dysbiosis based on a review of nursing literature. Method: A Rodgerian approach to concept analysis was used. CINAHL, PubMed, and Web of Science were queried using “dysbiosis” through December 2018. Each article was analyzed with regard to the antecedents, attributes, and consequences of dysbiosis. Essential elements were tabulated and compared across studies to determine recurring themes and notable outliers. Findings: Analysis revealed several important antecedences, attributes, and consequences of dysbiosis. The findings also elucidated notable gaps and highlighted the co-evolving nature of the proposed definition with advances in microbiome research. Conclusion: This article adds a proposed definition of dysbiosis, offering a contribution of conceptual clarity upon which to enhance dialogue and build research. The definition emphasizes risk factors and consequences of dysbiosis as implications for holistic nursing practice.

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“…Another related area for future work is in the specific model used for absolute abundances, which are not well-understood from currently available data; our current assumption holds if the total biomass of “typical” communities does not change under “typical” circumstances, but this is obviously quite qualitative. Direct measurements of microbial biomass in some environments such as the human gut have sometimes shown this within approximately one fold change [ 40 , 41 ], but not in all cases, and certainly not during extreme perturbations such as antibiotics [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

A statistical model for describing and simulating microbial community profiles

Ren

Mallick

et al. 2021

PLoS Comput Biol

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Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA’s model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. “taxa”) or between features and “phenotypes” to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA’s performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA’s utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

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Initial microbial community of the neonatal stomach immediately after birth

Cited by 13 publications

References 21 publications

A philosophical perspective on the prenatal in utero microbiome debate

A philosophical perspective on the prenatal in utero microbiome debate

Dysbiosis of the Gut Microbiome: A Concept Analysis

A statistical model for describing and simulating microbial community profiles

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