Compositional analyses reveal correlations between taxon-level gut bacterial abundance and peripheral T cell marker expression in African infants

Bp, Brown; Jaspan, Heather B.

doi:10.1080/19490976.2019.1643673

Cited by 4 publications

(6 citation statements)

References 42 publications

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“…Taken together, these data suggest that exclusive breastfeeding might reduce the risk of HAEC through modulation of the enteric microbiome. Breastfeeding is an important factor in the development of the enteric microbiome, [16][17][18] and breast milk ingestion may facilitate the enrichment of microbes during the acquisition of the enteric microbiome. 15 Gram-negative bacteria, a major cause of enteric infection, can activate mucosal inflammation by binding LPS, a component of the outer membrane, to enteric toll-like receptor 4, 19,20 and human milk can attenuate LPS-induced enteric inflammation by modulating CD14 expression in human enterocytes.…”

Section: Discussionmentioning

confidence: 99%

Prospective study reveals a microbiome signature that predicts the occurrence of post-operative enterocolitis in Hirschsprung disease (HSCR) patients

Tang

Yuan

et al. 2020

Gut Microbes

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Hirschsprung disease (HSCR) is a birth defect with an approximate incidence of 1/5,000 live births, and up to one-third of HSCR patients develop Hirschsprung-associated enterocolitis (HAEC), the leading cause of HSCR-related death. Very little is known about the pathogenesis, prevention, and early diagnosis of HAEC. Here, we used a prospective study to investigate the enteric microbiome composition at the time of surgery as a predictor for developing postoperative HAEC. We identified a microbiome signature containing 21 operational taxonomic units (OTUs) that can potentially predict postoperative HAEC with~85% accuracy. Furthermore, we identified exclusive breastfeeding as a novel protective factor for total HAEC (i.e., preoperative and postoperative HAEC combined). In addition, we discovered that breastfeeding was associated with a lowered risk for HAEC potentially mediated by modulating the gut microbiome composition characterized by a lower abundance of Gram-negative bacteria and lower LPS concentrations. In conclusion, modulating the gut microbiome by encouraging breastfeeding might prevent HAEC progression in HSCR patients.

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Section: Discussionmentioning

confidence: 99%

Prospective study reveals a microbiome signature that predicts the occurrence of post-operative enterocolitis in Hirschsprung disease (HSCR) patients

Tang

Yuan

et al. 2020

Gut Microbes

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“…are important microbes for both delivery and diet types. Streptococcus luteciae had previously been reported to be associated with infant feeding [47]; and, it is a skin bacterium, which can be the reason why in our study it was associated with birth mode (like other skin bacteria) and exhibited low neighbourhood dynamics. Other markers, such as Ruminococcus sp, Lachnospira and Bifidobacterium sp ., are too general, and little biological interpretation can be extracted in the context of this study.…”

Section: Discussionmentioning

confidence: 71%

“…The latter neighbourhood dynamics analysis did not account for differences between infants by diet during months 6 and 9, nor delivery mode. Specifically, Streptococcus luteciae had previously been reported to be associated with infant feeding [47]; moreover, its association with the delivery type can be explained by its relation to the skin bacterium.…”

Section: Longitudinal Analysis: Neighbourhood Dynamics In Individual-...mentioning

confidence: 99%

Capturing the dynamics of microbiomes using individual-specific networks

Yousefi

Melograna

Galazzo

et al. 2023

Preprint

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Background: Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains a daunting task. The vast majority of statistical tools and methods available to study the microbiota are based upon cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on how to incorporate individual-specific microbial associations in temporal analyses when the focus lies on microbial interactions. Results: Here, we propose a novel data analysis framework, called MNDA, to uncover taxon neighbourhood dynamics that combines representation learning and individual-specific microbiome co-occurrence networks. We show that tracking local neighbourhood dynamics in microbiome interaction or co-occurrence networks can yield complementary information to standard approaches that only use microbial abundances or pairwise microbial interactions. We use cohort data on infants for whom microbiome data was available at 6 and 9 months after birth, as well as information on mode of delivery and diet changes over time. In particular, MNDA-based prediction models outperform traditional prediction models based on individual-specific abundances, and enable the detection of microbes whose neighbourhood dynamics are informative of clinical variables. We further show that similarity analyses of individuals based on microbial neighbourhood dynamics can be used to find subpopulations of individuals with potential relevance to clinical practice. The annotated source code for the MNDA framework can be downloaded from: https://github.com/H2020TranSYS/microbiome\_dynamics Conclusions: MNDA extracts information from matched microbiome profiles and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

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“…The latter neighborhood dynamics analysis did not account for differences between infants by diet during months 6 and 9, nor delivery mode. Specifically, Streptococcus luteciae had previously been reported to be associated with infant feeding (Brown and Jaspan, 2020 ); moreover, its association with the delivery type can be explained by its relation to the skin bacterium.…”

Section: Resultsmentioning

confidence: 99%

Capturing the dynamics of microbial interactions through individual-specific networks

Yousefi¹,

Melograna²,

Galazzo³

et al. 2023

Front. Microbiol.

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Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

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Compositional analyses reveal correlations between taxon-level gut bacterial abundance and peripheral T cell marker expression in African infants

Cited by 4 publications

References 42 publications

Prospective study reveals a microbiome signature that predicts the occurrence of post-operative enterocolitis in Hirschsprung disease (HSCR) patients

Prospective study reveals a microbiome signature that predicts the occurrence of post-operative enterocolitis in Hirschsprung disease (HSCR) patients

Capturing the dynamics of microbiomes using individual-specific networks

Capturing the dynamics of microbial interactions through individual-specific networks

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