Causal Inference in Microbiomes Using Intervention Calculus

Sazal, Musfiqur Rahman; Stebliankin, Vitalii; Mathee, Kalai; Narasimhan, Giri

doi:10.1101/2020.02.28.970624

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…The human microbiome is an integral part of our (patho)physiology. Yet, an overwhelming part of microbiome research still concentrates on the description of microbial communities and their population dynamics (Round and Palm 2018 ; Sazal et al 2020 ; Walter et al 2020 ). Contrastingly and with hardly any in vitro systems available studying the underlying mechanisms and causality of host–microbe interactions remains a major challenge.…”

Section: Discussionmentioning

confidence: 99%

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

et al. 2020

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The skin`s microbiome is predominantly commensalic, harbouring a metabolic potential far exceeding that of its host. While there is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host there is still a lack of models for investigating causality of microbiome-associated pathophysiology or toxicity. We now report on a biologically characterised microbial–skin tissue co-culture that allows studying microbe–host interactions for extended periods of time in situ. The system is based on a commercially available 3D skin model. In a proof-of-concept, this model was colonised with single and mixed cultures of two selected skin commensals. Two different methods were used to quantify the bacteria on the surface of the skin models. While Micrococcus luteus established a stable microbial–skin tissue co-culture, Pseudomonas oleovorans maintained slow continuous growth over the 8-day cultivation period. A detailed skin transcriptome analysis showed bacterial colonisation leading to up to 3318 significant changes. Additionally, FACS, ELISA and Western blot analyses were carried out to analyse secretion of cytokines and growth factors. Changes found in colonised skin varied depending on the bacterial species used and comprised immunomodulatory functions, such as secretion of IL-1α/β, Il-6, antimicrobial peptides and increased gene transcription of IL-10 and TLR2. The colonisation also influenced the secretion of growth factors such as VFGFA and FGF2. Notably, many of these changes have already previously been associated with the presence of skin commensals. Concomitantly, the model gained first insights on the microbiome’s influence on skin xenobiotic metabolism (i.e., CYP1A1, CYP1B1 and CYP2D6) and olfactory receptor expression. The system provides urgently needed experimental access for assessing the toxicological impact of microbiome-associated xenobiotic metabolism in situ.

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Section: Discussionmentioning

confidence: 99%

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

et al. 2020

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“…Our framework complements recent causal inference approaches for microbiome data such as mediation methods [ 60 , 61 ], graphical models [ 62 ], and Mendelian randomization [ 63 , 64 ] to analyze observational gut microbiome data. In these studies, the target for interventions is the microbiome and the understanding of its effects on diseases, i.e., the microbiome is treated as the exposure and diseases as outcomes.…”

Section: Introductionmentioning

confidence: 98%

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

et al. 2022

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Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

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“…Our framework complements recent causal inference approaches for microbiome data such as mediation methods [59, 60], graphical models [61], and Mendelian randomization [62, 63] to analyze observational gut microbiome data. In these studies, the target for interventions is the microbiome and the understanding of its effects on diseases, i.e., the microbiome is treated as the exposure and diseases as outcomes.…”

Section: Introductionmentioning

confidence: 98%

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Sommer

Peters

Cyrys

et al. 2021

Preprint

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Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. The observational character of prospective cohort data and the intricate characteristics of microbiome data make it, however, challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

show abstract

Causal Inference in Microbiomes Using Intervention Calculus

Cited by 5 publications

References 54 publications

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

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