Cell Reports: Methods (Oct 2023)
Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses
Abstract
Summary: Understanding the effects of the microbiome on the host’s metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling. Motivation: Causal inference on microbiome-host relations in the domain of metabolism using observational data is often intractable due to the multivariate nature of both omics classes and the effects of unmeasured confounders. However, given the emerging importance of the microbiome as an interventional target, understanding the causal effects of the microbiome on the host’s metabolome becomes crucial to designing and testing the efficacy of pre-biotic and pro-biotic interventions. To aid the task of causal inference on observational microbiome-metabolome data, we propose integrating knowledge-based deterministic constraint-based modeling of microbiome communities with statistical multi-omics analysis. Analyzing confounding and causal paths, we demonstrate that this integration allows for causal inference on microbiome-metabolite relations, even in the presence of unmeasured confounders.
