Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses.
Cell Rep Methods
; 3(10): 100615, 2023 Oct 23.
Article
en En
| MEDLINE
| ID: mdl-37848031
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Microbiota
/
Microbioma Gastrointestinal
Idioma:
En
Revista:
Cell Rep Methods
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Estados Unidos