Microbiome Continuum: Connecting Environmental, Host-Associated and Engineered Microbiomes Through One Health and Artificial Intelligence.
- 1BCSIR Chattogram Laboratories, BCSIR, Chattogram, Bangladesh.
- 2Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh.
Abstract
Microbiomes are increasingly understood as interconnected ecological systems spanning soil, water, plants, animals, humans and engineered environments rather than as isolated host-associated communities. This microbiome continuum provides a mechanistic perspective for One Health by linking microbial cells with the movement of metabolites, genes, signalling molecules and ecological functions across environmental boundaries. Here, we critically examined the biological processes that maintain this continuity, including dispersal, horizontal gene transfer (HGT), metabolic exchange, environmental exposure and host-microbe communication. We consider how climate change and anthropogenic disturbance reshape these interactions and influence antimicrobial resistance (AMR), non-communicable disease, ecosystem function and pathogen emergence. We further evaluate how artificial intelligence (AI), including machine learning and deep learning, can integrate multi-omics and longitudinal data, while emphasizing that predictive performance does not establish causality and that AI remains dependent on data quality, biological knowledge and experimental validation. Engineered microbiomes offer opportunities for targeted intervention, but ecological stability, biosafety, HGT and translational uncertainty remain important constraints. We propose a One Health Microbiome Intelligence Framework (OH-MIF) as a conceptual architecture for cross-domain integration, with emphasis on standardized data, causal validation, equitable participation and human oversight.
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BibTeX
@article{bhuiyan2026microbiome,
title = {{Microbiome Continuum: Connecting Environmental, Host-Associated and Engineered Microbiomes Through One Health and Artificial Intelligence.}},
author = {Mohammad Nazrul Islam Bhuiyan},
journal = {Environmental microbiology reports},
year = {2026},
month = oct,
doi = {10.1111/1758-2229.70445},
url = {https://europepmc.org/article/MED/42919800},
note = {CC BY},
}
RIS
TY - JOUR
TI - Microbiome Continuum: Connecting Environmental, Host-Associated and Engineered Microbiomes Through One Health and Artificial Intelligence.
AU - Mohammad Nazrul Islam Bhuiyan
PY - 2026
DA - 2026/10/01
JO - Environmental microbiology reports
DO - 10.1111/1758-2229.70445
UR - https://europepmc.org/article/MED/42919800
AB - Microbiomes are increasingly understood as interconnected ecological systems spanning soil, water, plants, animals, humans and engineered environments rather than as isolated host-associated communities. This microbiome continuum provides a mechanistic perspective for One Health by linking microbial cells with the movement of metabolites, genes, signalling molecules and ecological functions across environmental boundaries. Here, we critically examined the biological processes that maintain this continuity, including dispersal, horizontal gene transfer (HGT), metabolic exchange, environmental exposure and host-microbe communication. We consider how climate change and anthropogenic disturbance reshape these interactions and influence antimicrobial resistance (AMR), non-communicable disease, ecosystem function and pathogen emergence. We further evaluate how artificial intelligence (AI), including machine learning and deep learning, can integrate multi-omics and longitudinal data, while emphasizing that predictive performance does not establish causality and that AI remains dependent on data quality, biological knowledge and experimental validation. Engineered microbiomes offer opportunities for targeted intervention, but ecological stability, biosafety, HGT and translational uncertainty remain important constraints. We propose a One Health Microbiome Intelligence Framework (OH-MIF) as a conceptual architecture for cross-domain integration, with emphasis on standardized data, causal validation, equitable participation and human oversight.
SN - 1758-2229
ER -