Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population

Abstract

Background The gut microbiome has proven to be an important factor affecting obesity; however, it remains a challenge to identify consistent biomarkers across geographic locations and perform precisely targeted modulation for obese individuals. Results This study proposed a systematic machine learning framework and applied it to 870 human stool metagenomes across five countries to obtain comprehensive regional shared biomarkers and conduct a personalized modulation analysis. In our pipeline, a heterogeneous ensemble feature selection diagram is first developed to determine an optimal subset of biomarkers through the aggregation of multiple techniques. Subsequently, a deep reinforcement learning method was established to alter the targeted composition to the desired healthy target. In this manner, we can realize personalized modulation by counterfactual inference. Consequently, a total of 42 species were identified as regional shared biomarkers, and they showed good performance in distinguishing obese people from the healthy group (area under curve (AUC) =0.85) when demonstrated on validation datasets. In addition, by pooling all counterfactual explanations, we found that Akkermansia muciniphila, Faecalibacterium prausnitzii, Prevotella copri, Bacteroides dorei, Bacteroides eggerthii, Alistipes finegoldii, Alistipes shahii, Eubacterium sp. _CAG_180, and Roseburia hominis may be potential broad-spectrum targets with consistent modulation in the multi-regional obese population. Conclusions This article shows that based on our proposed machine-learning framework, we can obtain more comprehensive and accurate biomarkers and provide modulation analysis for the obese population. Moreover, our machine-learning framework will also be very useful for other researchers to further obtain biomarkers and perform counterfactual modulation analysis in different diseases.

Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms

“…Considering such close interaction, it is logical to assume that microbiota influence host gene expression in all body areas that it colonizes, such as skin, respiratory organs, gastrointestinal, and urogenital tract. Two main mechanisms allow the microbiota to “regulate” our genome: through direct body exposure to microorganisms [ 21 ] or those of their metabolites [ 18 , 22 ]. Indeed, we must recognize a fine distinction between microbial antigen-mediated genome modulation and metabolite-mediated one [ 19 ].…”

“…Second, we could modulate human genome expression and beneficially affect gut microbiome, positively affecting human health. All this future “personalized medicine” approach requires big data analysis, correlation models of analysis and machine learning support [ 18 ].…”

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data

“…In this experiment, we applied GDmicro to identify important biomarkers and explore their contribution to hosts’ disease status from cohorts used in the cross-study experiment. It should be noted that the goal of this experiment is to identify potential biomarkers, and it is a common practice to conduct biomarker discovery using a batch of samples ( Segata et al 2011 , Liu et al 2022 ). Thus, we run GDmicro with the “batch_run” mode in this experiment.…”

Progress on network modeling and analysis of gut microecology: a review

“…1e ). Recent developments include using deep reinforcement learning to reward and penalize models based on improved performance, aiding biomarker identification and colony-driven node analysis ( 43 , 44 ). However, it is essential to recognize that RL approaches in microbiome research are still nascent and face challenges, such as the intricate modeling of microbiome-host interactions and the limited availability of large-scale experimental data to train deep RL models ( 45 ).…”