Associations between wastewater gut microbiome and community obesity rates: Potential microbial biomarkers for surveillance

Abstract

Gut microbes are crucial for human health, which are usually accumulated in urban wastewater systems. Seven wastewater treatment plants in Australia with distinct population obesity rates between 18% and 33% were selected for wastewater sampling and analysis. Human gut microbiome were detected using metagenomic sequencing to investigate their associations with the community obesity rate. To unravel this complex relationship, a range of algorithm models, including linear discriminant analysis effect size (LEfSe), similarity percentage analysis (SIMPER), statistical analysis of metagenomic profiles (STAMP), linear models for microarray and RNA-Seq data analysis (LIMMA), Relief, ratio approach for identifying differential abundance (RAIDA), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), Boruta, DESeq2 and analysis of compositions of microbiomes with bias correction (ANCOM-BC), were used to identify potential bacterial biomarkers for obesity in the wastewater microbiome. Among these algorithm models, LEfSe, LIMMA, SIMPER and SVM are effective in identifying multiple microbial biomarkers. Specific human gut microbes, including Ruminococcus_E, Agathobacter, Fusicatenibacter, Anaerobutyricum, Blautia_A and Neisseria, were identified as potential consensus microbial biomarkers for obesity in the population. A high obesity rate is mainly characterized by a high abundance of pathogenic bacteria and microorganisms associated with xenobiotic biodegradation and metabolism, endocrine and metabolic diseases, and transcription pathways. This study underscores the innovative potential of leveraging human gut microbes in wastewater as biomarkers for monitoring obesity levels across communities, offering a novel, cost-effective, and indirect approach to public health surveillance.

Keywords

• Human health
• LEfSe and ANCOM-BC
• Metagenomics
• Pathogenic bacteria
• SIMPER and LASSO
• LIMMA and SVM