Background
Wastewater-based epidemiology (WBE) is a tool that provides a novel disease surveillance strategy cost-effectively. Wastewater treatment plants (WWTPs) are hotspots for all anthropogenic actions and thus combine at the community level a myriad of potential disease markers present in human excreta. Currently, WBE approaches have evolved worldwide during the COVID-19 pandemic.
Goal
This study uses metagenomic and machine learning approaches to unveil and exploit the population health data the untreated community wastewater contains. The MiWaGen project will explore the potential of total metagenome from WWTPs as a trusted source of population health data at the community level. We hypothesize that when properly maintained, wastewater-based metagenomic sequence databanks could provide an essential comprehensive new data source of the community microbiome and related disease risk signatures to be deployed for population health research. The expected research results are 1.) optimized laboratory and bioinformatic methodologies that allow us to 2.) annotate prokaryotic, eukaryotic and viral sequences from untreated community wastewater and 3.) identify those metagenomic and virome signatures that reflect and predict population-level disease risks.
Impact
The project will collect a representative set of wastewater samples and their metagenomic signatures using high-throughput deep sequencing. The cutting-edge development of sequencing technology and new bioinformatics tools enables the comprehensive study of very complex mixtures of genetic material in the wastewater. MiWaGen will map the population-level spatial and temporal differences in the wastewater metagenomic signatures and investigate the metagenomic risk indicators for communicable and chronic diseases by comparing wastewater data to health registers. The bold objectives of MiWaGen also include testing a machine learning model to forecast disease incidence from metagenomic wastewater signatures.
Partners and co-operators
Tampere University will coordinate the project with the Finnish Institute for Health and Welfare (THL) as a consortium partner.
Funding
Academy of Finland in 2023-2027.