Meta-omic characterization of micropollutant biodegradation in combined reactors (anaerobic-anoxic-aerobic) applied to the sewage treatment

• Grant number: 25/04269-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: May 01, 2025
• End date: April 30, 2027
• Field of knowledge: Biological Sciences - Ecology
• Principal Investigator: Valeria Maia Merzel
• Grantee: Henrique de Souza Dornelles
• Host Institution: Centro Pluridisciplinar de Pesquisas Químicas, Biológicas e Agrícolas (CPQBA). Universidade Estadual de Campinas (UNICAMP). Paulínia , SP, Brazil
• Associated research grant: 22/10615-1 - Consolidation of the biorefinery concept applied to the biological treatment of wastewater and solid wastes, AP.TEM

Abstract

Traditional wastewater treatment plants often fail to efficiently remove organic micropollutants, which include pharmaceuticals and personal care products, posing risks to environmental and human health. Wastewater treatment plants, especially the biological process, often lead to the formation of transformation products that also end up reaching various environmental compartments and posing risks to human and environmental safety. This project will investigate the microbial taxonomic and functional profiles of biological reactors applied to the degradation of micropollutants present in sanitary sewage. The treatment system in this study combines anaerobic-anoxic-aerobic reactors, providing a unique ecosystem to enhance the biodegradation of the compounds of interest. This project will address the microbial communities established in the reactors, shedding light on the microorganisms and active metabolisms playing crucial roles in the pollutant removal, providing subsidies to enhance/optimize the application of the studied system. To this end, sludge samples from the reactors will be collected and subjected to DNA and RNA extraction for subsequent sequencing, using metagenomics and metatranscriptomics approaches. The datasets generated in the sequencing will be processed using bioinformatics tools in a Linux OS environment using the Python language, while subsequent statistical analyses will be performed using the R language.