Image: Marlene Johansson
Image: Marlene Johansson
Research project It is crucial to deepen our understanding of the potential impacts of anthropogenically-derived pollutants on aquatic environments and their food webs. In this project we will develop a cost-time efficient standardized method for a rapid assessment of contaminated sediments using structural and functional properties of natural microbial communities as bioindicators, with Hg as model pollutant.
Chemical pollution from anthropogenic activities is of major concern for different environmental authorities worldwide. Environmental pollutants are accumulated in aquatic ecosystems at concentrations well above ambient levels, which poses detrimental effects on the resident biota and ecosystem functioning. In addition, in a changing global climate where the transport and partitioning of hazardous chemicals are expected to be altered, it is crucial to deepen our understanding of the potential impacts of anthropogenically-derived pollutants on aquatic environments and their food webs.
Bioindication is an important tool used in the implementations of the EU environmental legislation for the assessment of the quality status of different natural environments. Due to their vast metabolic versatility, microbes inhabit a wide range of environments, including contaminated sediments. Thus, microbial communities may be used as rapid and sensitive indicators of environmental disturbance induced by the occurrence of contaminants.
In the project we develop a cost-time efficient standardized method for a rapid assessment of contaminated sediments using structural and functional properties of natural microbial communities as bioindicators, with Hg as model pollutant. A multi-omics (DNA/RNA) approach will be developed to conduct in situ molecular analyses from field and laboratory studies where Oxford Nanopore sequencing technologies will be employed. We will identify microbial taxa, functional genes, and metabolic pathways characteristic of the naturally occurring communities, and capable of responding to loads of pollutants under a wide range of physico-chemical profiles. Relevant genes and microbial taxa will be integrated in a modelling approach to link patterns of gene expression and community structure to loads of pollutants and bioaccumulation in the food web, which can have important implications in pollutant tracking, monitoring and risk assessment.
