Environmental microbiomes

Soil microbiomes ensure plant health and water quality and are essential for the remediation of pollution. Many soils are contaminated by nutrient excess, organic compounds or metals, as a result of human activities. WP4 strives to better understand the dynamics of soil microbiomes, and to develop technologies to restore damaged soil microbiomes back to a healthy state.

In contrast to soils, engineered environmental systems such wastewater treatment plants (WWTPs) offer a setting for experimental and process interventions. WWTPs represent the most widespread ‘artificial’ environmental microbiome, operating world-wide to remove carbon, nitrogen as well as phosphorous from liquid waste streams and to prevent contamination of the environment.

The objectives of WP4 are to:

  • Understand the principles of microbiome engineering in soil and wastewater treatment plants
  • Achieve targeted complementation of xenobiotic metabolism in soil microbiomes to restore soil health
  • Achieve targeted nitrogen and phosphorous removal in wastewater treatment granule microbiomes

WP4 studies wastewater granules, a soil community that transforms toxic compounds, and an agricultural topsoil. WP4 seeks to reveal the inner workings of microbiomes relevant to the remediation of polluted soils and the treatment of wastewater. Our findings will help to streamline soil remediation and promote energy efficiency in wastewater treatment.

Work Package Leader
Prof. Rizlan Bernier-Latmani

Latest publications

Environmental connectivity controls diversity in soil microbial communities
Dubey, M., Hadadi, N., Pelet, S., Carraro, N., Johnson, D. R., van der Meer, J. R. (2021).
doi: 10.1038/s42003-021-02023-2
Variability in arsenic methylation efficiency across aerobic and anaerobic microorganisms
Viacava, K., Lederballe Meibom, K., Ortega, D., Dyer, S., Gelb, A., Falquet, L., Minton, N.P., Mestrot, A., Bernier-Latmani (2020).
doi: 10.1021/acs.est.0c03908
Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data
Özel Duygan, B.D., Hadadi, N., Farizah Babu, A., Seyfried, M., van der Meer, J.R. (2020).
doi: 10.1038/s42003-020-1106-y