Predicting pesticide biodegradation potential from microbial community composition: new tools for bioremediation
Résumé
Bioaugmentation is receiving increasing attention as a green technology to treat contaminated
areas by inoculating specific biodegrading microorganisms. However, our understanding of the
role of microbial community composition and structure in the expression of contaminant
degradation potential is yet to improve. It could help making wise choice for microorganisms –
community or specific strain – to be inoculated in contaminated soils with consideration to their
indigeneous microbiota.
Here we tried to predict the microbial degradation of two herbicides, glyphosate and
isoproturon by means of penalized regression and machine learning methods routinely used in
genomic selection. To this end, we conducted experimental modifications of these two
herbicides degrading communities by applying biocide treatments coupled with serial dilutions.
We then applied three selected genomic selection methods (i.e. Ridge Regression, LASSO and
Random Forest) on these community variants to link their OTUs composition to their herbicide
degradation capacities.
Resulting predictions power is compelling with more than 80% correlation between predicted
and actual herbicide degradation capacities. Moreover, OTUs detected as having an impact on
herbicide degradation were confronted to literature and validated. To go further and test the
robustness of our methods, an experimental validation of the theoretical prediction was set up.
Mixed resulting prediction quality calls for promising advances in the field of soil bioremediation
with the need of further improvement. Finally, futures applications in a bioremediation
perspective are considered.