Supervisor: prof Ain Heinaru ja prof Jaak Truu
Opponent: Olli Tuovinen, professor, Ohio Ülikool
The increasingly industrialized global economy that has emerged over the last century has led to dramatically elevated releases of anthropogenic chemicals into the environment impacting whole ecosystems (i.e. the Gulf of Mexico oil spill), drinking water supplies or directly human health. Concurrently with increasing pollution levels, avid interest in developing strategies for remediation of environmental contaminants has emerged. As classic "suck and truck" strategies followed by off-site treatments are expensive, the in situ bioremediation processes like monitored natural attenuation (MNA), biostimulation, bioaugmentation and rhizoremediation have become an attractive way to rehabilitate contaminated sites. These bioremediation techniques rely extensively on the presence of an active microbial degrader population able to transform the bioavailable contaminants into harmless or less dangerous compounds. Bioremediation processes need to be continuously monitored to ensure their efficiency and sustainability. One of the increasingly used methods in bioremediation monitoring is quantitative polymerase chain reaction (qPCR) which enables quantification of the abundance of gene markers within the environment. The quantitative data generated can be used to relate variation in gene abundances with variation in abiotic and biotic factors and process rates. However, target gene quantification results from environmental samples depend on a number of factors such as the method and quality of DNA extraction, the subsequent presence of inhibitory substances in the extracted microbial community DNA, the qPCR chemistry used, the amplification efficiency achieved and the overall quality of the resultant datasets. We evaluated the scope of these aspects affecting gene enumerations by qPCR and found that modifications in qPCR workflow and analysis procedure steps can significantly influence the target gene quantification and normalization results from environmental samples and consequently also bioremediation related decision-making. For environmental monitoring purposes the most suitable method workflow relating to the characteristics of individual experiment conducted should be chosen to ensure the quality and truthfulness of obtained results.