Establishing the relationship between molecular biomarkers and biotransformation rates : Extension of knowledge for dechlorination of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs)

Waseem, H. and Ali, J. and Syed, J.H. and Jones, K.C. (2020) Establishing the relationship between molecular biomarkers and biotransformation rates : Extension of knowledge for dechlorination of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs). Environmental Pollution, 263 (A): 114676. ISSN 0269-7491

Abstract

Anaerobic reductive treatment technologies offer cost-effective and large-scale treatment of chlorinated compounds, including polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs). The information about the degradation rates of these compounds in natural settings is critical but difficult to obtain because of slow degradation processes. Establishing a relationship between biotransformation rate and abundance of biomarkers is one of the most critical challenges faced by the bioremediation industry. When solved for a given contaminant, it may result in significant cost savings because of serving as a basis for action. In the current review, we have summarized the studies highlighting the use of biomarkers, particularly DNA and RNA, as a proxy for reductive dechlorination of chlorinated ethenes. As the use of biomarkers for predicting biotransformation rates has not yet been executed for PCDD/Fs, we propose the extension of the same knowledge for dioxins, where slow degradation rates further necessitate the need for developing the biomarker-rate relationship. For this, we have first retrieved and calculated the bioremediation rates of different PCDD/Fs and then highlighted the key sequences that can be used as potential biomarkers. We have also discussed the implications and hurdles in developing such a relationship. Improvements in current techniques and collaboration with some other fields, such as biokinetic modeling, can improve the predictive capability of the biomarkers so that they can be used for effectively predicting biotransformation rates of dioxins and related compounds. In the future, a valid and established relationship between biomarkers and biotransformation rates of dioxin may result in significant cost savings, whilst also serving as a basis for action.