Quantifyingin situgrowth rate of a filamentous bacterial species in activated sludge using rRNA:rDNA ratio

Abstract

If the in situ growth rate of filamentous bacteria in activated sludge can be quantified, researchers can more accurately assess the effect of operating conditions on the growth of filaments and improve the mathematical modeling of filamentous bulking. We developed a method to quantify the in situ specific growth rate of Sphaerotilus natans (a model filament) in activated sludge using the species-specific 16S rRNA:rDNA ratio. Primers targeting the 16S rRNA of S. natans were designed, and real-time PCR and RT-PCR were used to quantify DNA and RNA levels of S. natans, respectively. A positive linear relationship was found between the rRNA:rDNA ratio (from 440 to 4500) and the specific growth rate of S. natans (from 0.036 to 0.172 h) using chemostat experiments. The in situ growth rates of S. natans in activated sludge samples from three water reclamation facilities were quantified, illustrating how the approach can be applied in a complex environment such as activated sludge.

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“…Studies of the last few years are dedicated to exploring more techniques (Martienssen & Adonadaga, 2015;Sheik et al, 2015;Nguyen et al, 2016;Deepnarain et al, 2020;Jiang et al, 2021) confirming that if the in-situ identification and growth rate of filamentous bacteria in the activated sludge can be determined researches can more accurately assess the effect of operating conditions on filament growth and improve the mathematical modeling of bulking. For this purpose, quantification methods of the in-situ growth rate of Sphaerotilus natans (as a model of filamentous bacteria) in activated sludge using the specific 16S rRNA/ rDNA ratio of the species are being explored.…”

A novel culture-independent method to search unknown dominant bacterial groups and its application to microbial analysis of membrane bioreactors

“…Most reports have focused mainly on the comparative study of overall microbial composition (23, 26-28, 32), and their changes in the reactor (29, 30). With respect to the well-known specific gene, or bacteria, their numbers were estimated by using real-time PCR (33-35), or by FISH (24, 25). There have been few reports to show whether numerically dominant specific microorganisms, which might affect the performance of the reactor, existed in the reactor or not until now (1, 2).…”