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Updated: Jan 7, 2026

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR
Published on: June 11, 2016
Differential decay of host-associated bacterial and mitochondrial DNA markers in sediment microcosms
1Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL 36849, USA.
Abstract:
Monitoring microbial water quality has traditionally focused on the water column. Sediment, however, has been shown to be a reservoir for fecal bacteria in aquatic environments, and their persistence in sediment remains understudied. In this study, we examined the persistence of host-associated genetic markers in freshwater sediment using laboratory microcosms and quantitative polymerase chain reactions (qPCR). The genetic markers included bacterial and mitochondrial DNA (mtDNA) markers associated with humans (HF183 and HcytB), cattle (CowM3 and QMIBo), and chickens (LA35 and Chicken-ND5), as well as the general Bacteroidales marker AllBac. Sediment microcosms constructed in sentinel chambers were spiked with sewage, cattle feces, or poultry litter, and the experiment was conducted over a 40-day period. The results showed that all markers followed either a first-order or biphasic decay pattern, and all host-associated bacterial markers decayed more rapidly than their corresponding mtDNA markers. Additionally, the general marker AllBac remained stable throughout the experiment and showed greater persistence than host-associated bacterial markers. The inactivation of cultivable E. coli was significantly and positively correlated with most markers except for cattle-associated mtDNA. Furthermore, the time to reach one log reduction for E. coli from cattle feces and poultry litter was significantly longer than that from sewage. These findings provide valuable insights into the fate of bacterial and mtDNA markers in sediment, underscoring the importance of considering marker type and fecal source when assessing sediment contamination. Knowledge of the variability in inactivation rates of genetic markers and E. coli will lead to the development of more accurate predictive models for microbial risk assessment.
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