At present, the oestrogens detected in the environment have been classified as group 1 carcinogens by the World Health Organization. They have obvious effects on organisms at extremely low environmental concentration (1.0 ng/L) and easily accumulate in the ecosystem, which has adverse effects on the environment and ecological health. The use of microbial degradation to remove steroid estrogens from polluted environments has received increasing attention. In this study, a bacterium capable of degrading 17b-estradiol was isolated from a sewage treatment plant in Jilin, China, and identified as Serratia nematodiphila DH-S01. The results of degradation experiments showed that after culturing the bacteria for 4 days, the degradation rate of oestrone and 17b-estradiol at 15 mg/L reached 93.47% and 93.2%, respectively. Genome-wide sequencing showed that the genome of strain DH-S01 consists of a single circular chromosome, 5,256,558 bp in length, which contains 4,874 predicted coding genes. Based on genome annotation, high abundance genes are related to the metabolism of terpenoids and polyketides. Nine types of sterol-and oestrogen-degrading enzymes were annotated in this strain, and the existence and expression of the enzymes were analyzed by polymerase chain reaction (PCR) and reverse transcription polymerase chain reaction (RT-PCR). Comparative genomic analysis showed that there are genes encoding eight enzymes in the common genes of the four Serratia strains, highlighting the potential of the other three Serratia strains to degrade steroid estrogen.
Chemotactic bacteria sense and respond to temporal and spatial gradients of chemical cues in their surroundings. This phenomenon plays a critical role in many microbial processes such as groundwater bioremediation, microbially enhanced oil recovery, nitrogen fixation in legumes, and pathogenesis of the disease. Chemical
In natural systems bacteria are exposed to many chemical stimulants; some attract chemotactic bacteria as they promote survival, while others repel bacteria because they inhibit survival. When faced with a mixture of chemoeffectors, it is not obvious which direction the population will migrate. Predicting this direction requires an understanding of how bacteria process information about their surroundings. We used a multiscale mathematical model to relate molecular level details of their twocomponent signaling system to the probability that an individual cell changes its swimming direction to the chemotactic velocity of a bacterial population. We used a microfluidic device designed to maintain a constant chemical gradient to compare model predictions to experimental observations. We obtained parameter values for the multiscale model of Escherichia coli chemotaxis to individual stimuli, α-methylaspartate and nickel ion, separately. Then without any additional fitting parameters, we predicted bacteria response to chemoeffector mixtures. Migration of E. coli toward α-methylaspartate was modulated by adding increasing concentrations of nickel ion. Thus, the migration direction was controlled by the relative concentrations of competing chemoeffectors in a predictable way. This study demonstrated the utility of a multiscale model to predict the migration direction of bacteria in the presence of competing chemoeffectors.
In natural systems bacteria are exposed to many chemical stimulants; some attract chemotactic bacteria as they promote survival, while others repel bacteria because they inhibit survival. When faced with a mixture of chemoeffectors, it is not obvious which direction the population will migrate. Predicting this direction requires an understanding of how bacteria process information about their surroundings. We used a multiscale mathematical model to relate molecular level details of their two-component signaling system to the probability that an individual cell changes its swimming direction to the chemotactic velocity of a bacterial population. We used a microfluidic device designed to maintain a constant chemical gradient to compare model predictions to experimental observations. We obtained parameter values for the multiscale model of Escherichia coli chemotaxis to individual stimuli, α-methylaspartate and nickel ion, separately. Then without any additional fitting parameters, we predicted the response to chemoeffector mixtures. Migration of E. coli toward α-methylaspartate was modulated by adding increasing concentrations of nickel ion. Thus, the migration direction was controlled by the relative concentrations of competing chemoeffectors in a predictable way. This study demonstrated the utility of a multiscale model to predict the migration direction of bacteria in the presence of competing chemoeffectors.
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