Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water.

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

The impact of Rhodiola rosea on the gut microbial community of Drosophila melanogaster.

BACKGROUND: The root extract of Rhodiola rosea has historically been used in Europe and Asia as an adaptogen, and similar to ginseng and Shisandra, shown to display numerous health benefits in humans, such as decreasing fatigue and anxiety while improving mood, memory, and stamina. A similar extract in the Rhodiola family, Rhodiola crenulata, has previously been shown to confer positive effects on the gut homeostasis of the fruit fly, Drosophila melanogaster. Although, R. rosea has been shown to extend lifespan of many organisms such as fruit flies, worms and yeast, its anti-aging mechanism remains uncertain. Using D. melanogaster as our model system, the purpose of this work was to examine whether the anti-aging properties of R. rosea are due to its impact on the microbial composition of the fly gut. RESULTS: Rhodiola rosea treatment significantly increased the abundance of Acetobacter, while subsequently decreasing the abundance of Lactobacillales of the fly gut at 10 and 40 days of age. Additionally, supplementation of the extract decreased the total culturable bacterial load of the fly gut, while increasing the overall quantifiable bacterial load. The extract did not display any antimicrobial activity when disk diffusion tests were performed on bacteria belonging to Microbacterium, Bacillus, and Lactococcus. CONCLUSIONS: Under standard and conventional rearing conditions, supplementation of R. rosea significantly alters the microbial community of the fly gut, but without any general antibacterial activity. Further studies should investigate whether R. rosea impacts the gut immunity across multiple animal models and ages.