Arsenic stress on soil microbial nutrient metabolism interpreted by microbial utilization of dissolved organic carbon.

In a 120-day microcosm incubation experiment, we investigated the impact of arsenic contamination on soil microbial nutrient metabolism, focusing on carbon cycling processes. Our study encompassed soil basal respiration, key enzyme activities (particularly, ß-1,4-N-acetylglucosaminidase and phosphatases), microbial biomass, and community structure. Results revealed a substantial increase (1.21-2.81 times) in ß-1,4-N-acetylglucosaminidase activities under arsenic stress, accompanied by a significant decrease (9.86%-45.20%) in phosphatase activities (sum of acid and alkaline phosphatases). Enzymatic stoichiometry analysis demonstrated the mitigation of microbial C and P requirements in response to arsenic stress. The addition of C-sources alleviated microbial C requirements but exacerbated P requirements, with the interference amplitude increasing with the complexity of the C-source. Network analysis unveiled altered microbial nutrient requirements and an increased resistance process of microbes under arsenic stress. Microbial carbon use efficiency (CUE) and basal respiration significantly increased (1.17-1.59 and 1.18-3.56 times, respectively) under heavy arsenic stress (500 mg kg-1). Arsenic stress influenced the relative abundances of microbial taxa, with Gemmatimonadota increasing (5.5-50.5%) and Bacteroidota/ Nitrospirota decreasing (31.4-47.9% and 31.2-63.7%). Application of C-sources enhanced microbial resistance to arsenic, promoting cohesion among microorganisms. These findings deepen our understanding of microbial nutrient dynamics in arsenic-contaminated areas, which is crucial for developing enzyme-based toxicity assessment systems for soil arsenic contamination.

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data.

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.