Genome-based identification of phosphate-solubilizing capacities of soil bacterial isolates.
Xiaoqing ChenYiting ZhaoShasha HuangJosep PeñuelasJordi SardansLei WangBang-Xiao ZhengPublished in: AMB Express (2024)
Identifying genomic markers for phosphate-solubilizing bacteria (PSB) is vital for advancing agricultural sustainability. This study utilizes whole-genome sequencing and comprehensive bioinformatics analysis, examining the genomes of 76 PSB strains with the aid of specialized genomic databases and analytical tools. We have identified the pqq gene cluster, particularly the pqqC gene, as a key marker for (P) solubilization capabilities. The pqqC gene encodes an enzyme that catalyzes the conversion of precursors to 2-keto-D-gluconic acid, which significantly enhances P solubilization in soil. This gene's importance lies not only in its biochemical function but also in its prevalence and effectiveness across various PSB strains, distinguishing it from other potential markers. Our study focuses on Burkholderia cepacia 51-Y1415, known for its potent solubilization activity, and demonstrates a direct correlation between the abundance of the pqqC gene, the quantitative release of P, and the production of 2-keto-D-gluconic acid over a standard 144-h cultivation period under standardized conditions. This research not only underscores the role of the pqqC gene as a universal marker for the rapid screening and functional annotation of PSB strains but also highlights its implications for enhancing soil fertility and crop yields, thereby contributing to more sustainable agricultural practices. Our findings provide a foundation for future research aimed at developing targeted strategies to optimize phosphate solubilization, suggesting areas for further investigation such as the integration of these genomic insights into practical agricultural applications to maximize the effectiveness of PSB strains in real-world soil environments.
Keyphrases
- copy number
- genome wide
- escherichia coli
- genome wide identification
- climate change
- systematic review
- risk assessment
- randomized controlled trial
- healthcare
- heavy metals
- bioinformatics analysis
- young adults
- palliative care
- mass spectrometry
- gene expression
- risk factors
- big data
- quantum dots
- microbial community
- artificial intelligence
- cancer therapy
- antibiotic resistance genes