Single-cell Raman and functional gene analyses reveal microbial P solubilization in agriculture waste-modified soils.
Hongzhe LiJiazhi DingLongji ZhuFei XuWenjing LiYanpo YaoLi CuiPublished in: mLife (2023)
Application of agricultural waste such as rapeseed meal (RM) is regarded as a sustainable way to improve soil phosphorus (P) availability by direct nutrient supply and stimulation of native phosphate-solubilizing microorganisms (PSMs) in soils. However, exploration of the in situ microbial P solubilizing function in soils remains a challenge. Here, by applying both phenotype-based single-cell Raman with D 2 O labeling (Raman-D 2 O) and genotype-based high-throughput chips targeting carbon, nitrogen and P (CNP) functional genes, the effect of RM application on microbial P solubilization in three typical farmland soils was investigated. The abundances of PSMs increased in two alkaline soils after RM application identified by single-cell Raman D 2 O. RM application reduced the diversity of bacterial communities and increased the abundance of a few bacteria with reported P solubilization function. Genotypic analysis indicated that RM addition generally increased the relative abundance of CNP functional genes. A correlation analysis of the abundance of active PSMs with the abundance of soil microbes or functional genes was carried out to decipher the linkage between the phenotype and genotype of PSMs. Myxococcota and C degradation genes were found to potentially contribute to the enhanced microbial P release following RM application. This work provides important new insights into the in situ function of soil PSMs. It will lead to better harnessing of agricultural waste to mobilize soil legacy P and mitigate the P crisis.
Keyphrases
- heavy metals
- single cell
- genome wide
- high throughput
- risk assessment
- sewage sludge
- microbial community
- rna seq
- human health
- genome wide identification
- antibiotic resistance genes
- public health
- climate change
- dna methylation
- bioinformatics analysis
- copy number
- label free
- raman spectroscopy
- mass spectrometry
- high resolution
- machine learning
- hepatitis c virus
- deep learning
- human immunodeficiency virus
- high speed
- municipal solid waste