Single-cell Raman and functional gene analyses reveal microbial P solubilization in agriculture waste-modified soils

Abstract

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 D₂O labeling (Raman-D₂O) 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 D₂O. 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.

Keywords: CNP functional genes; D2O isotope labeling; phosphate‐solubilizing microorganisms; single‐cell Raman.

Figure 1

Changes in soil properties following rapeseed meal (RM) amendment. Effects of RM addition on soil pH (A, D), Olsen P concentration (B, E), and dissolved organic carbon (DOC) (C, F). Data from three replicates (n = 3) are represented as means ± SD. One‐way analysis of variance was used to test the significant difference between samples. DH, DZ and QY represented the soils sampled from Donghu, Dezhou and Qiyang, respectively. The numbers 1 and 2 represent the first (30 days) and the second (60 days) sampling time, respectively. ***p < 0.001; ns, no significance.

Figure 2

The effect of RM addition on phenotypic microbial P solubilization function. (A) Workflow for in situ phosphate‐solubilizing microorganism (PSM) identification via single‐cell Raman D₂O. (B) Typical single‐cell Raman spectra of PSMs and non‐PSMs. (C) Distribution of C–D ratios measured from over 100 randomly selected single cells in DH, DZ, and QY soils amended with and without RM addition. Each point shows a measurement of a single cell. The red line at 10% shows the threshold for PSM identification. It was calculated as the mean + 3 × SD of C−D ratios from randomly selected bacteria incubated without D₂O. (D) The effects of RM addition on the abundance of PSMs in DH, DZ, and QY soils identified by single‐cell Raman D₂O. *p < 0.05, ***p < 0.001, ****p < 0.0001; ns, no significance.

Figure 3

The effect of RM addition on soil bacterial community. (A) Soil bacterial community composition (mean, n = 3) at the phylum level. Low‐abundance taxa were classified as “Others”. The alpha diversity (Chao 1 index) of bacterial communities at the first (30 days) (B) and second (60 days) (C) sampling time. ***p < 0.001; ns, no significance.

Figure 4

Changes of functional gene profiles following RM amendment. (A) Heat map of CNP functional gene profiles. The size of circles represents the relative abundance of CNP functional genes. The changes of the relative abundance of CNP functional gene profiles in DH (B, C), DZ (D, E), and QY (F, G) soils with and without RM application are shown. The numbers 1 and 2 represent the first (30 days) and second (60 days) sampling time, respectively. The differences were calculated as the relative abundance of CNP cycling genes between soil and soil + RM treatments. *p < 0.05, **p < 0.01, ***p < 0.001; ns, no significance.

Figure 5

The correlation between soil bacterial communities and functional gene profiles. Procrustes analysis and Mantel test reveal the correlation between CNP functional genes and bacterial communities on the basis of Bray–Curtis dissimilarity metrics in DH (A), DZ (B), and QY (C) soils, respectively.

Figure 6

The correlations between bacterial communities, functional gene profiles, and the abundance of phosphate‐solubilizing microorganisms (PSMs). (A) Correlations between the relative abundances of bacteria at the phylum level and the proportions of active PSMs (*p < 0.05). (B) Regression relationships between the abundances of Myxococcota and the proportions of active PSMs. (C) Correlations between the relative abundances of functional genes and the proportions of PSMs.