Analysis of richness and diversity of soil bacterial and microbial communities
Alpha diversity analysis
The results indicated that there was no significant difference in coverage between the treatments and an average value > 0.98 indicated a high coverage rate of the soil sample library (Table 1). The Chao, Shannon, Simpson, and Observed species indices values of low and medium-dose CC-treated groups were higher than CK. Furthermore, the Chao, Shannon, and Observed species indices values of high-dose CC treatment were lower than CK. Moreover, the values of Chao, Shannon, Simpson, and Observed species indices of low-concentration CC treatment were higher than other concentration treatments. There was no significant difference in the Simpson index, but a high CC concentration group indicated lower values than CK, indicating uneven species distribution and high diversity in the CC-treated communities. Overall, the application of different CC concentrations can increase the richness and diversity of peanut rhizosphere microbial communities, with low CC concentrations as the most optimal treatment.
Table 1
Alpha Diversity Index of Soil Samples
| Group | Coverage | Chao1 | Observed species | Shannon | Simpson |
| D | 0.99283a | 2512.39a | 2450.9a | 9.27990a | 0.98565a |
| M | 0.98960a | 2502.79a | 2375.2a | 8.52057b | 0.97555a |
| G | 0.99288a | 1709.12c | 1633.5c | 6.81162d | 0.94544a |
| CK | 0.99251a | 2118.82b | 2055.1b | 7.83026c | 0.96499a |
Note: Different lowercase letters indicate significant differences between treatments at the 5% level.
β - Diversity Analysis
The distance matrix and PCoA analysis (Fig. 1) revealed that the contribution rate of the first principal component (PCo1) was 31.5% and that of the second (PCo2) was 27.4%. Furthermore, their cumulative variance contribution rate reached 58.9%, indicating that they can reflect most of the information in the sample. Moreover, the different sample projection distances on the coordinate axis indicated that compared to CK, the D, M, and G treatments substantially altered the community structure of peanut rhizosphere bacteria (p < 0.05).
Analysis of Soil Bacterial and Microbial Community Composition
The top 5 dominant bacterial phyla in the rhizosphere soil of each treatment (Fig. 2) were Proteobacteria (36.67% ~ 56.22%), Firmicutes (16.38% ~ 38.16%), Acidobacteriota (4.05% ~ 8.77%), Bacteroidetes (4.19% ~ 6.99%), and Gematimonadota (3.23% ~ 7.39%) (Fig. 2A). The abundance of Proteobacteria in the D, M, and G treated group was significantly lower (33.04%, 34.77%, and 24.26%, respectively) than the CK. However, Firmicutes (64.47, 43.28, and 132.97%) and Bacteroidetes (66.83, 66.35, and 2.15%) had higher abundance than CK. The low- and medium-dose CC-treated Acinetobacter (36.36% and 39.87%) and Bacteroidetes (46.92% and 16.30%) phyla had higher abundance than the control. Furthermore, in the high CC treatment, their abundance was lower than the control (35.40% and 35.79%) (Fig. 2B).
At the genus level, the proportion of bacterial communities without identified genera was relatively high (Fig. 3). The dominant bacterial genera in the rhizosphere soil of each treatment were Clostridium_sensu_stricto_1, Rahnella1, Pseudomonas, Clostridium sensu-stricto_13, Azovibrio, Polaromonas, RB41, Flavobacterium, and Sphingomonas, Bacillus (Fig. 3A). Compared with CK, the D, M, and G treatments had reduced abundance of Pseudomonas (50.58, 7.27, and 38.95%), Polarimonas (97.59, 98.27, and 87.9%), and Azovibrio (72.87, 91.36, and 96.28%), whereas the abundance of Clostridium-sensu_stricito-13 (36.12, 71.74, and 8.72%) and Clostridium-sensu_stricito-1 (172.4, 55.2, and 792.8%) was increased. Moreover, compared to the control, Rahnella1 abundance in the D and M groups was decreased (97.83% and 61.5%, respectively), while increased in the G group (147.30%) (Fig. 3B).
Soil bacterial and microbial community OTUs clustering analysis
The treatments OTUs of D, M, G, and CK were 5583, 5430, 3910, and 4740, respectively (Fig. 4) and the order of OTU from high to low was D > M > CK > G. The number of OTUs decreased with the increase of CC concentration, indicating that low and medium CC concentrations can increase OUT values in peanut rhizosphere microbial community. Moreover, the low-concentration treatment had the highest OTU values, whereas the high concentration indicated reduced OTUs of peanut rhizosphere microbial communities.
In Fig. 5, the horizontal axis (PCo1) indicates 62.9% of the sample differences, and the vertical axis (PCo2) depicts 26.6% of the sample differences. Furthermore, according to the cluster analysis, the D and M treatments were repeated thrice and clustered in the same branch, whereas CK was clustered with D and M in the same branch. Furthermore, the distance between CK and D-treated samples was relatively close, with little difference, whereas the distance between G-treated samples and the other two branches was relatively far and had significant differences, and therefore were clustered in different branches. The use of different CC concentrations affected the peanut’s rhizosphere soil environment, thereby significantly altering the diversity of the bacterial community. The score coefficients of D and M treatments indicated small dispersion, while the score coefficients of G and CK treatments varied greatly (Fig. 5). Altogether, it was observed that the soil microbial community remains stable without CC use or in low to medium-dose CC spraying, as high CC concentration was observed to cause unstable soil microbial communities.
Analysis of Species Differences in Soil Bacterial Microbial Communities
The LEfSe analysis (Fig. 6) identified 100 bacterial biomarkers at the phylum, class, order, family, genus, and species levels. Of these, 9 were identified at the genus level (based on LDA log scores > 4), including Azovibrio, Bacillus, Clostridium-sensu-stricto-1, Polaromonas, Sphingobium, Clostridium-sensu-stricto-13, Sphingomonas, Massilia, and Rahnella1.
The heatmap of species composition (Fig. 7) indicated that the most abundant species in the CK group were Methylotener, Polarimonas, and Azovibrio, in the D group, were Bacillus, Massilia, Gemmatimonas, Variovorax, Sphingomonas, and Arenimonos, in the M group were Vicinamibacteraceae, Sphingobium, Rokubacteriales, and Clostridium-sensu-stricto_13, and in the G group were Clostridium-sensu-stricto_1 and Rahnella1.
Functional prediction of metabolites
The metabolic pathway (Fig. 8) included 4 cellular processing pathways, 3 environmental information processing pathways, 4 genetic information processing pathways, 6 human disease pathways, 11 metabolism pathways, and 6 biological systems pathways. The main concentrated metabolic pathways included the metabolisms of nucleotides, terpenoids, polyketides, other amino acids, cofactors, vitamins, lipids, glycan biosynthesis, energy, carbohydrates, xenobiotics, amino acids, and other secondary metabolites.
Compared to the CK group, the positive regulatory pathways were significantly different (p < 0.001, LogFC > 1) in the D group and included naphthalene degradation and spliceosome, whereas the negative regulatory pathways involved the degradation of polycyclic aromatic hydrocarbons (Fig. 9). Furthermore, the markedly different positive regulatory pathways observed in the M group included malaria, primary bile acid biosynthesis, hypertrophic cardiomyopathy, and other polysaccharide degradation pathways, whereas the negative regulatory pathways involved cell apoptosis and vasopressin-modulating water reabsorption pathways. The G concentration treatment was associated with the following positive regulatory pathways: phosphotransferase system, bacterial invasion of epithelial cells, secondary bile acid biosynthesis, bacterial invasion of epithelial cells, and bacterial invasion of epithelial cells. The G group negative regulatory pathways included D-arginine and D-ornithine metabolism, cell apoptosis, and styrene degradation pathways.