Soil properties and plant growth indicators
The continuous cropping group (C) decreased soil pH and AK compared with the non-continuous cropping (N). EC, AP, and AN indicators were much higher in the C group regulative to the N group (Fig. 1a-e). In addition, the C soil exhibited much higher catalase, and urease contents, while the N group had higher sucrase, and acid phosphatase contents (Fig. 2f-i). We found that the plant height (Height), root dry (DW), root vitality (RA), and root surface area (RSA) in site with the C group were significantly lower than the group with N (Fig. 2j-m, Fig. S1,).
Alpha diversity of the microbial community
Alpha diversity of the bacterial and communities in these root compartment samples showed different OTU richness at 97% sequence similarity level. We evaluated the alpha diversity of different root compartments using the Chao1, Richness, Shannon, and Simpson indices to measure microbial community diversity and richness, respectively (Fig. 3, Table S4-5). The results showed that the Chao1, Simpson, and Shannon diversity indices of the bacterial communities differed significantly (p < 0.05) in the endophytic roots. In addition, the fungal community alpha diversity sample Sc was found to be significantly smaller (p < 0.05) than sample Sn (Fig. 2). The results showed that the diversity of the microbial community in the root chamber samples changed after continuous crop, with the continuous crop group being significantly lower than the non-continuous group. It indicated that cropping would affect the alpha diversity in plants and soil.
Beta diversity of the microbial community
For a better estimation of the distance relationship between these root compartment samples, based on the unweighted UniFrac distance matrix, the microbial β diversity was further evaluated. We found that the community composition of the microbiota differed in samples. The NMDS results showed that the OTU distribution of the root samples was quite different from other samples, and the bulk soil and rhizosphere soil were clustered together (Fig. 3c, d). The Spearman clustering heat map shows that endosphere (Bc and Bn) samples are correlated, samples from bulk soil (Sc and Sn) are correlated with samples from rhizosphere soil (Rc and Rn), and importantly bulk soil and rhizosphere soil differed significantly from the endosphere (Fig. 3a). For the fungal communities, a similar pattern was observed and the greatest differences were observed between samples Bn and Sc (Fig. 3b). There was a large difference in beta diversity between the C and N samples. Similar results were also seen for the distribution of groups in the non-metric multidimensional scaling (NMDS) ordination, suggesting that microbial community composition varies considerably across soils (Fig. 3c-d). Moreover, the difference between the two treatment groups in rhizosphere soil and bulk soil samples is smaller than that of the roots. The results from the MNDS ranked clustering (Fig. 3c-d) show that all five root compartment samples in groups C and N show a non-random spatial allocation.
Microbial community structure variations
This study also showed the composition of bacterial and fungal species from phylum to genera level in root endosphere, rhizosphere, and bulk soil samples subjected to the different types of soil treatment alternatives. The paradoxical color stripes in Figs. 4 and 5 of different samples reflect that the rhizosphere soil bacteria had conducted alternative colonization on different root compartments. The proportional abundance of dominant taxa changed under different cropping systems and root chamber conditions.
The operational taxonomic units (OTUs) of the different root compartments bacterial communities belong to 39 phyla, 113 classes, 259 orders, 413 families, and 846 genera. Bacterial community circos of phylum abundances and composition is shown in Fig. 4a. The top 10 bacterial phyla in terms of root compartments abundance included Proteobacteria, Actinobacteria, Bacteroidota, Acidobacteriota, Cyanobacteria, Chloroflexi, Patescibacteria, Gemmatimonadetes, Myxococcota, and Verrucomicrobiota. The total of these phyla accounted for more than 98.2% of the bacterial sequences. Proteobacteria and Actinobacteria account for 66% of the bacterial community and are the two largest phyla. The relative abundance of Proteobacteria increased in the continuous cropping, respectively, compared to levels in the non-continuous cropping, and the abundance of rhizosphere and endosphere is greater than that of bulk soil (Fig. 4c). Furthermore, the Abundance of Actinobacteriota decreased. Pseudomonas, Flavobacterium, and Pedobacter were significantly higher in abundance in the continuous cropping samples. In contrast to bulk soil, the rhizosphere and endosphere harbored a higher portion of Novosphingobium.
The fungal OTUs belonged to 7 phyla, 25 orders, 71 families, 163 families and 363 genera in the high-throughput sequencing results. A total of four phyla and one unidentified phylum were identified from the phylum level map in Fig. 3b. The four phyla were Ascomycota, Olpidiomycota, Basidiomycota, and Mortierellomycota. Ascomycota was dominant in continuous cropping soils, especially BC (84.0%), and Olpidiomycota was dominated by Bn (85.8%) and Rn (76.7%). The relative abundance of Tausonia and Fusarium increased in the continuous cropping, respectively, compared to levels in the non-continuous cropping, the Tausonia abundance of rhizosphere and bulk soil is greater than that of endosphere, and the Fusarium abundance of endosphere is greater than that of other treatment groups (Fig. 4d). Furthermore, the Abundance of Olpidium decreased.
In addition, we built four three-axis ternary plots for the most abundant phyla (Fig. 5). Each color circle represents a phylum level, with each corner of the triangle representing a root compartment sample. The dimension of each circle represents its weighted average. Ternary plots showed that many phyla were present in similar proportions at the three sites (bulk, rhizosphere, and endophytic) but that some were comparatively more abundant at a specific position (Fig. 5). In the microbial species, a larger number of phyla showed a corporation with either the bulk soils or rhizosphere soils of the root compartments than with the endophytic. The ternary plots of dominant phyla revealed that different root compartments contained special phyla (Fig. 5). The distribution of other phyla of bacteria in each root compartment is not much different. Furthermore, Verrucomicrobiota was more prevalent in endophytic (Fig. 5a-b). Ascomycota was abundant in the root compartments of Bc and Sn and Olpidiomycota was enriched in the rhizosphere and endophytic of the continuous cropping group (Fig. 5c-d).
Discovery of biomarkers in microbial communities
Differences in root compartments microorganism community were assessed using linear discriminant analysis effect size (LEfSe) analysis at a linear discriminate analysis (LDA) threshold of 3 (Fig. 6). Across the bacterial community, there were more species of Actinobacteria, Acidobacteria, Bacteroides and Proteobacteria and there were species differences between the six test groups (Fig. 6a). For root endophytic microbial communities, Bc biomarkers include Flavobacterium (its phylum to genus), Streptomyces (its order to genus), Rhizobium (its order to genus), Comamonadaceae (family), Pseudoxanthomonas (genus) and Stenotrophomonas (genus) and Alphaproteobacteria (order) were significantly enriched in Bn. For the rhizosphere microbial community, Rc biomarkers included Massilia (its order, family and genus), Pseudomonas (its order to genus), Pedobacter (by order to genus), Xanthomonadaceae (its order and family) and Proteobacteria (family), while the Rn biomarkers mainly include Novosphingobium (its order to genus) and Burkholderia (its order to genus). In the bulk soil microbial community, Gemmatimonadaceae (its phylum to family), Intrasporangiaceae (its phylum to family), Frankiales (order), Gaiellales (order), Micrococcaceae (family), Arthrobacter (genus) and Pseudarthrobacter (genus) were significantly enriched in Sc, while RB41 (its phylum to genus), Vicinamibacteraceae (its order to family), Nocardioidaceae (its order to family), Solirubrobacterales (its order and phylum) Microtrichales (order) ), MB_A2_108 (order), Chloroflexia (its phylum and order) and KD4_96 (order) are significantly enriched in Sn.
Among the fungal communities, Ascomycota and Basidiomycota species were more numerous and there were species differences between the five test groups (Fig. 6b). For the root endophytic microbial community, the Phylum level of Bc biomarkers included Ascomycota and Basidiomycota, while the Genus level included Alternaria, Plectosphaerella, Dactylonectria and Fusarium, while Olpidiomycota (from phyla to genus) was significantly enriched in Bn. For the rhizosphere microbial community, Rc biomarkers included Trichocladium and Tausonia (from phylum to genus). In the bulk soil microbial community, Phoma (its family and genus), Pseudombrophila (by order to genus) and Gibellulopsis (genus) were significantly enriched in Sc, while Chaetomiaceae (its order and family), Helotiales (its class and order) and Mortierella (by order to genus) were significantly enriched in Sn.
Environmental drivers of root compartments microbial community composition
The influence of environmental variables was explored by calculating correlations between the microbial community composition and environmental factors (Fig. 7). To conclude which environmental factors cause changes in the composition of the microbial community in the root compartments, we correlated differences in functional community composition with soil properties employing distance correction. Soil pH, catalase, urease, and AK were significantly and negatively correlated with AN, AP, EC, acid phosphatase, and sucrase. As shown in Fig. 7, the environmental factors were highly correlated with both bulk soil and endosphere of the bacterial (Fig. 7a) and fungal (Fig. 7b) microbiome (P < 0.05). The fungal microbiome of rhizosphere soil is significantly related to environmental factors (P < 0.05). However, the bacterial microbiome only has a significant correlation with EC. This suggests the diversity and composition of the rhizosphere fungal community are more closely related to environmental factors than to bacteria. Besides, environmental factors are significantly related to both functional (based on FAPROTAX annotation) and fungal taxonomic composition of the root compartments microbiome. The bacterial taxonomic composition has a not significant correlation with the soil environmental factors (P > 0.05).
Functions of Bacterial Communities
Prediction of microbial ecological function in the FAPROTAX database, the annotated OTU is assigned to 61 predicted functional groups. Nevertheless, in the Kruskal-Wallis test, only 50 groups showed significant differences between the six root compartments (P < 0.05). Therefore, they are plotted as a functional heatmap (Fig. 8). Among these function predictions, nitrogen (10 groups), carbon (6 groups), sulfur (3 groups), and manganese (1 group) are involved in the geochemical cycle. In the root compartments, Sc enhanced the soil's functional advantages of phototrophy, photosynthetic cyanobacteria, oxygenic photoautotrophy, and aliphatic non-methane hydrocarbon degradation. These functions including dark oxidation of sulfur compounds, nitrogen respiration, nitrate respiration, and human pathogens were significantly (P < 0.05) enhanced in the rhizosphere soil and root zone of the continuous cropping area compared to the non-continuous cropping area.
Tax4Fun (http://tax4fun.gobics.de/) is predicted based on the SILVA database. The Sliva database was used to classify OTUs, and a linear relationship between Sliva classification and prokaryotic classification was constructed in the KEGG database to achieve predictions of microbial community function. In addition, for Pathway, three levels of metabolic pathway information and abundance tables at each level can be obtained by using the Tax4Fun pathway. The first reference pathway that contained genes in the first five of the KEGG pathway analysis was metabolic, environmental information processing, genetic information processing, human diseases, and cellular processes. The number of metabolic pathway genes with the highest content is about 25 times higher than that of the second residual genes (Fig. S2a). The metabolic pathways of the second reference pathway containing the first five genes in KEGG pathway analysis were global and overview maps, carbohydrate metabolism, amino acid metabolism, energy metabolism, and membrane transport (Fig. S2b). The metabolic pathways in which the third reference pathway contains the first five genes in KEGG pathway analysis are metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, biosynthesis of antibiotics, and carbon metabolism (Fig. S2c).