Altered soil acidity and high-order taxonomic composition upon short- versus long-term agricultural intensification
A highly soil acidification pH < 5 was found in long-term rice-vegetable rotations (V20) (Table 1). As shown in Fig. 1, soils in short- (V10) versus long-term rice-vegetable rotations were at acid-sensitive stage. Acid cations saturation ratios (proportion of H+, Al3+) were increased significantly with the ongoing of intensive cropping history (p < 0.05), which rose from 4.5% (V0) to 11.8% (V10) and 40.7% (V20). Concentrations of H+ and Al3+ in V20 were 3.0 and 7.6 times higher than V0, respectively. Furthermore, soil base cations saturation ratios (proportion of Ca2+, Mg2+, K+, Na+) reached the amount to 95.5% in control treatment (V0), among which Ca2+and Mg2+ dominated exchangeable soil cations pool and accounted for approximate 82.1% and 9.4%, respectively. By comparison, base cations saturation ratios significantly decreased to 88.2% in V10 and by 59.3% in V20.
To identify whether soil acidity can explain changes in microbial structure and functions among different intensive cropping history, we performed shotgun metagenomic sequencing on soil samples. A total of 549 million 150-bp paired-end reads were generated, with an average of 60.98 ± 2.73 (s.e.) million reads for each sample (Additional file 1: Table S1). After quality control, we obtained 543 million high-quality clean reads, with an average of 60.34 ± 2.69 (s.e.) million reads per sample (Additional file 1: Table S2). The clean reads were assembled, predicted and then aligned to the reference genomes from the National Center for Biotechnology Information (NCBI).
We found that soil microbial communities were disturbed after converting from conventional paddy (V0) to rice-vegetable rotations (V10 and V20) (Fig. 2a). Krona charts indicated that these changes in taxonomic composition were evident in high-order levels, particularly at class rank, which community structure shifted from a more balanced cluster comprising Betaproteobacteria, Deltaproteobacteria and Alphaproteobacteria in V0 to a less-balanced community comprising Alphaproteobacteria, Actinobacteria and Grammaproteobacteria dominated community in V10 and V20 (Fig. 2a, b). One-way ANOVA suggested, Betaproteobacteria, Deltaproteobacteria and Nitrospira were more abundant in V0 than in V10 and V20 (p < 0.05), whereas the relative abundances of Grammaproteobacteria, Gemmatimonadetes, Acidobacteriia, Ktedonobacteria and Sphingobacteriia were significantly increased in V20 than V0 (p < 0.05; Fig. 2c).
Shifts in fine-scale taxonomic composition and determine the aciduric biomarkers indicative of soil acidification
Furthermore, it is interesting to notice differentially fine-scale taxonomic compositions (Genus level) of soil microbiome in distinct intensive cropping history (Fig. 3 and Fig. 4a). V10 and V20 samples shared a similar microbial composition but showed a greater distance with V0 samples in both metagenome and 16S rRNA gene (Fig. 3a, c). Unconstrained principal coordinates analysis (PCoA) of Bray-Curtis distance revealed that microbial communities of V10 and V20 samples formed significantly distinct clusters with V0 in both metagenome (ANOSIM: statistic = 0.9835, p < 0.05) and 16S rRNA gene (ANOSIM: statistic = 1, p < 0.05), which separated along the first coordinate axis (Fig. 3b, d).
To characterize the core microbial composition and abundance, heat map analysis was performed with the top 50 most abundant genera (Fig. 4a). Increased abundance was evident at some dominant genera (e.g. Gemmatimonas, Bradyrhizobimu, Solirubrobacter, Gemmatirosa, Mycobacterium, Rhodanobacter, Arthrobacter and Preudolabrys, etc) of V10 and V20. The largest increase in relative abundance response to distinct intensive cropping history was by the genus Rhodanobacter, with the abundance increasing from an average of 0.06% [± 0.0086, s.e.] in V0 to 4.12% (± 0.6263, s.e.) of those in V20, followed by the genera Gemmatirosa, Gemmatimonas and Sphingomonas. The largest decrease was by the genus Nitrospira, with the abundance decreasing from an average of 1.65% [± 0.1325, s.e; V0] to 0.93 [± 0.0661, s.e; V20], followed by the genus Candidatus_Entotheonella and Geobacter.
Next, we analyzed whether these dominant members can be used as biomarkers to differentiate V0, V10 and V20. LDA scores (log 10) of 3.5 or greater are listed (Fig. 4b) to identify biomarkers for V0 and V20. The results showed that Nitrospira and Candidatus_Entotheonella were significantly enriched in V0, whereas Rhodanobacter, Gemmatirosa, Sphingomonas, Streptomyces, Haliangium, Candidatus_Solibacter and Thermogemmatispora showed higher relative abundance in V20 than V0. Furthermore, SparCC network analysis was constructed to investigate the possible interactions between the biomarkers. Two clusters were apparent from the network (Fig. 4c): Nitrospira, Candidatus_Entotheonella formed one cluster whereas Rhodanobacter, Gemmatirosa, Sphingomonas, Streptomyces, Haliangium, Candidatus_Solibacter and Thermogemmatispora formed the other. Intra-cluster associations were significantly (Spearman’s rank correlation coefficient, p < 0.05) positive whereas inter-cluster associations were significantly negative.
Redundancy analysis (RDA) assessed the relationship between acidity parameters and dominant microbial communities and its implication for soil acidification. Acid cations (H+, Al3+) and inorganic nitrogen (NO3−-N, NH4+-N) were positively (permutation test, p < 0.05) related with V20 samples, whereas base cations (Ca2+, Mg2+, Na+) presented a notable positive correlation (p < 0.05) with V0 samples. Twelve genera with high correlation were listed in RDA (Fig. 4d). H+, Al3+, NO3−-N and NH4+-N contributed positively (p < 0.05) to the distribution of Rhodanobacter, Gemmatirosa, Sphingomonas, Pseudolabrys, Gaiella, Candidatus_Solibacter, Streptomyces, while contributed negatively to Nitrospira, Candidatus_Entotheonella, Geobacter. Intriguingly, Rhodanobacter, Gemmatirosa, Sphingomonas, Candidatus_Solibacter, Streptomyces, Nitrospira and Candidatus_Entotheonella were above-mentioned biomarkers (Fig. 4b, c), thus they have potential for serving as acid-sensing biomarkers. Rhodanobacter, Gemmatirosa, Sphingomonas, Candidatus_Solibacter and Streptomyces acting as aciduric biomarkers.
Identifying differentially abundant KEGG modules in distinct intensive cropping history
To evaluated whether the shifts in soil microbial composition and abundance also had functional consequences concerning genes, we annotated the functions of genes that were specifically enriched in V0, V10 and V20 to Kyoto Encyclopedia of Genes and Genomes Orthology (KEGG) Module database. LEfSe analysis was performed and LDA scores (log 10) of 2 or greater are listed to identify indicative modules of V0, V10 and V20 respectively, which were related to 94 modules (Additional file 2: Table S3 ~ S5). Notably, most modules enriched in V0 samples were involved in ABC transporters, carbon metabolism, whereas modules enriched in V20 samples were related to oxidative phosphorylation, two-component system, DNA replication and nitrogen metabolism.
Second, using one-way ANOVA, we validated that modules were differentially enriched among sample groups (Tukey-Kramer test) (Fig. 5b). We found that gene clusters associated with functions supporting ABC transporters, e.g. branched-chain amino acid- (M00237), iron (III)- (M00190), tungstate- (M00186), phosphonate- (M00223), general L-amino acid- (M00232), manganese/zinc/iron- (M00319) transport system, were significantly enriched in V0 samples. Conversely, functional genes that were significantly more abundant in V20 samples were considerably related to multiple dehydrogenation pathways for pH homeostasis, which was typically represented by supporting proton-pumping: NAD biosynthesis (M00115), cytochrome complex oxidase (M00151, M00153, M00417), pentose phosphate pathway (M0004) and cytoplasmic proton consumption: GABA biosynthesis (M00135), assimilatory nitrate reduction (M00531).
Alteration in pH homeostasis gene modules likely drove differentially acid production of aciduric biomarkers
Finally, we want to figure out whether variation in soil acidity of sample groups were correlated with the 26 identified indicative modules. Results of spearman correlation analysis (Fig. 6a) showed that the concentrations of H+, Al3+ and NO3−-N were positively correlated with abundance of eleven modules significantly, including M0004, M00115, M00135, M00151, M00648, M00256, M00531, M00520, M00651, M00417 and M00523 (p < 0.05), while the base cations Ca2+, Mg2+, Na+ were negatively correlated with these modules (p < 0.05).
To investigate the role of gene-annotated modules in the recruitment of soil microbiome, we carried out species and functional contribution analyses targeting the above-mentioned nine acid-sensing biomarkers (Fig. 4b) and eleven pH homeostasis modules (Fig. 6a). Regression analysis of Bray-Curtis distance in pH homeostasis modules and biomarkers community composition revealed that soil acid-sensing microbiomes of V20 samples were separated from V0 and V10 along the first two coordinate axes. Notably, these gene modules provided the largest contribution to Rhodanobacter (total abundance for eleven modules, Additional file 3: Table S6) and contributed (One-way ANOVA, p < 0.01) more to the V20 (425.56 ± 55.35) samples than the V10 (28.55 ± 4.62) and V0 (3.61 ± 0.50). The same variation and differences have been found in Gemmatirosa and Sphingomonas, which abundance rose from 24.88 ± 3.19 (V0) to 173.03 ± 17.46 (V20) and 37.20 ± 16.38 (V0) to 208.00 ± 24.54 (V20) respectively. Typically, as for Rhodanobacter, pH homeostasis modules, including M00004, M00531, M00135, M00115, M00151 and M00417 exhibited significantly higher abundance in V20 than V10 and V0 samples (Fig. 7a ~ f).