This study analyzed the rhizosphere soil microbiota of five different critical ancestral sugarcane species using 16S rRNA and nifH gene sequencing to understand their bacterial community diversity, especially that ofdiazotrophs.
Data filtration, quality evaluation, and sequence optimization
A total of 233,455 effective sequences number with an average length of 413 bp were obtained by sequencing 16S rRNA from different sugarcane species samples. nifH gene sequencing resulted in a total of 182,185 effective sequences with 357 bp average length from all the samples. Filtration of raw reads of 16S rRNA and nifH genes was done using QIIME quality filters, followed by OTU identification, clustering, and analysis, respectively (Additional file 2: Table S1 and S2).
Identification and analysis of Operational Taxonomic Units (OTUs)
To assess microbial diversity among all the sugarcane samples, a Rarefaction curve was drawn. The presence of OTUs across all the 16S samples is shown in Additional file 1: Figure S1A. The highest number of OTUs was observed in S. sinense followed by S. robustum, S. barberi, S. officinarum and S. spontaneum. Presence of the OTUs identified in nifH sequence data across all the samples is shown in Additional file 1: Figure S1B. The highest number of OTUs was observed in S. barberi and S. sinense followed by S. robustum and S. spontaneum. The clustering of OTUs was done based on 97% sequence similarity.
Rank abundance curves (RACs)
Comparison of rank abundance based on OTU ranks derived from 16S rRNA and nifH sequences (Additional file 1: Figure S2 A and B) was performed to visualize the relative species abundance across all the samples. RACs depict the species richness and species evenness, which help identify common and rare rhizobacterial species in the sugarcane species studied. Figure S2A, revealed that all the sugarcane samples showed high abundance at least OTU ranks i.e. 1, following this at 500 OTU rank equal species richness and evenness was displayed. At the highest OTU rank, i.e. 1000, all the samples showed least species abundance and high species evenness. S. sinense samples displayed the highest species richness among all the samples at highest OTU rank. But in (Additional file 1) Figure S2B, S. barberi samples displayed the highest species richness among all the samples at highest OTU rank. The S. spontaneum sample, compared to all the other samples, showed less species evenness at low OTU rank, i.e. approximately 180.
We analyzed common and unique OTUs based on 16S rRNA and nifH gene sequences for each sample in the Venn diagram (Figure 1A & B). In the 16S rRNA sequence data, 6202 OTUs were identified collectively, of which 519 OTUs were common across all samples. According to the Venn diagram, the relative frequency of OTUs in the studied species is as follows: S. sinense>S.robustum>S.barberi>S. officinarum>S. spontaneum. The highest number of OTUs was recorded in S. sinense. A total of 1099 OTUs were identified in all the samples of nifH gene sequence data. Among them, 14 were common OTUs found across all the samples. According to the Venn diagram, occurrence of OTUs in the plant samples was as follows: S.barberi>S. robustum>S. officinarum>S. sinense>S. spontaneum. The highest numbers of OTUs were found in S. barberi and, S. spontaneum had the lowest number of OTUs for both 16S rRNA and nifH gene sequence data.
Principal component analysis (PCA)
To understand the rhizobacterial community composition, PCA plots were generated based on 16S rRNA and nifH gene samples OTUs data (Figure S3 A and B). The OTUs present in the 16S samples shows that S. officinarum and S. robustum are not identical, S. sinense and S. robustum are more similar, and S. spontaneum is distinct from all the other samples (Figure S3 A). PCA of nifH gene samples showed a close identity between S. barberi and S. spontaneum whereas S. sinense, S. officinarum and S. robustum remained distinct to one another.
Alpha diversity refers to the diversity within a particular sample individually, and it is usually represented by the species (i.e. species richness) enumerated in the test samples. Alpha diversity analysis was done using Shannon, Simpson and Chao Indices Rarefaction curves for both 16S and nifH sequence data. Additional file 1: Figure S4 A consists of two plots displaying Shanon, Simpson, and Chao Indices, built using 16S samples. The Shannon Index increased as both the species richness and the evenness in the community increase. Among the 16S rRNA data of all the samples analyzed, Shanon and Chao Index of S. sinense sample was the highest as compared to the other four samples with little variation among themselves. Whereas, Simpson Index increased as diversity increased. In the Simpson Index plot, S. officinarum sample showed the highest value implying that it has more species diversity compared to the other four samples. Shannon and Chao Index of nifH gene data of all the samples showed more species diversity in S. barberi sample than others. According to the Simpson Index, of S. officinarum sample was the highest compared to other (Additional file 1: Figure S4 B).
Community composition analysis
The relative abundance of the microbial communalities differed among five sugarcane species analyzed.The abundant phyla identified in 16S samples in all sugarcane species are Proteobacteria, Firmicutes, Actinobacteria, Acidobacter, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Planctomycetes and Nitrospirae (Figure 2A). Firmicutes were the highest phyla present in S. officinarum compare to other samples. Gemmatimonadetes was the highest in S. barberi, Acidobacter was highest in S. spontaneum, and Bacteroidetes was highest in S. sinense. Based on nifH gene samples, we observed the occurrence of Proteobacteria and Verrucomicrobia (Figure 2B). Firmicutes were abundant in S. spontaneum samples compare to other samples. Many unclassified phyla were also represented abundantly in S. spontaneum followed by S. sinense and S. barberi samples.
Genus distribution using 16S rRNA sequence data (Figure 3A) showed Bacillus, Pseudomonas, Pseudoarthobacter, Massilia, Lysobacter, Nitrospira, Gemmatimonas, Streptomyces, and Rhizobiumweremost abundant in S. officinarum whereas S. barberi sample was dominated by Bacillus, Pseudomonas, Pseudoarthobacter, Lysobacter, Gemmatimonas, and Sphingomonas species. Bacillus, Pseudomonas, Pseudoarthobacter, Massilia, Nitrospira, Gemmatimonas, Streptomyces, Paenibacillus, and Dechloromonaswere reported in S. robustum. S. spontaneum sample contained Pseudomonas, Pseudoarthobacter, Massilia, Tumebacillus, Remibacter, Sphingomonas, and Skermanelia. Bacillus, Pseudomonas, Pseudoarthobacter, Massilia, Lysobacter, Nitrospira, Faecalibacterium and Streptococuswere detected in S. sinense. Bacillus was the most abundant genus in S. officinarum, S. robustum and S. sinense while Pseudomonas became the number one genus in S. barberi and S. spontaneum.
Genus distribution using nifH gene data is presented in Figure 3B. Bradyrhizobium, Dechloromonas, Desulfovibrio, and Stenotrophomas were abundant in S. officinarum, while Bradyrhizobium, Desulfovibrio, Xanthobacter and Anaeromyxobacter were the leading genera in S. barberi. Bradyrhizobium, Dechloromonas, Desulfovibrio, and Anaeromyxobacter were the dominant groups in S. robustum. S. spontaneum contained Bradyrhizobium, Azospirillum, Methanobacterium, and Paenibacilllus species. Bradyrhizobium, Dechloromonas, Xanthobacter and Anaeromyxobacter were present in S. sinense. Genus Burkolderia was found in S. barberi, S. spontaneum, and S. sinense while Beijerinckia was recorded in S. barberi, S. spontaneum, and S. sinense. Genus Idenella was commonly present in all the samples except S. spontaneum and Kosakonia was commonly present in S. barberi and S. spontaneum.
The KRONA software was used to visualize the species annotation results, in which the circles represent different points from the inside out class level, the size of the fan represented the relative proportions of the results of different OTU comments (Figure 4). Species annotation results visualized with KRONA identified Bacillus, Pseudomonas, Pseudoarthobacter, Rhizobiales, Burkholderiales, Massilia and Streptomyces as the most dominant ones (Figure 4A). Similar analyses carried out with nifH OTUs showed Bradyrhizobium, Dechloromonas, Desulfovibrio, Stenotrophomonas, Anaeromyxobacter etc. as the most common genera identified (Figure 4B). The relative abundance of the top twenty diazotrophs at the genus level present in all sugarcane species is shown in Figure 5. Diazotrophs belonging to the genus Bradyrhizobium were present in all the samples tested.
Star analysis of all the sugarcane samples
Star analysis was conducted using the top 10 genera of each sample. Star analysis for both 16S and nifH sequence data are presented in Additional file 1: Figure S5 A&B. Top 10 genera used for the 16S data based analysis were Pseudoarthobacter, Pseudomonas, Bacillus, Massilia, Gemmatimonas, Nitrospora, Haliangium, Ramibacter, Tumebacillus and Lysobacter. From this analysis, Bacillus genus was found high in S. officinarum and S. robustum samples, while Pseudomonas was the dominant one in S. barberi, S. robustum, S. spontaneum and S. sinense. Tumebacillus genus was found only in S. spontaneum. Similarly, star analysis for nifH gene samples identified the presence of Bacillus in all the samples. Desulfovibrio was found in S. officinarum, S. robustum and S. barberi in significant numbers. Xanthobacter was abundant in S. sinense. Anaeromyxo bacter was found in all the samples except that of S. spontaneum. Pseudoacidovorax genus was found only in S. robustum, S. barberi and S. sinense samples. Azospirillum and Methylobacterium were unique to S. spontaneum.
Beta diversity measures species diversity among different samples collected from different or similar environments. We performed the principal coordinated analysis (PCoA) and UniFrac-based cluster analysis to understand the beta diversity of 16S rRNA and nifH gene in the sugarcane species studied (Figures 6 & 7). Based on 16S rRNA data, S. sinense, along with S. barberi, and S. officinaruma long with S. robustum, formed independent clusters. And, S. spontaneum segregated away from all others, displaying higher beta diversity. The same was found true for nifH gene analysis (Figure 7A). Hierarchical clustering based on the UniFrac cluster analysis showed similar results for both 16S rRNA and nifH gene data, containing similar identical sequences showing 0 (blue colour) distances. S. sinense to S. spontaneum showed more distance (red colour = 0.3) indicating dissimilarity in sequences of S. sinense to S. spontaneum samples (Figure 6A and 7B).
The distribution of dominant genera based on their relative abundance performed with Bray–Curtis algorithm showed Bacillus being the dominant genus in all the samples except those from S. Spontaneum (Figure 8). Second dominant genus identified was Pseudomonas, while Pseudoarthobacter was the third leading one. S. spontaneum showed the presence of most unidentified genera in our analysis. Similarly, distribution of dominant genera in nifH samples showed the presence of Dechloromonas sp. abundant in all samples except those from S. barberi. Xanthobacter and Bradyrhizobiumg were found to be dominant in S. Officinarum and S. sinense, respectively (Figure 9).