BackgroundSeeds of plants are a confirmation of their next generation and come associated with a unique microbia community. Vertical transmission of this microbiota signifies the importance of these organisms for a healthy seedling and thus a healthier next generation for both symbionts. Seed endophytic bacterial community composition is guided by plant genotype and many environmental factors. In north-east India, within a narrow geographical region, several indigenous rice genotypes are cultivated across broad agroecosystems having standing water in fields ranging from 0-2 m during their peak growth stage. Here we tried to trap the effect of rice genotypes and agroecosystems where they are cultivated on the rice seed microbiota. We used culturable and metagenomics approaches to explore the seed endophytic bacterial diversity of seven rice genotypes (8 replicate hills) grown across three agroecosystems.ResultsFrom seven growth media, 16 different species of culturable EB were isolated. A predictive metabolic pathway analysis of the EB showed the presence of many plant growth promoting traits such as siroheme synthesis, nitrate reduction, phosphate acquisition, etc. Vitamin B12 biosynthesis restricted to bacteria and archaea; pathways were also detected in the EB of two landraces. Analysis of 522,134 filtered metagenomic sequencing reads obtained from seed samples (n=56) gave 4061 OTUs. Alpha diversity indices showed significant differences in observed OTU richness (P≤0.05) across genotypes. Significant differences were also found between the individual hills of a rice genotype. PCoA analysis exhibited three separate clusters and revealed the clusters separated based on genotype, while agroecosystem showed a minimal effect on the variation of seed microbiota (adonis, R2=0.07, P=0.024). Interestingly, animal gut resident bacteria such as Bifidobacterium, Faecalibacterium, Lactobacillus, etc. were found in abundance as members of the seed microbiota.ConclusionOverall, our study demonstrates, indigenous rice genotypes of north-east India have a unique blend of endophytic bacteria in their mature seeds. While there are notable variations among plants of the same genotype, we found similarities among genotypes cultivated in completely different environmental conditions. The beta diversity variations across the seven rice genotypes were significantly shaped by their genotype rather than their agroecosystems.
A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison. So, there is a high need for faster sequence analysis algorithms. In this study, we developed an alignment-free algorithm for faster sequence analysis. The novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. The method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. These estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. This matrix is used as an input for the neighbor program in the PHYLIP package for phylogenetic tree construction. Our method was tested on eight benchmark datasets and on in-house generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior). The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale.
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