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Yurgel, S. N.

Publications and source records attributed to Yurgel, S. N..

2 recordsLinked to original sources

Influence of plant genotype on nodule microbiome composition and function in seasonal yellow pea varieties

Background and AimsLegume root nodules host symbiotic rhizobia essential for nitrogen fixation but also harbor diverse non-rhizobial taxa that remain poorly characterized. Yellow pea (Pisum sativum) cultivars adapted to distinct seasonal growth (spring and winter) offer an opportunity to explore whether host genotype influences nodule microbiome composition and function. This study investigates the taxonomic and functional profiles of nodule-associated microbial communities in seasonal yellow pea varieties. MethodsA field experiment with 6 field pea cultivars (spring and winter types) was conducted in South Dakota. Surface-sterilized root nodules were subjected to full-length 16S rRNA gene sequencing using Oxford Nanopore technology. Reads were quality filtered, organellar sequences removed, and taxonomic classification performed with the EMU pipeline. Microbial diversity, community structure, and core taxa were analyzed using R, with predicted functions inferred by FAPROTAX. ResultsThe nodule microbiome was dominated by Rhizobium, accounting for up to 98% of classified reads. After excluding Rhizobium, non-rhizobial diversity revealed a conserved core microbiome shared across cultivars, including cyanobacteria with potential phototrophic and diazotrophic traits. Minor seasonal differences were observed, with winter cultivars exhibiting higher evenness and specific associations. ConclusionYellow pea nodules harbor a stable, cyanobacteria-enriched core microbiome, largely consistent across seasonal cultivars. Season-specific microbial patterns suggest potential host-genotype influences, warranting further validation.

plant biology↗

PICRUSt2: An improved and extensible approach for metagenome inference

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2 (https://github.com/picrust/picrust2), which expands the capabilities of the original PICRUSt method1 to predict the functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, and novel phenotype predictions. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches overall. PICRUSt2 is also now more flexible and allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes, which are related to the inherent challenges of analyzing metagenome data in general.

bioinformatics↗