Abstract

Seeds are reproductive structures able to carry and transfer microorganisms that play an important role in plant fitness. Genetic and external factors are reported to be partly responsible for the plant microbiome assemblage, but their contribution in seeds is poorly understood. In this study, wheat, canola, and lentil seeds were analyzed to characterize diversity, structure, and persistence of seed-associated microbial communities. Five lines and 2 generations of each crop were subjected to high-throughput amplicon sequencing of the 16SrRNA and internal transcribed spacer (ITS) regions. Bacterial and fungal communities differed most by crop type (30% and 47% of the variance), while generation explained an additional 10% and 15% of the variance. The offspring (i.e., generation harvested in 2016 at the same location) exhibited a higher number of common amplicon sequence variants (ASVs) and less variability in microbial composition. Additionally, in every sample analyzed, a "core microbiome" was detected consisting of 5 bacterial and 12 fungal ASVs. Our results suggest that crop, genotype, and field environmental conditions contributed to the seed-associated microbial assemblage. These findings not only expand our understanding of the factors influencing the seed microbiome but may also help us to manipulate and exploit the microbiota naturally carried by seeds.

Highlights

  • A vector of parameters to be tested, either a character vector of names or an integer. a function for computing p values, see summary.glht. additional arguments passed to summary.glht

  • ### multiple comparison procedures ### set up a one-way ANOVA data(warpbreaks) amod

  • ### using covariates data(warpbreaks) amod2

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Summary

Testing Estimated Coefficients

A vector of parameters to be tested, either a character vector of names or an integer. A function for computing p values, see summary.glht. Details The usual z- or t-tests are tested without adjusting for multiplicity.

Cholesterol Reduction Data Set
Contrast Matrices
Detergent Durability Data Set
General Linear Hypotheses
Methods for General Linear Hypotheses
Litter Weights Data Set
Generic Accessor Function for Model Parameters
Multiple Endpoints Data
Model Parameters
Arguments x type
Recovery Time Data Set
Systolic Blood Pressure Data
Frankonian Tree Damage Data
Industrial Waste Data Set
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