Composition of the Maize Endophytic Microbiome is Correlated with Maize Genotype
All plants contain endophytes that have the potential to provide fitness benefits to their hosts by increasing tolerance to environmental stressors, boosting plant nutrition and growth, and providing increased resistance or tolerance to insect pests and plant pathogens. We are characterizing endophytic populations inhabiting aboveground maize tissues with the goal of associating maize genetic variation with the diversity, structure and constitution of maize-associated microbial communities. Nine maize lines, representing a diverse subset of the founders of the NAM (Nested Association Mapping) population, were field-grown and assayed for culturable endophytic bacteria and fungi. Two distinct seed sources for each maize line were planted in a randomized experimental design, and three replicates per seed source were assayed, representing a total of 54 samples. Leaf pieces were harvested just prior to pollination for each maize line, surface sterilized using standard endophyte isolation methodologies, and ground leaf extracts were cultured on four media that select for slow- and fast-growing fungi and copiotrophic, diazotrophic, and oligotrophic bacteria. Approximately 65% of the samples contained one or more phenotypically distinct, culturable bacteria, 28% contained one or more fungi, 22% contained both bacteria and fungi, and endophytes were undetectable in 28% of the samples. Interestingly, a greater number and diversity of fungi were cultured from tropical maize lines than from temperate lines. Bacteria were isolated from all maize lines, with some lines exhibiting significantly greater microbial community diversity than others. Several phenotypically similar bacteria and fungi were isolated from multiple maize lines. Microbial cataloging of unculturable endophytes via 16S and ITS sequencing, as well as identification of novel endophytes via whole genome metagenomic sequencing, is in progress. In a parallel analysis, whole genome shotgun sequences generated for the nine maize lines were selected from the HapMap2 dataset for in-silico taxonomic classification of the microbial population. Identification of the bacterial microbiome is underway using FCP (naïve Bayes) and Phymm (Hidden Markov Models). Characterization of fungal endophytes is being done using a read-mapping algorithm and custom ITS databases. We are particularly interested in identifying members of the microbiome that modulate disease symptoms caused by maize leaf and ear pathogens. Hence, future studies will focus on in vitro and in planta endophyte-pathogen interactions.