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Abstract PremiseEndophytic plant‐microbe interactions range from mutualistic relationships that confer important ecological and agricultural traits to neutral or quasi‐parasitic relationships. In contrast to root‐associated endophytes, the role of environmental and host‐related factors in the acquisition of leaf endophyte communities at broad spatial and phylogenetic scales remains sparsely studied. We assessed endofoliar diversity to test the hypothesis that membership in these microbial communities is driven primarily by abiotic environment and host phylogeny. MethodsWe used a broad geographic coverage of North America in the genusHeucheraL. (Saxifragaceae), representing 32 species and varieties across 161 populations. Bacterial and fungal communities were characterized using 16S and ITS amplicon sequencing, respectively, and standard diversity metrics were calculated. We assembled environmental predictors for microbial diversity at collection sites, including latitude, elevation, temperature, precipitation, and soil parameters. ResultsAssembly patterns differed between bacterial and fungal endophytes. Host phylogeny was significantly associated with bacteria, while geographic distance was the best predictor of fungal community composition. Species richness and phylogenetic diversity were consistent across sites and species, with only fungi showing a response to aridity and precipitation for some metrics. Unlike what has been observed with root‐associated microbial communities, in this system microbes show no relationship with pH or other soil factors. ConclusionsOverall, this work improves our understanding of the large‐scale patterns of diversity and community composition in leaf endophytes and highlights the relative significance of environmental and host‐related factors in driving different microbial communities within the leaf microbiome.
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Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations
Abstract The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
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- PAR ID:
- 10523256
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s42003-024-06093-w
