Global population structure and adaptive evolution of aflatoxin-producing fungi

Geromy G. Moore, Rodrigo A. Olarte, Bruce W. Horn, Jacalyn L. Elliott, Rakhi Singh, Carolyn J. O'Neal, Ignazio Carbone

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14 Scopus citations

Abstract

Aflatoxins produced by several species in Aspergillus section Flavi are a significant problem in agriculture and a continuous threat to human health. To provide insights into the biology and global population structure of species in section Flavi, a total of 1,304 isolates were sampled across six species (A. flavus, A. parasiticus, A. nomius, A. caelatus, A. tamarii, and A. alliaceus) from single fields in major peanut-growing regions in Georgia (USA), Australia, Argentina, India, and Benin (Africa). We inferred maximum-likelihood phylogenies for six loci, both combined and separately, including two aflatoxin cluster regions (aflM/alfN and aflW/aflX) and four noncluster regions (amdS, trpC, mfs and MAT), to examine population structure and history. We also employed principal component and STRUCTURE analysis to identify genetic clusters and their associations with six different categories (geography, species, precipitation, temperature, aflatoxin chemotype profile, and mating type). Overall, seven distinct genetic clusters were inferred, some of which were more strongly structured by G chemotype diversity than geography. Populations of A. flavus S in Benin were genetically distinct from all other section Flavi species for the loci examined, which suggests genetic isolation. Evidence of trans-speciation within two noncluster regions, whereby A. flavus SBG strains from Australia share haplotypes with either A. flavus or A. parasiticus, was observed. Finally, while clay soil and precipitation may influence species richness in Aspergillus section Flavi, other region-specific environmental and genetic parameters must also be considered.

Original languageEnglish (US)
JournalEcology and Evolution
DOIs
StateAccepted/In press - 2017
Externally publishedYes

Bibliographical note

Funding Information:
North Carolina Cooperative State Research, Education and Extension Service, Grant/ Award Number: 2008-34500-19396 and 2010-34500-21676; National Research Initiative of the USDA Cooperative State Research, Education and Extension Service, Grant/Award Number: 2005-35319-16126; Agriculture and Food Research Initiative Competitive Grants Program, Grant/Award Number: 2013-68004-20359; USDA National Institute of Food and Agriculture (NIFA); National Science Foundation?s Dimensions of Biodiversity (DoB) Program, Grant/ Award Number: DEB-1046167; National Science Foundation (NSF) Genealogy of Life (GoLife) Program, Grant/Award Number: DEB-1541418; University of North Carolina General Administration Funding is from the North Carolina Cooperative State Research, Education and Extension Service, grant nos. 2008-34500-19396 and 2010-34500-21676 and the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service, grant no. 2005-35319-16126. This project was also supported by the Agriculture and Food Research Initiative Competitive Grants Program grant no. 2013-68004-20359 from the USDA National Institute of Food and Agriculture (NIFA). We also thank the National Science Foundation?s Dimensions of Biodiversity (DoB) Program for financial support, DEB-1046167, to I. Carbone. Development of T-BAS v2.0 and SNAP Workbench is supported by the National Science Foundation (NSF) Genealogy of Life (GoLife) Program to IC (DEB-1541418). This work was also supported in part by the University of North Carolina General Administration under an award for High Performance Computing (HPC) and Computational Sciences. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Publisher Copyright:
© 2017 Published by John Wiley & Sons Ltd.

Keywords

  • Aspergillus
  • Balancing selection
  • Maximum likelihood
  • Multilocus sequence typing
  • Principal component analysis

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