A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Timothy A. Crombie, Robyn E. Tanny, Claire M. Buchanan, Nicole M. Roberto, Erik C. Andersen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Caenorhabditis elegans is one of the major model organisms in biology, but only recently have researchers focused on its natural ecology. The relative sparsity of information about C. elegans in its natural context comes from the challenges involved in the identification of the small nematode in nature. Despite these challenges, an increasing focus on the ecology of C. elegans has caused a wealth of new information regarding its life outside of the laboratory. The intensified search for C. elegans in nature has contributed to the discovery of many new Caenorhabditis species and revealed that congeneric nematodes frequently cohabitate in the wild, where they feed on microbial blooms associated with rotting plant material. The identification of new species has also revealed that the androdioecious mating system of males and self-fertilizing hermaphrodites has evolved three times independently within Caenorhabditis. The other two selfing species, C. briggsae and C. tropicalis, share the experimental advantages of C. elegans and have enabled comparative studies into the mechanistic basis of important traits, including self-fertilization. Despite these advances, much remains to be learned about the ecology and natural diversity of these important species. For example, we still lack functional information for many of their genes, which might only be attained through an understanding of their natural ecology. To facilitate ecological research of selfing Caenorhabditis nematodes, we developed a highly scalable method to collect nematodes from the wild. Our method makes use of mobile data collection platforms, cloud-based databases, and the R software environment to enhance researchers' ability to collect nematodes from the wild, record associated ecological data, and identify wild nematodes using molecular barcodes.

Original languageEnglish (US)
Article numbere63486
JournalJournal of Visualized Experiments
Volume2022
Issue number181
DOIs
StatePublished - Mar 2022

ASJC Scopus subject areas

  • Neuroscience(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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