Back

Shallow Genome Sequencing for Phylogenomics of Mycorrhizal Fungi from Endangered Orchids

Unruh, S. A.; Pires, C. A.; Zettler, L. W.; Erba, L.; Grigoriev, I. V.; Barry, K. W.; Daum, C.; Lipzen, A. V.; Stajich, J. E.

2019-12-03 evolutionary biology
10.1101/862763 bioRxiv
Show abstract

Most plant species form symbioses with mycorrhizal fungi and this relationship is especially important for orchids. Fungi in the genera Tulasnella, Ceratobasidium, and Serendipita are critically important for orchid germination, growth and development. The goals of this study are to understand the phylogenetic relationships of mycorrhizal fungi and to improve the taxonomic resources for these groups. We identified 32 fungal isolates with the internal transcribed spacer region and used shallow genome sequencing to functionally annotate these isolates. We constructed phylogenetic trees from 408 orthologous nuclear genes for 50 taxa representing 14 genera, 11 families, and five orders in Agaricomycotina. While confirming relationships among the orders Cantharellales, Sebacinales, and Auriculariales, our results suggest novel relationships between families in the Cantharellales. Consistent with previous studies, we found the genera Ceratobasidium and Thanatephorus of Cerabotasidiaceae to not be monophyletic. Within the monophyletic genus Tulasnella, we found strong phylogenetic signals that suggest a potentially new species and a revision of current species boundaries (e.g. Tulasnella calospora); however it is premature to make taxonomic revisions without further sampling and morphological descriptions. There is low resolution of Serendipita isolates collected. More sampling is needed from areas around the world before making evolutionary-informed changes in taxonomy. Our study adds value to an important living collection of fungi isolated from endangered orchid species, but also informs future investigations of the evolution of orchid mycorrhizal fungi.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.