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Resolving the Mortierellaceae phylogeny through synthesis of multi-gene phylogenetics and phylogenomics

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Abstract

Early efforts to classify Mortierellaceae were based on macro- and micromorphology, but sequencing and phylogenetic studies with ribosomal DNA (rDNA) markers have demonstrated conflicting taxonomic groupings and polyphyletic genera. Although some taxonomic confusion in the family has been clarified, rDNA data alone is unable to resolve higher level phylogenetic relationships within Mortierellaceae. In this study, we applied two parallel approaches to resolve the Mortierellaceae phylogeny: low coverage genome (LCG) sequencing and high-throughput, multiplexed targeted amplicon sequencing to generate sequence data for multi-gene phylogenetics. We then combined our datasets to provide a well-supported genome-based phylogeny having broad sampling depth from the amplicon dataset. Resolving the Mortierellaceae phylogeny into monophyletic genera resulted in 13 genera, 7 of which are newly proposed. Low-coverage genome sequencing proved to be a relatively cost-effective means of generating a high-confidence phylogeny. The multi-gene phylogenetics approach enabled much greater sampling depth and breadth than the LCG approach, but has limitations too. We present this work to resolve some of the taxonomic confusion and provide a genus-level framework to empower future studies on Mortierellaceae diversity and evolution.

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Data availability

All amplicon sequences and genome sequences have been deposited to Genbank. All trees have been deposited to Treebase (http://purl.org/phylo/treebase/phylows/study/TB2:S25806?x-access-code=16dea5a74941bc1aa812c9ad125aed0&format=html).

Code availability

All custom scripts and pipelines are available on GitHub and/or Zenodo.

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Acknowledgements

We extend our gratitude to Amy McGovern and Gail Doehring for assistance in performing DNA extractions, the MGP library preparation and sequencing, and metadata collection. We are grateful to Dr Marty Chilvers, Dr. Andrea Porras-Alfaro, Dr Matthew E Smith, Dr. Matt Kasson and the ZyGoLife project for contributing isolates that were used in these analyses. The collection of Modicella provided by Dr. M.E. Smith was made possible by NSF grants DEB1354802 and DEB1441677. We thank Dr. Kevin Liu for advice on phylogenetic analyses, as well as Bryan Rennick and Alicja Okrasińska for proofreading and helpful discussions.

Disclaimer

The mention of company names or trade products does not imply that they are endorsed or recommended by the US Department of Agriculture over other companies or similar products not mentioned. USDA is an equal opportunity provider and employer.

Funding

US National Science Foundation (NSF) DEB 1737898 (GB and NVP), Michigan State University AgBioResearch NIFA project MICL02416 (GB), NSF STC BEACON Cooperative Agreement DBI-093954 (GB & NVP); US National Science Foundation (The Zygomycetes Genealogy of Life) DEB1354802 and DEB1441677 (JS); JGI-the work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; Data analyses were performed on the High-Performance Computing Cluster at the University of California-Riverside in the Institute of Integrative Genome Biology supported by NSF DBI-1429826 and NIH S10-OD016290 (JS).

Author information

Authors and Affiliations

  1. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA

    Natalie Vandepol & Gregory Bonito

  2. Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA

    Julian Liber

  3. Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA

    Alessandro Desirò & Gregory Bonito

  4. Joint Genome Institute, Berkeley, CA, 94720, USA

    Hyunsoo Na, Megan Kennedy, Kerrie Barry & Igor V. Grigoriev

  5. Illinois Natural History Survey, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA

    Andrew N. Miller

  6. United States Department of Agriculture, Agricultural Research Service, Peoria, IL, 61604, USA

    Kerry O’Donnell

  7. Department of Microbiology and Plant Pathology & Institute for Integrative Genome Biology, University of California-Riverside, Riverside, CA, 92521, USA

    Jason E. Stajich

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  1. Natalie Vandepol
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  2. Julian Liber
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  3. Alessandro Desirò
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  7. Igor V. Grigoriev
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  10. Jason E. Stajich
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  11. Gregory Bonito
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Contributions

NV-primer design & validation, MLST PCR, MLST sequence analysis, & writing. JL-microscopy, photography, species & genus description, isolate troubleshooting. AD-DNA extraction & strain isolations. HN-LCG sequencing. MK-LCG sequencing. KB-LCG sequencing coordination. IVG-LCG sequencing coordination. ANM-metadata for shared strains & proofreading. KO-idea, strains, research support, MLST sequencing, & proofreading. JES-idea, software development, LCG sequence assembly, annotation, analysis, & writing, data deposition. GB–research ideas, research support, & writing.

Corresponding author

Correspondence to Gregory Bonito.

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All authors have agreed to participate in this research

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All authors have read and approved the submitted manuscript.

Electronic supplementary material

Below is the link to the electronic supplementary material.

13225_2020_455_MOESM1_ESM.xlsx

Supplementary Table 1: Species not included. A summary of the described species not included in this study, the estimated placement under the proposed taxonomy, the basis for the estimation, and the reference for the original species definition. Supplementary material 1 (XLSX 79 kb)

13225_2020_455_MOESM2_ESM.xlsx

Supplementary Table 2: Isolate metadata. The substrate, geographic origin, collector, collection year, vouchered by, and synonymous isolate identification numbers known for each isolate used in this study. Supplementary material 2 (XLSX 93 kb)

13225_2020_455_MOESM3_ESM.xlsx

Supplementary Table 3: LCG assembly & annotation. The LCG assembly statistics, BUSCO analysis on the fungi_odb9 dataset which contained 290 single-copy marker genes, protein ortholog detection, number of markers used out of 434 total, and assembly deposition/accession details. Sample identifications were adjusted to correct misidentified samples or outdated taxonomy. “REF” indicates reference de novo genomes that were used to guide LCG sequence analysis, which are best accessed by BioSample number. Supplementary material 3 (XLSX 101 kb)

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Supplementary Table 4: Primer sets. All primer sets produced by the MGP locus selection pipeline. Status indicates which tests the primer set has passed: in silico = simulated PCR in IPCRESS; in vitro = amplification & sequencing of each independent primer set using genomic DNA from a panel of isolates; in vivo = multiplex PCR and sequencing to generate mutli-gene phylogenetic data. Failure at each stage came in the form of 1) non-specific in silico “amplification”, 2) off target in vitro amplification or failure to amplify across the panel of isolates, and 3) in vivo MGP sequence data analysis revealing selective pressure or potential gene duplication of that locus. Supplementary material 4 (XLSX 21 kb)

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Supplementary Table 5: Raw sequences per locus. The total number of sequences recovered, the total number of isolates represented, and the ratio between the number of sequences and isolates, full and partial length, for each locus. Supplementary material 5 (XLSX 83 kb)

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Supplementary Table 6: Rejected Strains. The strain number, preliminary identification, ITS-based identification, number of full-length sequences for each locus, and reason the strain was excluded from the final MGP analyses. Supplementary material 6 (XLSX 75 kb)

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Supplementary Table 7: MGP loci by isolate. Values indicate the Genbank reference number for the sequence included in the final dataset. Numbers in parentheses indicate the initial degree of sequence duplication for that sample at that locus. “0.5” indicates at least one partial sequence was detected but could not be included due to insufficient length. Asterisks indicate sequences obtained from a low coverage or de novo genome sequence, rather than PCR amplification. Supplementary material 7 (XLSX 77 kb)

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Supplementary Table 8: Primer mismatch. The total number of isolates belonging to each of the ITS-based clades defined by Wagner et al. (2013), the number of those detected in each locus, and the number of usable isolates included in the phylogenetic analyses. The larger the discrepancy between total and detected isolates, the poorer the performance of the primer set on this lineage of Mortierellomycotina. Supplementary material 8 (XLSX 75 kb)

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Supplementary Table 9: Locus Sequence Variability at the Species Level. Summary statistics from pairwise blastn analyses of the locus sequences as submitted to Genbank. Empty cells indicate that the locus was not recovered for any strains in that species. ‘n’ = the number of non-self pairwise blastn analyses conducted, where ‘0’ indicates that only one sequence was available to represent the species and therefore no intraspecific analyses could be conducted. “Min % - Max %” = the range of percent sequence variability, where ‘0’ means the sequences were identical. Values in bold indicate a meaningful difference between the maximum observed intraspecific variation and the minimum observed interspecific variation. Supplementary material 9 (XLSX 20 kb)

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Supplementary Table 10: Locus Sequence Variability at the Genus Level. Summary statistics from pairwise blastn analyses of the locus sequences as submitted to Genbank. Empty cells indicate that the locus was not recovered for any strains of any species in that genus. ‘n’ = the number of non-self pairwise blastn analyses conducted, where ‘0’ indicates that only one sequence was available to represent the genus and therefore no intrageneric analyses could be conducted. “Min % - Max %” = the range of percent sequence variability, where ‘0’ means the sequences were identical. Values in bold indicate a meaningful difference between the maximum observed intrageneric variation and the minimum observed intergeneric variation. Supplementary material 10 (XLSX 14 kb)

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Supplementary Table 11: Species characteristics. The current and proposed classification, synonyms, geographic distribution, ecology, and spore morphologies of Mortierellaceae species represented in this study. Supplementary material 11 (XLSX 82 kb)

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Supplementary Table 12: Comparative phylogenies. A comparison of the Mortierellaceae phylogenies generated based on the species included in this study, Wagner et al. (2013), Petkovits et al. (2011), and the taxonomic groupings of Linnemann and Gams published in 1977–1989. Supplementary material 12 (XLSX 81 kb)

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Supplementary Fig. 1: Unconstrained MGP Mortierellaceae phylogeny. Unconstrained Maximum Likelihood analysis of the concatenated 6-gene MGP dataset (8181 characters). Taxa are named according to the initial ITS-based species identification and current taxonomy. Clade colors indicate monophyletic groupings according to the proposed taxonomy, lines and names denote previously defined clades for the purpose of discussion. Supplementary material 13 (TIFF 113994 kb)

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Supplementary Fig. 2: MrBayes MGP Mortierellaceae phylogeny. A Bayesian analysis of the concatenated MGP dataset using a series of partial constraints defined by major nodes in the LCG phylogeny. Clade colors indicate groupings according to the Constrained RAxML MGP phylogeny. Supplementary material 14 (TIFF 413623 kb)

Supplementary material 15 (DOCX 11 kb)

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Vandepol, N., Liber, J., Desirò, A. et al. Resolving the Mortierellaceae phylogeny through synthesis of multi-gene phylogenetics and phylogenomics. Fungal Diversity 104, 267–289 (2020). https://doi.org/10.1007/s13225-020-00455-5

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