Data partitions, Bayesian analysis and phylogeny of the zygomycetous fungal family Mortierellaceae, inferred from nuclear ribosomal DNA sequences

Abstract

Although the fungal order Mortierellales constitutes one of the largest classical groups of Zygomycota, its phylogeny is poorly understood and no modern taxonomic revision is currently available. In the present study, 90 type and reference strains were used to infer a comprehensive phylogeny of Mortierellales from the sequence data of the complete ITS region and the LSU and SSU genes with a special attention to the monophyly of the genus Mortierella. Out of 15 alternative partitioning strategies compared on the basis of Bayes factors, the one with the highest number of partitions was found optimal (with mixture models yielding the best likelihood and tree length values), implying a higher complexity of evolutionary patterns in the ribosomal genes than generally recognized. Modeling the ITS1, 5.8S, and ITS2, loci separately improved model fit significantly as compared to treating all as one and the same partition. Further, within-partition mixture models suggests that not only the SSU, LSU and ITS regions evolve under qualitatively and/or quantitatively different constraints, but that significant heterogeneity can be found within these loci also. The phylogenetic analysis indicated that the genus Mortierella is paraphyletic with respect to the genera Dissophora, Gamsiella and Lobosporangium and the resulting phylogeny contradict previous, morphology-based sectional classification of Mortierella. Based on tree structure and phenotypic traits, we recognize 12 major clades, for which we attempt to summarize phenotypic similarities. M. longicollis is closely related to the outgroup taxon Rhizopus oryzae, suggesting that it belongs to the Mucorales. Our results demonstrate that traits used in previous classifications of the Mortierellales are highly homoplastic and that the Mortierellales is in a need of a reclassification, where new, phylogenetically informative phenotypic traits should be identified, with molecular phylogenies playing a decisive role.

Figure 1. Examples for the macro- and micromorphology of the investigated Mortierellales strains.

Lobed growth of Mortierella capitata on MEA. (a). Micromorphology of M. verticillata (b), arrows indicate the sporangiophores after dehiscence. Chlamydospore of M. gemmifera (c). Sporangiophores with sporangiospores (d) and stylospores (e) of M. polycephala. Stylospores of M. polygonia produced on MEA (f) and its chlamydospores produced on OA (g). Sporangiophores and sporangia of M. amoeboidea (h). Sporangiophores of M. biramosa (i), the arrow indicates the characteristic collar remaining at the apex after the dehiscence of the sporangium. Stylospores of M. indohii (j). Sporangia of Dissophora decumbens (k). Chlamydospores (l) and sporangium (m) produced by D. ornata. Sporangiophores and sporangia after the dehiscence of M. turficola (n). Branching sporangiophores and sporangiospores of Gamsiella multidivaricata (o). Sporangia and sporangiophores produced by M. mutabilis on MEA (p) and its branching sporangiophores produced on OA (q). Sporangia of M. exigua (r). In each panel, the lengths of scale bars denote 10 µm.

Figure 2. Saturation of log likelihood (lnL) values as a function of model complexity, calculated as a mean of the likelihoods of post–burn–in trees.

(a) Comparison of all 15 partitioned models. Figure shows that the indel matrix is best described by one–parameter models, which show up as local plateaus (see arrowheads) in the saturation of likelihoods. (b) Comparison of MCMC analyses performed by using the mixture models (in BayesPhylogenies) only with 1 to 7 GTR+ Γ matrices.

Figure 3. The 50% Majority Rule consensus phylogram inferred from the nuclear ribosomal sequence data of 90 Mortierellales strains using BayesPhylogenies, under a model with 5 GTR+ Γ matrices for each of the six partitions.

Numbers above branches indicate Maximum Parsimony (MPBS), Maximum Likelihood bootstrap (MLBS) percentages and Bayesian posterior probabilities (BBP), respectively. Missing or weakly supported nodes (<50% or 0.5) are denoted by a “–”.