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The First Mitochondrial Genome for Geastrales (Sphaerobolus stellatus) Reveals Intron Dynamics and Large-Scale Gene Rearrangements of Basidiomycota.

ABSTRACT: In this study, the mitogenome of artillery fungus, Sphaerobolus stellatus, was assembled and compared with other Basidiomycota mitogenomes. The Sphaerobolus stellatus mitogenome was composed of circular DNA molecules, with a total size of 152,722 bp. Accumulation of intergenic and intronic sequences contributed to the Sphaerobolus stellatus mitogenome becoming the fourth largest mitogenome among Basidiomycota. We detected large-scale gene rearrangements in Basidiomycota mitogenomes, and the Sphaerobolus stellatus mitogenome contains a unique gene order. The quantity and position classes of intron varied between 75 Basidiomycota species we tested, indicating frequent intron loss/gain events occurred in the evolution of Basidiomycota. A novel intron position classes (P1281) was detected in the Sphaerobolus stellatus mitogenome, without any homologous introns from other Basidiomycota species. A pair of fragments with a total length of 9.12 kb in both the nuclear and mitochondrial genomes of Sphaerobolus stellatus was detected, indicating possible gene transferring events. Phylogenetic analysis based on the combined mitochondrial gene set obtained well-supported tree topologies (Bayesian posterior probabilities ≥ 0.99; bootstrap values ≥98). This study served as the first report on the mitogenome from the order Geastrales, which will promote the understanding of the phylogeny, population genetics, and evolution of the artillery fungus, Sphaerobolus stellatus.

SUBMITTER: Ye J

PROVIDER: S-EPMC7432440 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.ResultsIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc .

Dataset Information

The First Mitochondrial Genome for Geastrales (Sphaerobolus stellatus) Reveals Intron Dynamics and Large-Scale Gene Rearrangements of Basidiomycota.

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