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The frequently encountered macroscopic slime molds of the genus Ceratiomyxa have long been recognized by mycologists and protistologists for hundreds of years. These organisms are amoebozoan amoebae that live and grow inside and on the surface of decaying wood. When conditions are favorable, they form subaerial sporulating structures called fruiting bodies which take on a variety of forms. These forms are typically some arrangement of column and/or branches, but one is uniquely poroid, forming folds instead. Originally, this poroid morphology was designated as its own species. However, it was not always clear what significance fruiting body morphology held in determining species. Currently, Ceratiomyxa fruticulosa var. porioides, the poroid form, is considered a taxonomic variety of Ceratiomyxa fruticulosa based on morphological designation alone. Despite its long history of observation and study, the genus Ceratiomyxa has been paid little molecular attention to alleviate these morphological issues. We have obtained the first transcriptomes of the taxon C. fruticulosa var. porioides and found single gene phylogenetic and multigene phylogenomic support to separate it from C. fruticulosa. This provides molecular evidence that fruiting body morphology does correspond to species level diversity. Therefore, we formally restore Ceratiomyxa porioides stat. nov. to its original status. 

ABSTRACT Phylogenies built from multiple genes have become a common component of evolutionary biology studies. Molecular phylogenomic matrices used to build multi-gene phylogenies can be built from either nucleotide or protein matrices. Nucleotide-based analyses are often more appropriate for addressing phylogenetic questions in evolutionarily shallow timescales (i.e., less than 100 million years) while protein-based analyses are often more appropriate for addressing deep phylogenetic questions. PhyloFisher is a phylogenomic software package written in Python3. The manually curated PhyloFisher database contains 240 protein-coding genes from 304 eukaryotic taxa. Here we presentnucl_matrix_constructor.py, an expansion of the PhyloFisher starting database, and an update to PhyloFisher that maintains DNA sequences. This combination will allow users the ability to easily build nucleotide phylogenomic matrices while retaining the benefits of protein-based pre-processing used to identify contaminants and paralogy. 

Abstract Biological soil crusts represent a rich habitat for diverse and complex eukaryotic microbial communities. A unique but extremely common habitat is the urban sidewalk and its cracks that collect detritus. While these habitats are ubiquitous across the globe, little to no work has been conducted to characterize protists found there. Amoeboid protists are major predators of bacteria and other microbial eukaryotes in these microhabitats and therefore play a substantial ecological role. From sidewalk crack soil crusts, we have isolated three naked amoebae with finely tapered subpseudopodia, and a simple life cycle consisting of a trophic amoeba and a cyst stage. Using a holistic approach including light, electron, and fluorescence microscopy as well as phylogenetics using the ribosomal small subunit rRNA gene and phylogenomics using 230 nuclear genes, we find that these amoeboid organisms fail to match any previously described eukaryote genus. However, we determined the amoebae belong to the amoebozoan lineage Variosea based on phylogenetics. The molecular analyses place our isolates in two novel genera forming a grade at the base of the variosean group Protosteliida. These three novel varioseans among two novel genera and species are herein named “Kanabo kenzan” and “Parakanabo toge.” 

Abstract PhyloFisher is a software package written primarily in Python3 that can be used for the creation, analysis, and visualization of phylogenomic datasets that consist of protein sequences from eukaryotic organisms. Unlike many existing phylogenomic pipelines, PhyloFisher comes with a manually curated database of 240 protein‐coding genes, a subset of a previous phylogenetic dataset sampled from 304 eukaryotic taxa. The software package can also utilize a user‐created database of eukaryotic proteins, which may be more appropriate for shallow evolutionary questions. PhyloFisher is also equipped with a set of utilities to aid in running routine analyses, such as the prediction of alternative genetic codes, removal of genes and/or taxa based on occupancy/completeness of the dataset, testing for amino acid compositional heterogeneity among sequences, removal of heterotachious and/or fast‐evolving sites, removal of fast‐evolving taxa, supermatrix creation from randomly resampled genes, and supermatrix creation from nucleotide sequences. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Constructing a phylogenomic dataset Basic Protocol 2: Performing phylogenomic analyses Support Protocol 1: Installing PhyloFisher Support Protocol 2: Creating a custom phylogenomic database 

Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher ( https://github.com/TheBrownLab/PhyloFisher ), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic “single-copy orthogroup” datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.