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ABSTRACT The assembly of the mitoribosomal small subunit involves folding and modification of rRNA, and its association with mitoribosomal proteins. This process is assisted by a dynamic network of assembly factors. Conserved methyltransferases Mettl15 and Mettl17 act on the solvent-exposed surface of rRNA. Binding of Mettl17 is associated with the early assembly stage, whereas Mettl15 is involved in the late stage, but the mechanism of transition between the two was unclear. Here, we integrate structural data fromTrypanosoma bruceiwith mammalian homologs and molecular dynamics simulations. We reveal how the interplay of Mettl15 and Mettl17 in intermediate steps links the distinct stages of small subunit assembly. The analysis suggests a model wherein Mettl17 acts as a platform for Mettl15 recruitment. Subsequent release of Mettl17 allows a conformational change of Mettl15 for substrate recognition. Upon methylation, Mettl15 adopts a loosely bound state which ultimately leads to its replacement by initiation factors, concluding the assembly. Together, our results indicate that assembly factors Mettl15 and Mettl17 cooperate to regulate the biogenesis process, and present a structural data resource for understanding molecular adaptations of assembly factors in mitoribosome. 

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 

Abstract Ochrophyta is an algal group belonging to the Stramenopiles and comprises diverse lineages of algae which contribute significantly to the oceanic ecosystems as primary producers. However, early evolution of the plastid organelle in Ochrophyta is not fully understood. In this study, we provide a well-supported tree of the Stramenopiles inferred by the large-scale phylogenomic analysis that unveils the eukaryvorous (nonphotosynthetic) protist Actinophrys sol (Actinophryidae) is closely related to Ochrophyta. We used genomic and transcriptomic data generated from A. sol to detect molecular traits of its plastid and we found no evidence of plastid genome and plastid-mediated biosynthesis, consistent with previous ultrastructural studies that did not identify any plastids in Actinophryidae. Moreover, our phylogenetic analyses of particular biosynthetic pathways provide no evidence of a current and past plastid in A. sol. However, we found more than a dozen organellar aminoacyl-tRNA synthases (aaRSs) that are of algal origin. Close relationships between aaRS from A. sol and their ochrophyte homologs document gene transfer of algal genes that happened before the divergence of Actinophryidae and Ochrophyta lineages. We further showed experimentally that organellar aaRSs of A. sol are targeted exclusively to mitochondria, although organellar aaRSs in Ochrophyta are dually targeted to mitochondria and plastids. Together, our findings suggested that the last common ancestor of Actinophryidae and Ochrophyta had not yet completed the establishment of host–plastid partnership as seen in the current Ochrophyta species, but acquired at least certain nuclear-encoded genes for the plastid functions. 

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.