bioRxiv ScienceSearch

bioRxiv · 10.1101/2021.09.08.459357

Large dataset of octocoral mitochondrial genomes provides new insights into mt-mutS evolution and function

Abstract

All studied octocoral mitochondrial genomes contain a gene from the MutS family, whose members code for proteins involved in DNA mismatch repair, other types of DNA repair, meiotic recombination, and other functions. Although mutS homologues are found in all domains of life as well as viruses, octocoral mt-mutS is the only such gene encoded in an organellar genome. While the function of mtMutS is not known, its domain architecture, conserved sequence, and presence of some characteristic residues suggest its involvement in mitochondrial DNA repair. This inference is supported by exceptionally low rates of mt-sequence evolution observed in octocorals. Previous studies of mt-mutS have been limited by the small number of octocoral mt-genomes available. We utilized sequence-capture data from the recent Quattrini et al. study to assemble complete mitochondrial genomes for 97 species of octocorals. Combined with sequences publicly available in GenBank, this resulted in a dataset of 184 complete mitochondrial genomes, which we used to re-analyze the conservation and evolution of mt-mutS. We discovered the first case of mt-mutS loss among octocorals in one of the two Pseudoanthomastus sp. assembled from Quattrini et al. data. This species displayed accelerated rate and and changed patterns of nucleotide substitutions in mt-genome, which we argue provide additional evidence for the role of mtMutS in DNA repair. In addition, we found accelerated mt-sequence evolution in the presence of mt-mutS in several octocoral lineages. This accelerated evolution did not appear to be the result of relaxed selection pressure and did not entail changes in patterns of nucleotide substitutions. Overall, our results support previously reported patterns of conservation in mt-mutS and suggest that mtMutS is involved in DNA repair in octocoral mitochondria. They also indicate that the presence of mt-mutS contributes to, but does not fully explain, the low rates of sequence evolution in octocorals

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muthye, V. R., Mackereth, C., Stewart, J., Lavrov, D. V.. 2021-09-09. Large dataset of octocoral mitochondrial genomes provides new insights into mt-mutS evolution and function. https://doi.org/10.1101/2021.09.08.459357

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics