bims-mitran 2026-05-31 papers

Issue of 2026–05–31
two papers selected by
Andreas Kohler, Umeå University

Cell Mol Life Sci. 2026 May 25.

Mitochondrial ribosomal protein MRPL27 supports glioma malignancy through regulation of oxidative phosphorylation.

Jingyao Gu, Boya Zhou, Lin Ji, Haoyang Yuan, Haibo Ji, Mingxiao Zhu, Chunyan He, Xueping Gu, Suji Yan, Yin Wang.

Glioma is a highly invasive primary brain tumor with a poor prognosis and currently lacks effective treatment methods. Increasing evidence indicates that mitochondrial oxidative phosphorylation (OXPHOS) is crucial for the development of glioma; however, the regulatory mechanisms controlling mitochondrial protein synthesis and energy metabolism are not yet fully understood. Analysis of the TCGA and CGGA databases reveals that MRPL27 is highly expressed in glioma tissues and is significantly associated with poor patient survival. Silencing MRPL27 significantly inhibits the proliferation, migration, and tumor growth of glioma cells, while inducing cell apoptosis. Mechanistically, the absence of MRPL27 impairs the mitochondrial oxidative phosphorylation process, reduces ATP production, disrupts redox balance, and increases oxidative stress. Notably, MRPL27 deficiency specifically reduces the protein content of mitochondrial-encoded oxidative phosphorylation components without altering their transcriptional levels, indicating its role in post-transcriptional regulation of mitochondrial protein expression. In summary, these findings suggest that MRPL27 is a key regulator of mitochondrial translation and energy metabolism in glioma, and emphasize the significance of mitochondrial ribosome regulation as a potential metabolic weakness for therapeutic intervention.

Keywords: Glioma progression; MRPL27; Mitochondrial translation; Oxidative phosphorylation

DOI: https://doi.org/10.1007/s00018-026-06236-8

BMC Bioinformatics. 2026 May 25.

Mito_Plot: open-source pipeline for quantification and visualization of mitochondrial DNA heteroplasmy.

Kohta Nakamura, Naoyuki Matsumoto, Yasushi Okazaki.

BACKGROUND: Mitochondrial DNA heteroplasmy plays a crucial role in mitochondrial function, aging, and a wide range of human diseases. Recent advances in high-throughput sequencing have enabled large-scale detection of heteroplasmic variants; however, effective cohort-level integration, comparison, and visualization of Mutant Allele Frequency (MAF) values remain challenging. Existing tools often focus on single-sample visualization or require substantial manual preprocessing, limiting their scalability and usability for large cohorts. To address these challenges, we developed Mito_Plot, an open-source computational pipeline designed for standardized quantification and intuitive visualization of Mitochondrial DNA (mtDNA) heteroplasmy across multiple samples.
RESULTS: Mito_Plot accepts standard mitochondrial VCF files and automatically calculates MAF based on allelic depth information. MAF data from multiple samples are aggregated into a unified matrix aligned by genomic position, enabling direct cross-sample comparison. The pipeline provides interactive two-dimensional circular plots that map MAF onto the mitochondrial genome with gene-level annotations, facilitating rapid identification of mutation hotspots and sample-specific patterns. In addition, Mito_Plot offers optional three-dimensional visualizations that enhance exploration of large cohorts by separating variant distributions across samples and genomic regions. Application of Mito_Plot to multi-sample mitochondrial sequencing datasets demonstrated robust handling of both variants with low and high MAF values, efficient processing of large cohorts, and improved interpretability compared with static or single-sample visualizations.
CONCLUSIONS: Mito_Plot is a scalable, user-friendly software pipeline for cohort-scale quantification and visualization of mtDNA MAF. By integrating standardized MAF calculation with interactive 2D and 3D visualizations, Mito_Plot facilitates comprehensive exploration of mitochondrial variant landscapes across large datasets. The open-source and modular design of the software supports reproducible research and flexible integration into existing analysis workflows, making Mito_Plot a practical resource for mitochondrial genomics research and clinical investigations.

Keywords: Circular genome; Cohort-scale analysis; Data visualization; Mitochondrial DNA; Mitochondrial heteroplasmy; Variant analysis

DOI: https://doi.org/10.1186/s12859-026-06476-2