Mitochondrial encephalomyopathies: what next?

S DiMauro

doi:10.1007/bf01799110

Home/Archive/PubMed

PubMed · 8884573

Mitochondrial encephalomyopathies: what next?

S DiMauro

View original source ↗Export metadata ↓

Abstract

In few areas of medicine has progress been more spectacular than in the field of mitochondrial diseases, especially those related to mtDNA mutations. Much remains to be done, however, and this brief review discusses the following areas of research where progress has been more limited or data are still controversial: (1) the molecular basis of respiratory-chain defects due to nuclear DNA mutations; (2) defects of mitochondrial protein importation; (3) defects of intergenomic signalling; (4) pathophysiology of mtDNA-related disorders; (5) ageing and age-related neurodegenerative diseases; (6) therapy; and (7) genetic counselling.

Explore related subjects

Aging DNA, Mitochondrial Electron Transport Genetic Counseling Humans Metabolism, Inborn Errors Mitochondrial Encephalomyopathies Mutation Nervous System Diseases Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

S DiMauro. 1996. Mitochondrial encephalomyopathies: what next?. https://doi.org/10.1007/bf01799110

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

KEEP EXPLORING

Related citations

PubMed2026

An open benchmark and language models for AI in aging biology.

Over the past two decades, human aging has been characterized across DNA methylation, transcriptomic, proteomic, and clinical modalities, yet no benchmark evaluates whether AI systems can interpret these heterogeneous data types in the context of aging biology. We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six developer teams. Despite recent advances in AI, no single model dominates all tasks, with omics-based age prediction being the hardest task regardless of scale. To test whether these gaps can be closed without frontier-scale resources, we fine-tuned a family of five multitask Longevity-LLMs on domain-specific aging data. The compact (0.6B-9B parameters) Longevity-LLMs matched or exceeded far larger frontier systems on LongevityBench, showing that general-purpose language models can be adapted to structured-omics tasks. We publicly release the benchmark, models, and Longevity Claw, an agentic research interface for aging researchers.

Aging↗

PubMed2026

Association between sirtuin 1 and markers of oxidative stress in master athletes.

BACKGROUND: Lifelong training in master athletes confers protective effects, promoting higher sirtuin levels and enhanced antioxidant capacity. Although Sirtuin 1 (SIRT1) is well studied, no previous study has examined the relationship between circulating SIRT1 levels and antioxidant defense variables in master athletes. PURPOSE: To compare and analyze the relationships between circulating levels of SIRT1 and variables related to antioxidant defense in master athletes (MA) and untrained middle-aged individuals (UMA). METHODS: Male MA (n = 42; 51.62 ± 7.33 years; ≥10 years of training and competition in running) and UMA (n = 15; 47.73 ± 8.52 years) were evaluated. Venous blood samples were collected for biochemical analyses of SIRT1, antioxidant enzymes, TBARS and F2-isoprostanes, 8-OHdG, and redox balance indexes. RESULTS: MA showed higher levels of SIRT1 (18.22 ± 4.53 vs. 6.08 ± 2.11 ng/mL; p < 0.0001), as well as of SOD, CAT, and GSH (p < 0.001), indicating a more favorable antioxidant profile. After adjustment for body fat percentage, differences in SOD, CAT, GSH and TBARS, remained significant. SIRT1 was positively correlated with SOD (r = 0.279; p = 0.031), CAT (r = 0.485; p < 0.001), GSH (r = 0.476; p < 0.001) and CAT/8-OHdG (r = 0.430; p = 0.032), and negatively correlated with TBARS (r = - 0.518; p < 0.001). CONCLUSION: Master athletes exhibited higher circulating SIRT1 concentrations and a more favorable systemic redox profile than untrained individuals, with SIRT1 being associated with markers of antioxidant defense, lipid peroxidation, and redox balance.

Aging↗

PubMed2026

Decoding SUMOylation as a metabolic stress sensor in aging and age-related disorders: Mechanisms, tissue specificity and therapeutic potential.

SUMOylation is a reversible post-translational modification increasingly recognized for its role in coordinating cellular responses to metabolic stress during aging. Emerging evidence indicates that it functions beyond a conventional modification, representing an adaptive stress‑responsive regulatory network that integrates metabolic, oxidative, inflammatory, and proteotoxic signals. Rather than acting on isolated pathways, this network finely tunes mitochondrial function, proteostasis, genome maintenance, immune balance, and epigenetic regulation. Accumulating evidence indicates that SUMO-dependent regulation exhibits remarkable tissue specificity, supporting mitochondrial adaptation and contractile integrity in skeletal muscle, shaping lipid and glucose metabolism in the liver, modulating proteotoxic stress and neuronal resilience in the brain, and contributing to immune cell differentiation and chronic low-grade inflammation during aging. In this review, we summarize current mechanistic insights into SUMO signaling across aging-relevant tissues, with particular emphasis on its functional interplay with other post-translational modifications, including ubiquitination and acetylation. We discuss how SUMOylation operates as a shared regulatory layer while enabling context-dependent outcomes that underlie diverse aging phenotypes and age-related disorders. Finally, we evaluate emerging translational approaches-ranging from pharmacological modulation of SUMO enzymes to lifestyle interventions such as caloric restriction and exercise-that highlight both the opportunities and challenges of targeting SUMO-regulated stress responses in aging. Together, this synthesis provides a framework for understanding how SUMOylation links metabolic stress to tissue-specific aging trajectories and therapeutic potential.

Aging↗