Colson, S. | bioRxiv Science

bioRxiv2026

Vitality capacity preservation through lifelong aerobic exercise: a pathway to healthy ageing

BackgroundThe distinction between healthy and pathological ageing has led to the concept of vitality capacity (VC), which can be understood as the bodys physiological reserve. An individuals VC can be estimated using 12 biomarkers spread across 3 domains: immune and stress response, energy and metabolism and neuromuscular function. Vitality capacity may be preserved by lifelong physical activity. This cross-sectional study aimed to examine the relationship between lifelong aerobic physical activity and VC. MethodsVC of 20 lifelong active and 19 inactive healthy adults aged >55 years was assessed using 12 biomarkers across the three VC domains. Domain-specific z-scores were calculated and averaged to derive a global VC score. Principal component analysis was performed and loadings extracted to estimate domains weight, and multiple correlations were conducted to identify associations among biomarkers, domains and VC scores. ResultsVC was higher in lifelong active participants (+0.2 z-score units, p = 0.006) and correlated with age (r = -0.53, p < 0.001). Neuromuscular domain contributed most to VC variability, and the immune and stress response domain was higher in the active group (+0.4 z-score units, p = 0.001) as energy/metabolism among female participants (+0.5 z-score units, p.adj = 0.006). ConclusionLifelong aerobic physical activity is associated with higher VC in older adults, particularly within the immune and stress response domain. These findings highlight the role of physical activity in preserving the physiological reserve and reinforce the relevance of lifelong aerobic physical activity as a driver of healthy ageing.

physiology↗

bioRxiv2026

CentroFinder: accurate de novo identification of centromeres in fungal genomes

MotivationCentromeres are essential chromosomal loci, yet their computational identification remains challenging due to rapid sequence evolution, high repeat content, and the absence of conserved defining motifs. This challenge is particularly pronounced in fungi, where centromere architectures vary widely in size, sequence composition, and chromatin organization, limiting the effectiveness of single-feature or motif-based prediction approaches. ResultsWe present CentroFinder, a fungal-specific computational framework for de novo centromere prediction from long-read sequencing-based genome assemblies. CentroFinder integrates multiple genomic and long-read-derived features into a weighted scoring model to identify loci where centromere-associated signals converge. Benchmarking against experimentally mapped centromeres in Cryptococcus deuterogattii, Magnaporthe oryzae, and Neurospora crassa demonstrates that CentroFinder consistently predicts a single centromeric region per chromosome, fully nested within CENP-A-defined domains despite substantial diversity in centromere size, sequence composition, and chromatin context. Availability and ImplementationCentroFinder is freely available as open-source software at https://github.com/RahnamaLab/CentroFinder. The pipeline is designed for high-performance computing environments and leverages features derived from long-read sequencing data.

bioinformatics↗