Suraganov, A. | Search bioRxiv

bioRxiv2026

From Genes to Brains: Molecular Evolution and Mammalian Brain Cellular Diversity

Mammalian brains exhibit extensive diversity in size, cellular composition, and organization, yet the molecular evolutionary changes associated with this phenotypic diversification remain incompletely understood. Here, we investigated whether protein-sequence evolution covaries with quantitative differences in brain phenotypes across mammals. We integrated 29 brain and body traits with orthologous protein sequences from 18 mammalian species and obtained gene-specific molecular evolutionary measures for 10,395 genes. To reduce redundancy among strongly correlated phenotypes, we selected five representative traits that explained 88.12% of the standardized trait variance under linear reconstruction. Using RERconverge, we tested for associations between gene-specific relative evolutionary rates and evolutionary changes in these five representative traits. The primary analysis, based on log-transformed phenotypes, identified 25 significant gene--trait associations involving 23 genes, 19 of which were also recovered using untransformed phenotypes, indicating that a substantial subset of the associations was robust to phenotype transformation. Expression profiling of these shared candidate genes across adult human GTEx brain tissues revealed heterogeneous patterns ranging from broad expression across the examined tissues to low adult brain expression. Functional enrichment analysis further showed that the gene set retained for comparative analysis was enriched for diverse biological processes, including metabolic, cellular, and neural pathways. These results identify gene-specific evolutionary-rate associations with quantitative mammalian phenotypes and provide functional context for the genes represented in the comparative analysis. Our study provides a comparative framework linking relative evolutionary rates of protein-coding genes to quantitative variation in mammalian brain cellular architecture while accounting for shared evolutionary history.

evolutionary biology↗

bioRxiv2025

Assessing the feasibility of machine learning for ancient DNA age prediction: limitations and insights

We investigated the possibility of estimating the age of ancient biological samples directly from their DNA damage profiles using supervised machine learning. Traditional dating methods such as radiocarbon dating, dendrochronology rely on either material context or isotope composition, while our approach exploits intrinsic molecular degradation signatures. Using damage statistics obtained from ancient DNA sequencing data, we trained several regression models to predict sample age over a temporal range of up to 10,000 years. Despite initial correlations between specific damage features and age, cross-validation and external testing revealed no statistically significant predictive signal beyond mean-based baselines. These findings indicate that, in the current formulation, DNA damage information alone is insufficient for reliable age estimation. However, this negative result provides important methodological insight: environmental and biochemical factors appear to dominate damage variation, effectively masking chronological signal. We suggest that integrating contextual data, expanding labeled datasets, and incorporating physical models of DNA decay may improve future attempts. Our study thus contributes to a transparent assessment of the limitations and prospects of DNA-based fossil dating.

bioinformatics↗