Vadim N. Gladyshev

The paper identifies a compact panel of 21 plasma proteins that predict age-related diseases and organ health better than existing models and panels.

MethylGPT is a transformer-based model trained on human DNA methylation data that accurately predicts methylation values and improves age and disease prediction.

This paper introduces Ageome, a framework to measure epigenetic age across molecular pathways in mice and humans, revealing pathway-specific aging patterns and heterogeneity in mortality prediction.

The paper shows that imputing DNA methylation loci from epigenome-wide data provides stronger associations with smoking and age than directly measured CpGs.

This paper introduces P3GPT, a multimodal transformer model for aging research and drug discovery that performs multiple tasks across species, tissues, and omics data.

This study identifies transcriptomic biomarkers of mortality across species and models, revealing common aging mechanisms and rejuvenation signatures.

The paper investigates epigenetic aging by comparing single-cell DNA methylation changes across aging and development, revealing stochastic patterns and introducing a new algorithm for analyzing CpG clusters.

The paper identifies a Niemann-Pick type C mouse model with accelerated brain aging as a novel model for Alzheimer's disease research by analyzing transcriptomic data from congenital diseases.

The paper uses machine learning on plasma proteome data to develop organ-specific and conventional aging models, showing that age-related diseases reflect accelerated aging of specific organs and systems.

The paper questions the TNT method for iPSC generation by showing persistent Sendai virus expression in hiPSCs, altering reprogramming factor levels and ratios.

The paper introduces causality-enriched epigenetic clocks, DamAge and AdaptAge, to track detrimental and adaptive methylation changes linked to aging, showing their association with adverse outcomes and beneficial adaptations.

This study shows senolytic drugs reduce brain aging and COVID-19-related neuropathology by targeting senescent cells in human brain organoids and mice.

This study uses multi-omics analysis to show that chemical reprogramming reduces biological age in mouse fibroblasts by altering metabolism and mitochondrial function.

The paper develops OMICmAge, a multi-omics biomarker of biological age, and shows it outperforms existing markers in predicting chronic diseases and mortality.

The paper tracks heritable methylation transmission in C. elegans and shows that parental starvation induces intergenerational hormesis via 18S rRNA methylation.

The paper develops universal epigenetic clocks that accurately estimate age across mammalian tissues using DNA methylation data from 185 species.

This study shows biological age can increase with stress and decrease with recovery, using epigenetic clocks in mice and humans.

The paper reanalyzes millions of molecular profiles to identify aging interventions using AI agents, finding over 500 interventions that reduce biological age and revealing patterns in aging modulation.

This study identifies a transcriptomic signature of reprogramming in mouse and human cells that reverses age-related gene expression changes and reveals mechanisms of rejuvenation independent of pluripotency gain.

The paper uses ribosome profiling to show how iron deficiency affects protein translation in yeast by altering gene expression and RNA-binding protein activity.

The paper identifies gastrulation in Xenopus laevis as the stage with the lowest epigenetic age and highest developmental disruption, suggesting it as the onset of aging.

The paper identifies PRC2 low-methylated regions as a universal biomarker of aging and rejuvenation, showing they account for most age-related DNA methylation gain across tissues.

The paper shows that aging leads to increased intron retention and spurious splicing, causing transcriptome deterioration across multiple human tissues.

Rapamycin treatment during early life extends lifespan and healthspan in mice and Daphnia magna by slowing development and delaying aging.

This study shows that cyclic OKSM expression rejuvenates adult cardiomyocytes in mice with heart failure, improving systolic function without dedifferentiation.

This study introduces scAge, a probabilistic method to estimate epigenetic age in single cells, validated in mice, showing cell-level aging heterogeneity and rejuvenation events.

The paper analyzes lifespan variation in wild yeast isolates to identify genetic and environmental factors shaping replicative lifespan and cellular processes involved in aging.

This paper develops a cost-effective method for epigenetic age profiling using shallow methylation sequencing data, demonstrating its application in mouse blood samples and identifying effects of calorie restriction and cellular reprogramming on aging.

The paper introduces TIME-Seq, a low-cost method for epigenetic age prediction, and validates it on mouse and human samples, showing improved scalability and accuracy.