Biology subjects

Vladimirova, A.

Publications and source records attributed to Vladimirova, A..

2 recordsLinked to original sources

bioRxiv2024

Transcriptomic Hallmarks of Mortality Reveal Universal and Specific Mechanisms of Aging, Chronic Disease, and Rejuvenation

Health is strongly affected by aging and lifespan-modulating interventions, but the molecular mechanisms of mortality regulation remain unclear. Here, we conducted an RNA-seq analysis of mice subjected to 20 compound treatments in the Interventions Testing Program (ITP). By integrating it with the data from over 4,000 rodent tissues representing aging and responses to genetic, pharmacological, and dietary interventions with established survival data, we developed robust multi-tissue transcriptomic biomarkers of mortality, capable of quantifying aging and change in lifespan in both short-lived and long-lived models. These tools were further extended to single-cell and human data, demonstrating common mechanisms of molecular aging across cell types and species. Via a network analysis, we identified and annotated 26 co-regulated modules of aging and longevity across tissues, and developed interpretable module-specific clocks that capture aging- and mortality-associated phenotypes of functional components, including, among others, inflammatory response, mitochondrial function, lipid metabolism, and extracellular matrix organization. These tools captured and characterized acceleration of biological age induced by progeria models and chronic diseases in rodents and humans. They also revealed rejuvenation induced by heterochronic parabiosis, early embryogenesis, and cellular reprogramming, highlighting universal signatures of mortality, shared across models of rejuvenation and age-related disease. They included Cdkn1a and Lgals3, whose human plasma levels further demonstrated a strong association with all-cause mortality, disease incidence and risk factors, such as obesity and hypertension. Overall, this study uncovers molecular hallmarks of mammalian mortality shared across organs, cell types, species and models of disease and rejuvenation, exposing fundamental mechanisms of aging and longevity.

systems biology↗

bioRxiv2023

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

bioinformatics↗