Biology subjects

Andrianova, E. L.

Publications and source records attributed to Andrianova, E. L..

3 recordsLinked to original sources

bioRxiv2026

Thermodynamic scaling of canine aging and reversible clock deceleration by a reverse transcriptase inhibitor

Aging was tracked in a cohort of 99 "retired" sled dogs over four years to characterize the latent dynamics of physiological decline. Animals were randomized to receive either placebo or the reverse transcriptase inhibitor lamivudine for [~]30 months. We employed a variational autoregressive model to integrate longitudinal blood parameters and DNA methylation (DNAm) profiles. The model defines Biological Age (BA) as a signature of irreversible damage with Poissonian statistics, a feature conserved across mammalian scales. Lamivudine modulated age-independent latent variables and temporarily decelerated the DNAm clock in females, but these effects were reversible upon treatment discontinuation and did not alter the long-term BA trajectory. Critically, we show that physiological fluctuations are governed by a single systemic factor - an effective or phenotypic temperature representing an emergent (macroscopic) property. We show that while the rate of damage accumulation (the BA slope) is independent of this temperature, actuarial aging parameters (initial mortality and the Gompertz exponent) are strongly temperature dependent. This supports a model where mortality arises from effective activation across a protective free energy barrier that erodes with age. These findings identify phenotypic temperature as an important control variable governing the kinetics of organism-level failure, offering a compelling target for interventions aimed at extending healthspan by "squaring" the survival curve.

systems biology↗

bioRxiv2025

The Nicotinamide Salvage Pathway is a Metabolic Vulnerability of High-Risk MDS Stem Cells

High-risk myelodysplastic syndrome (HR-MDS) is a malignant clonal disorder originating in hematopoietic stem and progenitor cells (HSPCs). The current standard of care for HR-MDS patients is hypomethylating agents; however, the response rate is poor. There is thus a need to explore vulnerabilities of HR-MDS HSPCs for better clinical outcomes. We demonstrate that HR-MDS HSPCs have significant upregulation of metabolic proteins required for glycolysis, citric acid cycle, and oxidative phosphorylation. Consistently, we see increased oxygen consumption rate in HR-MDS HSPCs compared to healthy, suggesting an increased metabolic rate. Corroboratively, compared to healthy HSPCs, HR-MDS HSPCs have increased abundance of mitochondrial complex I proteins, which are NADH dehydrogenases, and crucial for energy production. Therefore, we investigated whether HR-MDS HSPCs are functionally reliant on NAMPT, the rate-limiting enzyme in the nicotinamide salvage pathway of NAD anabolism. NAMPT inhibition significantly decreased NAD(H) in HR-MDS HSPCs. Consequently, NAMPT inhibition reduced the oxygen-consuming capacity of HR-MDS-HSPCs compared to healthy. Importantly, NAMPT inhibition significantly impaired the self-renewal and colony-forming potential, increased cell death and reduced disease burden specifically of HR-MDS HSPCs, compared to healthy controls. Collectively, our data suggest that NAMPT is selectively required for the function and survival of HR-MDS HSPCs representing a promising therapeutic target.

cancer biology↗

bioRxiv2020

Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice

We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouses health state by means of stochastic evolution of a single organism state variable, the "dynamic frailty index" (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.

bioinformatics↗