Biology subjects

Moskalev, A.

Publications and source records attributed to Moskalev, A..

4 recordsLinked to original sources

bioRxiv2025

EpImAge: An Epigenetic-Immune Clock for Disease-Associated Biological Aging

BackgroundWe present EpImAge, an explainable deep learning tool that integrates epigenetic and immunological markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging - epigenetic alterations and immunosenescence. MethodsFirst, epigenetic and immunologic data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic immunological biomarkers and training an age estimation model. ResultsUsing state-of-the-art deep neural networks optimized for tabular data analysis, EpImAge achieves competitive performance metrics against 33 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each immunological feature to the age prediction. ConclusionsThe sensitivity to multiple diseases due to combining immunologic and epigenetic profiles is promising for both research and clinical applications. EpImAge is released as an easy-to-use web tool that generates the age estimates and levels of immunological parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.

systems biology↗

bioRxiv2024

Bridging biomolecular modalities for knowledge transfer in bio-language models

In biology, messenger RNA (mRNA) plays a crucial role in gene expression and protein synthesis. Accurate predictive modeling of mRNA properties can greatly enhance our understanding and manipulation of biological processes, leading to advancements in medical and biotechnological applications. Utilizing bio-language foundation models allows for leveraging large-scale pretrained knowledge, which can significantly improve the efficiency and accuracy of these predictions. However, mRNA specific foundation models are notably limited posing challenges for efficient predictive modeling in mRNA-focused tasks. In contrast, DNA and protein modalities have numerous general-purpose foundation models trained on billions of sequences. This paper explores the potential for adaptation of existing DNA and protein bio-language models for mRNA-focused tasks. Through experiments using various mRNA datasets curated from both public domain and internal proprietary database, we demonstrate that pre-trained DNA and protein models can be effectively transferred for mRNA-focused tasks using various adaptation techniques such as probing, full-rank, and low-rank finetuning. In addition, we identify key factors that influence successful adaptation, offering guidelines on when general-purpose DNA and protein models are likely to perform well for mRNA-focused tasks. We further assess the impact of model size on adaptation efficacy, finding that medium-scale models often outperform larger ones for cross-modal knowledge transfer. We conclude that by leveraging the interconnectedness of DNA, mRNA, and proteins, as outlined by the central dogma of molecular biology, the knowledge in foundation models can be effectively transferred across modalities, significantly enhancing the repertoire of computational tools available for mRNA analysis.

bioinformatics↗

bioRxiv2021

Geroprotective effects of Aronia melanocarpa fruit extract on Drosophila melanogaster

Aging and its consequences is one of the main problems of humanity. Phytochemicals such as polyphenols, flavonoids, anthocyanins have great potential as geroprotectors. Plants including Aronia used as dietary supplements are an excellent source of these compounds. We have studied the effects of the ethanol extract from Aronia fruits on Drosophila melanogaster lifespan, locomotor activity, stress resistance (oxidative stress, heat shock and starvation). And also, in order to reveal the influence of Aronia extract applied to different life cycles of Drosophila melanogaster, we selected different dietary schemes for imago: throughout life, during the first two weeks (1 - 2) and during average age (4 - 6 weeks). It was revealed that the ethanol extract of chokeberry increases the median life expectancy in males and females by 5% when Drosophila melanogaster is added to the diet for 4 - 6 weeks of life. This suggests that intervention even in old age is sufficient to increase lifespan. In addition, no harmful effects of ABE on locomotor activity were found as an indicator of fly health. We showed that Aronia extract increased stress resistance to hyperthermia and oxidative stress. At the same time, Aronia extract did not significantly affect the resistance of flies to starvation. ABE supplementation has increased expression of heat shock proteins (Hsp27, Hsp68, Hsp83), oxidative stress resistance genes (Keap1, NRF, Sod1), some circadian clock genes (Clk, per) and gene of longevity Sirt1.

pharmacology and toxicology↗

bioRxiv2019

Gray whale transcriptome reveals longevity adaptations associated with DNA repair, autophagy and ubiquitination

One important question in aging research is how differences in genomics and transcriptomics determine the maximum lifespan in various species. Despite recent progress, much is still unclear on the topic, partly due to the lack of samples in non-model organisms and due to challenges in direct comparisons of transcriptomes from different species. The novel ranking-based method that we employ here is used to analyze gene expression in the gray whale and compare its de novo assembled transcriptome with that of other long- and short-lived mammals. Gray whales are among the top 1% longest-lived mammals. Despite the extreme environment, or maybe due to a remarkable adaptation to its habitat (intermittent hypoxia, Arctic water, and high pressure), gray whales reach at least the age of 77 years. In this work, we show that long-lived mammals share common gene expression patterns between themselves, including high expression of DNA maintenance and repair, ubiquitination, apoptosis, and immune responses. Additionally, the level of expression for gray whale orthologs of pro- and anti-longevity genes found in model organisms is in support of their alleged role and direction in lifespan determination. Remarkably, among highly expressed pro-longevity genes many are stress-related, reflecting an adaptation to extreme environmental conditions. The conducted analysis suggests that the gray whale potentially possesses high resistance to cancer and stress, at least in part ensuring its longevity. This new transcriptome assembly also provides important resources to support the efforts of maintaining the endangered population of gray whales.

evolutionary biology↗