Biology subjects

Kriukov, D.

Publications and source records attributed to Kriukov, D..

8 recordsLinked to original sources

bioRxiv2025

Somatic mutations impose an entropic upper bound on human lifespan

Somatic mutations accumulate with age and can cause cell death, but their quantitative contribution to limiting human lifespan remains unclear. We developed an incremental modeling framework that progressively incorporates factors contributing to aging into a model of population survival dynamics, which we used to estimate lifespan limits if all aging hallmarks were eliminated except somatic mutations. Our analysis reveals fundamental asymmetry across organs: post-mitotic cells such as neurons and cardiomyocytes act as critical longevity bottlenecks, with somatic mutations reducing median lifespan from a theoretical non-aging baseline of 430 years to 169 years. In contrast, proliferating tissues like liver maintain functionality for thousands of years through cellular replacement, effectively neutralizing mutation-driven decline. Multi-organ integration predicts median lifespans of 134-170 years --approximately twice current human longevity. This substantial yet incomplete reduction indicates that somatic mutations significantly drive aging but cannot alone account for observed mortality, implying comparable contributions from other hallmarks.

systems biology↗

bioRxiv2025

The Divergent 3D Genome Landscapes of Aging and Neurodegenerative mouse models

Chromatin structure is essential for gene regulation and genome stability, and neurons must preserve their 3D genome organization throughout life. This structure gradually deteriorates with aging and is further disrupted in neurodegenerative diseases. To investigate whether aging and neurodegeneration share early chromatin changes or diverge, we performed Hi-C on cortical neurons from adult, aged, and brain-specific SIRT6-knockout (S6-KO) mice, and compared them to the CK-p25 Alzheimers disease model results stratified by {gamma}H2AX levels. All models showed early features such as weakened interactions between chromosomes in expanded nuclei, A-to-B compartment shifts, and loss of architectural loops. However, aged neurons retained TAD prominence, short-range interactions, and enhancer-promoter loops, while pathological models showed reduced TAD prominence, shorter loops, and disorganized A-B mixing. These changes correlate with increased DNA damage. Our findings suggest that aging represents a "primed" state, where chromatin regulation begins to erode, but further stressors like DNA damage are associated with progression toward neurodegenerative breakdown.

molecular biology↗

bioRxiv2024

ComputAgeBench: Epigenetic Aging Clocks Benchmark

The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages, and tested 13 published clock models. Additionally, we compiled 46 separate datasets to facilitate the training of new aging clocks. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging. Codehttps://github.com/ComputationalAgingLab/ComputAge Datasethttps://huggingface.co/datasets/computage/computage_bench

bioinformatics↗

bioRxiv2023

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

systems biology↗

bioRxiv2023

Unveiling Rotational Structure in Neural Data

Spatiotemporal properties of the activity of neuronal populations in cortical motor areas have been the subject of many experimental and theoretical investigations, which generated numerous inter-pretations regarding the mechanisms of preparing and executing limb movements. Two competing models, namely representational and dynamical models, strive to explain the temporal course of neuronal activity and its relationship to different parameters of movements. One proposed dynamical model employs the jPCA method, a dimensionality reduction technique, to holistically characterize oscillatory activity in a population of neurons by maximizing rotational dynamics that are present in the data. Different interpretations have been proposed for the rotational dynamics revealed with jPCA approach in various brain areas. Yet, the nature of such dynamics remains poorly understood. Here we conducted a comprehensive analysis of several neuronal-population datasets. We found that rotational dynamics were consistently accounted for by a travelling wave pattern. To quantify the rotation strength, we developed a complex-valued measure termed the gyration number. Additionally, we identified the parameters influencing the extent of rotation in the data. Overall, our findings suggest that rotational dynamics and travelling waves are the same phenomena, which requires reevaluation of the previous interpretations where they were considered as separate entities.

neuroscience↗

bioRxiv2023

On standardization of controls in lifespan studies

The search for interventions to slow down and even reverse aging is a burgeoning field. The literature cites hundreds of supposedly beneficial pharmacological and genetic interventions in model organisms: mice, rats, flies and worms, where research into physiology is routinely accompanied by lifespan data. Naturally the negative results are more frequent, yet scientifically quite valuable if analyzed systematically. Yet, there is a strong "discovery bias", i.e. results of interventions which turn out not to be beneficial remain unpublished. Theoretically, all lifespan data is ripe for re-analysis: we could contrast the molecular targets and pathways across studies and help focus the further search for interventions. Alas, the results of most longevity studies are difficult to compare. This is in part because there are no clear, universally accepted standards for conducting such experiments or even for reporting such data. The situation is worsened by the fact that the authors often do not describe experimental conditions completely. As a result, works on longevity make up a set of precedents, each of which might be interesting in its own right, yet incoherent and incomparable. Here we point out specific issues and propose solutions for quality control by checking both inter- and intra-study consistency of lifespan data.

systems biology↗

bioRxiv2022

Longevity and rejuvenation effects of cell reprogramming are decoupled from loss of somatic identity

Partial somatic cell reprogramming has been touted as a promising rejuvenation strategy. However, its association with mechanisms of aging and longevity at the molecular level remains unclear. We identified a robust transcriptomic signature of reprogramming in mouse and human cells that revealed co-regulation of genes associated with reprogramming and response to lifespan-extending interventions, including those related to DNA repair and inflammation. We found that age-related gene expression changes were reversed during reprogramming, as confirmed by transcriptomic aging clocks. The longevity and rejuvenation effects induced by reprogramming in the transcriptome were mainly independent of pluripotency gain. Decoupling of these processes allowed predicting interventions mimicking reprogramming-induced rejuvenation (RIR) without affecting somatic cell identity, including an anti-inflammatory compound osthol, ATG5 overexpression, and C6ORF223 knockout. Overall, we revealed specific molecular mechanisms associated with RIR at the gene expression level and developed tools for discovering interventions that support the rejuvenation effect of reprogramming without posing the risk of neoplasia.

systems biology↗

bioRxiv2021

Hi-C Resolution Enhancement with Genome Sequence Data

The increasing interest in chromatin conformation inside the nucleus and the availability of genome-wide experimental data make it possible to develop computational methods that can increase the quality of the data and thus overcome the limitations of high experimental costs. Here we develop a deep-learning approach for increasing Hi-C data resolution by appending additional information about genome sequence. In this approach, we utilize two different deep-learning algorithms: the image-to-image model, which enhances Hi-C resolution by itself, and the sequence-to-image model, which uses additional information about the underlying genome sequence for further resolution improvement. Both models are combined with the simple head model that provides a more accurate enhancement of initial low-resolution Hi-C data. The code is freely available in a GitHub repository: https://github.com/koritsky/DL2021_HI-C.

systems biology↗