Biology subjects

Kerepesi, C.

Publications and source records attributed to Kerepesi, C..

16 recordsLinked to original sources

bioRxiv2026

Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

systems biology↗

bioRxiv2026

Rapid direct neuronal reprogramming of human dental pulp stem cells

Direct neuronal reprogramming offers an alternative to induced pluripotent stem cell-based differentiation by converting somatic cells directly into neurons without passage through pluripotency. However, commonly used fibroblast-based protocols are often slow and inefficient. Here, we evaluated human dental pulp stem cells (DPSCs), which originate from the cranial neural crest and possess intrinsic neurogenic potential, as a developmentally relevant source of induced neurons (iNs). Using an all-in-one lentiviral vector, we converted DPSCs into iNs within 17 days, compared with 28 days for fibroblasts reprogrammed with the same vector, and achieved significantly higher neuronal purity under the respective established protocols. Multi-omic profiling revealed coordinated suppression of mesenchymal and cell-cycle programs and induction of neuronal, synaptic, and metabolic pathways. Single-nucleus RNA sequencing resolved fibroblast-like, transitional, maturing neuronal, GABAergic-like, and alternative fates, while trajectory inference suggested divergent neuronal and non-neuronal conversion paths. Whole-cell recordings showed that a subset of DPSC-iNs developed early neuronal excitability and voltage-gated inward and outward currents. Together, our findings establish DPSCs as an accessible and developmentally relevant source for rapid direct neuronal conversion. This integrated molecular, single-nucleus, and electrophysiological characterization defines the cellular heterogeneity of DPSC-to-neuron reprogramming and provides a framework for protocol refinement and future patient-specific disease modeling.

neuroscience↗

bioRxiv2026

An inflammatory gene set driven epigenetic clock tracks down disease progression and rejuvenation

Chronic, low-level inflammation, characterized by elevated pro-inflammatory programs, including epigenetic changes, in the absence of infection, is a major driver of aging and age-related diseases. On the other side of the spectrum, aging interventions work, at least in part, by decreasing inflammation. However, the molecular connection between epigenetic aging and inflammatory profiles in chronic diseases and rejuvenation has not been established yet. This study aimed to investigate the role of a newly described inflammatory signature gene set (ISig) in aging, previously associated with accelerated aging, in the progression of chronic diseases and rejuvenation. To achieve this, we developed inflammation-derived epigenetic aging clocks using ElasticNet regression models trained on CpG sites from ISig promoter regions. The newly developed inflammation aging clocks were validated on healthy samples and tested for their capacity to detect accelerated aging in diseased samples and rejuvenation during cellular reprogramming. The data demonstrate that the ISig inflammatory clocks accurately predict age, detect rejuvenation, and identify accelerated aging in disease contexts. Furthermore, we have demonstrated that it is possible to use a curated inflammatory gene-set with biological relevance to estimate biological age acceleration. We also developed a web application, the GeneClock Studio (available at https://ilab.sztaki.hu/geneclockstudio/), that allows researchers to apply the inflammatory aging clocks to their own DNA methylation datasets without requiring any programming expertise. Furthermore, the GeneClockStudio supports the training of new aging clocks based on an arbitrarily selected gene set in a similar way as in the case of the ISig inflammatory clocks.

bioinformatics↗

bioRxiv2025

Improving face age prediction by using multiple-angle photos

Face photo-based age provides a cost-effective and readily accessible tool for biological age studies. However, face photo age models were usually trained on a single front-view photo per subject. Here, we hypothesized that face photo-based age prediction performance might be improved by using multiple photos of the same subject at the same time, captured from different angles. To test this hypothesis, we used an available dataset containing mugshots and developed age prediction models trained on (i) only front-view images, (ii) only the side-view images, and (iii) both front and side images. We found that accurate age prediction is possible using side photos despite the smaller facial area compared to front-facing photos (MAE = 3.1 years for the front-view and MAE = 3.7 years for the side-view images). The age prediction performance further improved by using two images from one person at the same time, captured from two different angles, front and side (MAE = 2.9 years). We found that subjects who age faster based on front-view face photos generally age faster based on side-view face photos. We also found that side-view models handle the rotation of the face better compared to the front-view model. In summary, we showed that two photos at different angles can improve age prediction and may provide a better approach to determining biological age for personalized medicine and rejuvenation studies.

bioinformatics↗

bioRxiv2025

CpGeneAge: multi-omics aging clocks associated with Nf-κB signaling pathway in aging

Aging clocks have emerged as the primary tools for measuring biological aging and have been developed for a wide range of single-omic measurements. Epigenetic aging clocks showed high accuracy in age prediction, however, their biological interpretation is still a challenging task. Transcriptomics aging clocks provide better interpretability but worse age prediction accuracy. To exploit the benefits of both omics techniques, the main goal of this study was to develop the first multi-omics aging clocks based on combined epigenetics and transcriptomics features. For this purpose, we utilized a dataset where reduced representation bisulfite sequencing (RRBS) and RNA-seq measurements were measured at the same time for peripheral blood samples of 182 individuals. Then we trained machine learning models (ElasticNet) using the methylation and gene expression features at the same time. While the most accurate models tended to use exclusively methylation features, we were able to develop highly accurate multi-omics aging clocks too (called CpGenAge). Both the canonical and the non-canonical Nf-{kappa}B signaling pathways, with the genes EDA, EDA2R, EDARADD, and CD70, were overrepresented among the gene expression features of the multi-omics aging clocks. The EDARADD, which is a unique hallmark of aging, was represented among both the gene expression and methylation features. By developing single-omic clocks on the same multi-omics dataset, we found that epigenetic age acceleration and transcriptomics age acceleration do not correlate with each other, further supporting the benefits of our multi-omics approach. In summary, here, we demonstrate that multi-omics aging clocks are useful tools to investigate aging and biological age at the multi-omics level.

bioinformatics↗

bioRxiv2025

Face photo-based age acceleration predicts all-cause mortality and differs among occupations

While scientists argue what aging is and what drives aging, it is widely accepted that our face changes drastically with age and that mortality increases in late life. We hypothesize that people of the same age can be biologically older than others and that the human face may reflect accelerated molecular aging. To test this hypothesis we examine the associations of face photo-based age acceleration with mortality and lifestyle. For this purpose, we trained and tested artificial intelligence models on 442,110 photos of famous people. We found that face photo-based age predicts all-cause mortality for middle-aged and older individuals meaning that those age faster based on their face photo die sooner. We also found that, based on face photos, sport is the slowest aging occupation among famous people consistently to previous findings showing the benefits of exercise to epigenetic aging. Overall, we demonstrate that the face photo-base age model approaches biological age in some extent and provides a low-cost and fast complementary measurement for personalized medicine, as well as aging and rejuvenation studies. The model is available for demonstration and academic research purposes at https://photoage.sztaki.hu/.

bioinformatics↗

bioRxiv2025

Human brain cell-type-specific aging clocks based on single-nuclei transcriptomics

Aging is the primary risk factor for most neurodegenerative diseases, yet the cell-type-specific progression of brain aging remains poorly understood. Here, we developed human cell-type-specific transcriptomic aging clocks using high-quality single-nucleus RNA sequencing data from post mortem human prefrontal cortex tissue of 31 donors aged 18 - 94 years, encompassing 73,941 high-quality nuclei. We observed distinct transcriptomic changes across major cell types, including upregulation of inflammatory response genes in microglia from older samples. Aging clocks trained on each major cell type accurately predicted chronological age and remained robust in independent single-nucleus RNA-sequencing datasets, underscoring their broad applicability. These findings demonstrate the feasibility of cell-type-specific transcriptomic clocks to measure biological aging in the human brain and highlight potential mechanisms of selective vulnerability in neurodegenerative diseases. We anticipate these clocks will serve as a basis for further studies in other brain regions and more diverse populations, ultimately advancing our understanding of age-related neurodegenerative processes at the single-cell level.

neuroscience↗

bioRxiv2024

Organ specificity and commonality of epigenetic aging in low- and high-running capacity rats

BackgroundEpigenetic drift, which are gradual age-related changes in DNA methylation patterns, plays a significant role in aging and age-related diseases. However, the relationship between exercise, epigenetics, and aging and the molecular mechanisms underlying their interactions are poorly understood. The aim of this study was to investigate the relationship between cardiorespiratory fitness (CRF), epigenetic aging, and promoter methylation of individual genes across multiple organs in selectively bred low- and high-capacity runner (LCR and HCR) aged rats. MethodsIn this study, we performed reduced representation bisulfite sequencing (RRBS) on LCR and HCR aged rats, focusing on the hippocampus, heart, soleus muscle, and large intestine samples. We calculated various epigenetic indicators, including rat epigenetic clocks, global mean DNA methylation (GMM), mean methylation entropy (MME), and gene-specific promoter methylation. ResultsEpigenetic clocks, trained on available rat blood-derived RRBS data did not reflect differences in CRF between LCR and HCR rats across all four organs. However, we observed organ specific differences in GMM and MME between LCR and HCR rats. The direction of these differences was the opposite compared to the age-related changes in the rat blood suggesting that a high CRF may mitigate age-related epigenetic changes in an organ-specific manner. Notably the soleus muscle, exhibited the most pronounced differences in promoter methylation due to CRF. We also identified seven genes whose promoter methylation was consistently influenced by CRF in all four organs. Moreover, we found that age acceleration of the soleus muscle was significantly higher compared to the heart and the hippocampus, and significantly lower compared to the large intestine. Finally, we found that the age acceleration was not consistent across organs. ConclusionsOur data suggest that CRF associates with epigenetic aging in an organ-specific manner and regulates the promoter methylation of individual genes in an organ-specific and organ-common manner. Our findings provide important insights into the biology of aging and emphasize the need to validate rejuvenation strategies in the context of the organ-specific nature of epigenetic aging.

physiology↗

bioRxiv2023

ClockBase: a comprehensive platform for biological age profiling in human and mouse

Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent, a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

bioinformatics↗

bioRxiv2022

Biological age is increased by stress and restored upon recovery

Aging is classically conceptualized as an ever-increasing trajectory of damage accumulation and loss of function, leading to increases in morbidity and mortality. However, recent in vitro studies have raised the possibility of age reversal. Here, we report that biological age is fluid and exhibits rapid changes in both directions. By applying advanced epigenetic aging clocks, we find that the biological age of young mice is increased by heterochronic parabiosis and restored following surgical detachment of animals. We also identify transient changes in biological age during major surgery, pregnancy, and severe COVID-19 in humans and/or mice. Together, these data show that biological age undergoes a rapid increase in response to diverse forms of stress, which is reversed following recovery from stress. Our study uncovers a new layer of aging dynamics that should be considered in future studies. Elevation of biological age by stress may be a quantifiable and actionable target for future interventions.

systems biology↗

bioRxiv2022

Deterioration of the human transcriptome with age due to increasing intron retention and spurious splicing

Adult aging is characterized by a progressive deterioration of biological functions at physiological, cellular and molecular levels, but its damaging effects on the transcriptome are not well characterized. Here, by analyzing splicing patterns in [~]1,000 human subjects sampled across multiple tissues, we found that splicing fidelity declines with age. Most prominently, genuine introns fail to be spliced out, manifesting as a broad surge in intron retention, and this is exacerbated by the increase in diverse spurious exon-exon junctions with age. Both of these effects are prominently detected in the majority of human tissues. Collectively, they result in the progressive deterioration of the active transcriptome, wherein functional mRNAs are increasingly diluted with non-functional splicing isoforms. We discuss the concept of "splicing damage" and formulate methods to quantify it. Using these tools, we show that splicing damage increases both with age and with the incidence of diseases. Altogether, this work uncovers transcriptome damage as a critical molecular indicator of human aging and healthspan.

genomics↗

bioRxiv2021

Multi-omic rejuvenation and lifespan extension upon exposure to youthful circulation

Heterochronic parabiosis (HPB) is known for its functional rejuvenation effects across several mouse tissues. However, its impact on the biological age of organisms and their long-term health remains unknown. Here, we performed extended (3-month) HPB, followed by a 2-month detachment period of anastomosed pairs. Old detached mice exhibited improved physiological parameters and lived longer than control isochronic mice. HPB drastically reduced the biological age of blood and liver based on epigenetic analyses across several clock models on two independent platforms; remarkably, this rejuvenation effect persisted even after 2 months of detachment. Transcriptomic and epigenomic profiles of anastomosed mice showed an intermediate phenotype between old and young, suggesting a comprehensive multi-omic rejuvenation effect. In addition, old HPB mice showed transcriptome changes opposite to aging, but akin to several lifespan-extending interventions. Altogether, we reveal that long-term HPB can decrease the biological age of mice, in part through long-lasting epigenetic and transcriptome remodeling, culminating in the extension of lifespan and healthspan.

cell biology↗

bioRxiv2021

Ultra-cheap and scalable epigenetic age predictions with TIME-Seq

Epigenetic "clocks" based on DNA methylation (DNAme) have emerged as the most robust and widely employed aging biomarkers, but conventional methods for applying them are expensive and laborious. Here, we develop Tagmentation-based Indexing for Methylation Sequencing (TIME-Seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-Seq, we applied multi-tissue and tissue-specific epigenetic clocks to over 1,600 mouse DNA samples. We also discovered a novel approach for age prediction from shallow sequencing (e.g., 10,000 reads) by adapting scAge for bulk measurements. In benchmarking experiments, TIME-Seq performed favorably against prevailing methods and could quantify the effects of interventions thought to accelerate, slow, and reverse aging in mice. Finally, we built and validated a highly accurate human blood clock from 1,056 demographically representative individuals. Our methods increase the scalability and reduce the cost of epigenetic age predictions by more than 100-fold, enabling accurate aging biomarkers to be applied in more large-scale animal and human studies.

genomics↗

bioRxiv2021

Profiling epigenetic age in single cells

DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multi-variate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of bulk tissue with remarkable accuracy. However, intrinsic sparsity and digitized methylation in individual cells have so far precluded the assessment of aging in single cell data. Here, we present scAge, a probabilistic approach to determine the epigenetic age of single cells, and validate our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulate chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggest that while tissues age in a coordinated fashion, some cells age more or less rapidly than others. We show that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue show a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age of individual cells, the latter supporting a natural rejuvenation event during gastrulation. scAge is both robust against the low coverage that is characteristic of single cell sequencing techniques and is flexible for studying any cell type and vertebrate organism of interest. This study demonstrates for the first time the potential for accurate epigenetic age profiling at single-cell resolution.

genomics↗

bioRxiv2021

Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging

The notion that germline cells do not age goes back to the 19th century ideas of August Weismann. However, being in a metabolically active state, they accumulate damage and other age-related changes over time, i.e., they age. For new life to begin in the same young state, they must be rejuvenated in the offspring. Here, we developed a new multi-tissue epigenetic clock and applied it, together with other aging clocks, to track changes in biological age during mouse and human prenatal development. This analysis revealed a significant decrease in biological age, i.e. rejuvenation, during early stages of embryogenesis, followed by an increase in later stages. We further found that pluripotent stem cells do not age even after extensive passaging and that the examined epigenetic age dynamics is conserved across species. Overall, this study uncovers a natural rejuvenation event during embryogenesis and suggests that the minimal biological age (the ground zero) marks the beginning of organismal aging.

developmental biology↗

bioRxiv2021

Aging predisposes B cells to malignancy by activating c-Myc and perturbing the genome and epigenome

While cancer is an age-related disease, many cancer studies utilize younger animal models. Here, we uncover how a cancer, B-cell lymphoma, develops as a consequence of a naturally aged system. We show that this malignancy is associated with increased cell size, splenomegaly, and a newly discovered age-associated clonal B-cell (ACBC) population. Driven by exogenous c-Myc activation, hypermethylated promoters and somatic mutations, ACBC cells clonally expand independent of germinal centers (IgM+) and show increased biological age and hypomethylation in partially methylated domains related to mitotic solo-CpGs. Epigenetic changes in transformed mouse B cells are enriched for changes observed in human B-cell lymphomas. Mechanistically, the data suggest that cancerous ACBC cells originate from age-associated B cells, in part involving CD22 protein signaling fostered by the aging microenvironment. Transplantation assays demonstrate that ACBC evolve to become self-sufficient and support malignancy when transferred into young recipients. Inhibition of mTOR or c-Myc in old mice attenuates premalignant changes in B cells during aging and emerges as a therapeutic strategy to delay the onset of age-related lymphoma. Together, we show how aging contributes to B-cell lymphoma through a previously unrecognized mechanism involving cell-intrinsic changes and the aged microenvironment, characterize a model that captures the origin and progression of spontaneous cancer during aging and identify candidate interventions against age-associated lymphoma.

cancer biology↗