Biology subjects

Moqri, M.

Publications and source records attributed to Moqri, M..

16 recordsLinked to original sources

bioRxiv2026

Systemic control and inter-organ communication of ovarian aging

Whether ovarian aging is intrinsic and unidirectional or dynamically regulated by systemic context remains an open question for fertility and women's healthspan. To address it, we developed a mouse ovarian transcriptomic aging clock (OvaAge) and applied it, together with published transcriptomic, epigenetic and pathway-resolved DNA methylation (PathAge) clocks, multi-omic analyses and reproductive outcomes, to new reciprocal heterochronic ovary transplantation and parabiosis experiments. Ovarian molecular age shifted in both directions: aged ovaries became younger in young hosts and young ovaries became older in aged hosts, and in each case the shift was partial across transcriptomic, epigenetic and pathway-resolved measures. Ovaries from 13-month-old C57BL/6J donors supported full-term pregnancies and live births at 17-18 months of age when transplanted into young hosts, but not when transplanted into old hosts, and young ovaries supported live births in 17-18-month-old recipients. Donor ovary age altered the transcriptomic age of the uterus in both directions, while young ovaries also lowered the transcriptomic age of aged liver and brain, identifying the ovary as an active node within an inter-organ aging network. Across unilateral and bilateral transplantation and parabiosis, an older environment increased inflammatory gene expression in the ovary, whereas a younger environment reduced it and increased mitochondrial and biosynthetic programs. Integration of methylation, expression and cell-type markers linked these changes to declining oocyte-associated programs and an expanding stromal compartment enriched for multinucleated giant cells (MNGCs). MNGC-rich regions formed a reversibly remodeled, focally aged niche with distinct spatial metabolic features, and a related macrophage expression signature was detected in human ovaries. Together, these findings identify ovarian aging as a modular, bidirectionally plastic process that is regulated by the systemic environment and point to tractable intervention points.

systems biology↗

bioRxiv2025

DNA Methylation Ageing Atlas Across 17 Human Tissues

Aging involves widespread epigenetic remodeling across tissues, yet the nature and consistency of these changes remain unclear. We conducted a meta-analysis of more than 15,000 human methylomes spanning 17 tissues, identifying both conserved and tissue-specific aging signatures. We examined linear changes via differentially methylated positions, variability shifts via variably methylated positions, and Shannon-entropy to capture methylation disorder. Network analysis revealed fragile co-methylation modules largely resistant to beneficial perturbation. Key disruptors, including PCDHGA1, MEST, HDAC4, and HOX genes, exacerbated aging signals across tissues. Notably, a resilient module enriched for NAD{square} salvage metabolism supports therapeutic targeting of NAD{square} in aging. PCDHGA1 emerged as a conserved cross-tissue driver, suggesting protocadherin-mediated adhesion plays a broader role in maintaining structural and signaling stability in multiple organ systems. Our open-access atlas provides a foundational resource for dissecting the molecular architecture of human aging and identifying testable targets for intervention, biomarkers, and translational epigenetic therapies.

bioinformatics↗

bioRxiv2024

Profiling Epigenetic Aging at Cell-Type Resolution Through Long-Read Sequencing

DNA methylation can give rise to robust biomarkers of aging, yet most studies profile it at the bulk tissue level, which masks cell type-specific alterations that may follow distinct aging trajectories. Long-read sequencing technology enables methylation profiling of extended DNA fragments, which allows mapping to their cell type of origin. In this study, we introduce a framework for evaluating cell type-specific aging using long-read sequencing data, without the need for cell sorting. Leveraging cell type-specific methylation patterns, we map long-read fragments to individual cell types and generate cell type-specific methylation profiles, which are used as input to a newly developed probabilistic aging model, LongReadAge, capable of predicting epigenetic age at the cell-type level. We apply LongReadAge to track aging of myeloid cells and lymphocytes from bulk leukocyte data as well as circulating cell-free DNA, demonstrating robust performance in predicting age despite limited shared features across samples. This approach provides a novel method for profiling the dynamics of epigenetic aging at cell-type resolution.

bioinformatics↗

bioRxiv2024

An Open Competition for Biomarkers of Aging

Open scientific competitions have successfully driven biomedical advances but remain underutilized in aging research, where biological complexity and heterogeneity require methodological innovations. Here, we present the results from Phase I of the Biomarkers of Aging Challenge, an open competition designed to drive innovation in aging biomarker development and validation. The challenge leverages a unique DNA methylation dataset and aging outcomes from 500 individuals, aged 18 to 99. Participants are asked to develop novel models to predict chronological age, mortality, and multi-morbidity. Results from the chronological age prediction phase show important advances in biomarker accuracy and innovation compared to existing models. The winning models feature improved predictive power and employ advanced machine learning techniques, innovative data preprocessing, and the integration of biological knowledge. These approaches have led to the identification of novel age-associated methylation sites and patterns. This challenge establishes a paradigm for collaborative aging biomarker development, potentially accelerating the discovery of clinically relevant predictors of aging-related outcomes. This supports personalized medicine, clinical trial design, and the broader field of geroscience, paving the way for more targeted and effective longevity interventions.

bioinformatics↗

bioRxiv2024

MethylGPT: a foundation model for the DNA methylome

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the models attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

systems biology↗

bioRxiv2024

High-dimensional Ageome Representations of Biological Aging across Functional Modules

The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organisms overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

systems biology↗

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗

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↗

bioRxiv2024

Integrative epigenetics and transcriptomics identify aging genes in human blood

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these "multi-omic aging genes" in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

systems biology↗

bioRxiv2023

Biolearn, an open-source library for biomarkers of aging

Aging biomarkers are essential for understanding and quantifying the aging process and developing targeted longevity interventions. However, validation of these tools has been hindered by the lack of standardized approaches for cross-population validation, disparate biomarker designs, and inconsistencies in dataset structures. To address these challenges, we developed Biolearn, an open-source library that provides a unified framework for the curation, harmonization, and systematic evaluation of aging biomarkers. Leveraging Biolearn, we conducted a comprehensive evaluation of various aging biomarkers across multiple datasets. Our systematic approach involved three key steps: (1) harmonizing existing and novel aging biomarkers in standardized formats; (2) unifying public datasets to ensure coherent structuring and formatting; and (3) applying computational methodologies to assess the harmonized biomarkers against the unified datasets. This evaluation yielded valuable insights into the performance, robustness, and generalizability of aging biomarkers across different populations and datasets. The Biolearn python library, which forms the foundation of this systematic evaluation, is freely available at https://Bio-Learn.github.io. Our work establishes a unified framework for the curation and evaluation of aging biomarkers, paving the way for more efficient and effective clinical validation and application in the field of longevity research.

bioinformatics↗

bioRxiv2023

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

bioinformatics↗

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

Nature of epigenetic aging from a single-cell perspective

Age-related changes in DNA methylation (DNAm) form the basis for the development of most robust predictors of age, epigenetic clocks, but a clear mechanistic basis for exactly what part of the aging process they quantify is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the aging dynamics of tissue and single-cell DNAm (scDNAm) with scDNAm changes during early development, and corroborate our analyses with a single-cell RNAseq analysis within the same multi-omics dataset. We show that epigenetic aging involves co-regulated changes, but it is dominated by the stochastic component, and this agrees with transcriptional coordination patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns, providing new opportunities for targeting aging and evaluating longevity interventions.

systems biology↗

bioRxiv2022

Epigenetic profiling and incidence of disrupted development point to gastrulation as aging ground zero in Xenopus laevis

Recent studies suggest the existence of a natural rejuvenation event during early embryonic development of mice, followed by epigenetic aging. Here, by profiling embryonic DNA methylation in the African clawed frog, Xenopus laevis, we found that the epigenetic entropy basepoint maps to the gastrulation stage of embryogenesis and corresponds to a rapid increase in embryo transcript abundance. We further developed a frog aging clock, revealing that this species epigenetically ages. Application of this clock to developmental stages identified a decrease in epigenetic age during early embryogenesis, with the minimal age reached around gastrulation. By examining individual developmental trajectories of 6,457 embryos, we found that this stage is also accompanied by a higher incidence of disrupted development. Taken together, our data point to gastrulation as a critical stage for aging and natural rejuvenation, characterized by the lowest epigenetic age, increased mortality, nadir of DNA methylation entropy and rapid increase in embryo transcript abundance, defining aging ground zero as the basepoint where rejuvenation ends and the aging process begins.

genetics↗

bioRxiv2022

Multi-omics analysis reveals a crucial role for Retinoic Acid in promoting epigenetic and transcriptional competence of an in vitro model of human Pharyngeal Endoderm.

In vitro differentiation of human Pluripotent Stem Cells (hPSCs) into different cell types has enabled the study of developmental processes that are impossible to dissect in vivo. This innovation has allowed for the derivation of therapeutically relevant cell types that can be used for downstream applications and studies. The Pharyngeal Endoderm (PE) is considered an extremely relevant developmental tissue since it acts as a precursor to a plethora of organ systems such as Esophagus, Parathyroids, Thyroids, Lung, and Thymus. While several studies have highlighted the importance of these cells, an in vitro platform to generate human PE cells is still missing. Here we fill this knowledge gap, by providing a novel in vitro protocol for the derivation of bona fide PE cells from hPSCs. We demonstrated that our PE cells robustly express Pharyngeal Endoderm markers, they are transcriptionally similar to PE cells isolated from in vivo mouse development and represent a transcriptionally homogeneous population. Importantly, we elucidated the contribution of Retinoic Acid in promoting a transcriptional and epigenetic rewiring of PE cells. In addition, we defined the epigenetic landscape of PE cells by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of these data led to the identification of new putative regulatory regions and to the generation of a gene regulatory network orchestrating the development of PE cells. By combining hPSCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and modeling of their developmental defects in genetic syndromes.

developmental biology↗

bioRxiv2022

PRC2 clock: a universal biomarker of aging and rejuvenation

DNA methylation (DNAm) is one of the most reliable biomarkers for aging across many mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, age-dependent DNAm gain is less specified. Multiple studies have demonstrated that polycomb repressive complex 2 (PRC2) targets are enriched among the CpG sites which gain methylation with age. However, a systematic whole-genome examination of all PRC2 targets in the context of aging methylome as well as whether these associations are pan-tissue or tissue-specific is lacking. Here, by analyzing DNAm data from different assays and from multiple young and old human and mouse tissues, we found that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells gain methylation with age in all examined somatic mitotic cells. We also estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the "PRC2 clock," defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells. In addition, we demonstrate the application of this biomarker in the evaluation of different anti-aging interventions, including dietary restriction and partial epigenetic reprogramming.

genomics↗