Biology subjects

Sehgal, R.

Publications and source records attributed to Sehgal, R..

13 recordsLinked to original sources

bioRxiv2026

CpG Atlas: A centralized multi-layer database and AI interface for DNA methylation research

DNA methylation research has vastly expanded over the past decade, producing a wealth of epigenome-wide association studies, biomarker algorithms such as epigenetic clocks, technical performance analyses, and functional annotations for CpG sites. However, these resources remain fragmented across dozens of databases and supplementary files within manuscripts, forcing researchers to spend time and effort on data cleaning and integration prior to meaningful analyses. No single resource currently unifies this information into a centralized, easy-to-query framework. Here, we present CpG Atlas, a curated relational database that integrates 18 distinct annotation layers encompassing over 1.2 million CpG sites across all four generations of Illumina methylation arrays (HM450K, EPIC v1, EPIC v2, and MSA). Built on a snowflake schema with a canonical probe identifier hub implemented in SQL, CpG Atlas consolidates over 800,000 CpG-trait associations, results from Mendelian randomization analyses, CpG membership across 81 epigenetic clocks, array manifest information, and probe reliability data. It further includes specialized layers such as solo-WCGW, CoRSIVs, PRC2 binding, transposon and retroelement annotations, tissue-specific differentially methylated positions across 17 tissues, and hallmarks of aging and cancer. To maximize utility and ease of use, the database is paired with an interactive web tool and a natural language-to-SQL query interface, enabling users to quickly perform complex multi-dimensional queries. Detailed documentation about every data source and table is also provided, facilitating the identification and interpretation of relevant studies. We demonstrate the utility of CpG Atlas through two case studies: a systematic enrichment analysis revealing distinct functional signatures across 16 epigenetic clocks, and an iterative biomarker discovery workflow for IBD that leverages cross-layer integration. Because it is readily scalable simply by adding or updating tables in the database, CpG Atlas provides a continuously evolving and extensible infrastructure for the epigenetics community that supports collaborative research, interpretable biomarker development, and integrative analyses across the growing landscape of epigenetic data.

bioinformatics↗

bioRxiv2025

TranslAGE: A Comprehensive Platform for Systematic Validation of Epigenetic Aging Biomarkers

Epigenetic clocks are powerful biomarkers of biological aging, however, their performance varies across studies and contexts. Current limitations include siloed datasets, inconsistent validation methods, and the absence of a standardized framework for systematic comparison. Here, we introduce TranslAGE: a publicly available online resource that addresses this gap by harmonizing 179 human blood DNA methylation datasets and precalculating a suite of 41 epigenetic biomarker scores for each of the >42,000 total samples. Users can explore these data through interactive dashboards that evaluate four fundamental performance domains: Stability, Treatment response, Associations, and Risk, collectively forming the STAR framework. Stability quantifies robustness to multiple types of technical and biological noise. Treatment response measures biomarker sensitivity to aging interventions and environmental exposures. Associations capture cross-sectional relationships with age, demographics, disease, and other phenotypes, and Risk assesses predictive power for future functional decline, morbidity and mortality. The STAR framework unifies these test metrics into a single composite scoring system that enables researchers to identify, benchmark, and validate biomarkers best suited to their scientific or clinical applications. TranslAGE will be continually updated, with rapid scaling by adding datasets, biomarkers, or analyses. By providing harmonized datasets, precomputed biomarker scores, and interactive data tools, TranslAGE establishes the first standardized, reproducible framework for benchmarking epigenetic aging biomarkers across populations, and accelerates the translation toward clinical use.

genomics↗

bioRxiv2025

Beyond the Genotype: A Multi-Omic Analysis of APOEe4's Role in Alzheimer's Disease

Alzheimers disease (AD) is characterized by widespread molecular dysregulation, with the APOEe4 allele recognized as its strongest genetic risk factor. However, the mechanisms by which APOEe4 drives distinct molecular changes - whether by exacerbating pathology or triggering compensatory responses - remain incompletely understood. We generated and analyzed proteomic, epigenetic, and genetic data from post-mortem dorsolateral prefrontal cortex samples of a uniquely APOEe4-enriched subset of the Religious Orders Study and Memory and Aging Project (ROSMAP). Specifically, we generated DIA LC-MS proteomic data (n = 302), analyzed previously generated DNA methylation profiles from our group (n = 310), and used published whole-genome sequencing data (n = 254) to compute polygenic risk scores (PRS). In this cohort, 69% (n = 214) were APOEe4 carriers, and 19.6% (n = 42) of them showed no pathological evidence of AD based on NIA-Reagan criteria, enabling identification of APOEe4-related risk and resilience mechanisms. In the absence of AD, APOEe4 carriers exhibited lower levels of 27 proteins, suggesting early synaptic (e.g., VAMP1, SYN3, CASKIN1) and metabolic (e.g., GLUD1, PI4KA) vulnerability. By contrast, APOEe4 carriers with AD displayed marked upregulation of inflammatory and proteostatic proteins (e.g., GNAO1, AHNAK, FGG, HEBP1, APEX1, RAB4A, SLC12A5, LRP1, BAG6) and hypermethylation of cg06329447 in ELAVL4. Network analyses highlighted convergent disruptions in synaptic transmission, metabolism, and proteostasis - key pathways altered in APOEe4-associated AD. Mediation analyses identified GRIPAP1 and GSTK1 as top protein mediators (accounting for [~]26-33% of APOEe4s effect), with VAMP1, CASKIN1, DPP3, SYN3, and FGG each contributing [~]9-15%. ELAVL4 hypermethylation also mediated [~]12% of the APOEe4 effect, linking epigenetic dysregulation to disease risk. To assess whether the identified proteins reflected broader genetic risk for AD or were specific to APOEe4, we calculated PRS both excluding and including the APOE genomic region. While the non-APOE PRS showed no association with identified molecular markers, the APOE-inclusive PRS was significantly associated with eight AD-related proteins in carriers, indicating they are not explained by polygenic risk outside of APOE. Finally, predictive modeling stratified by APOEe4 status revealed that in non-carriers, PRS most effectively classified AD (AUC = 0.73), whereas in carriers, proteomic and epigenetic markers outperformed PRS (AUC up to 0.74). Together, these findings demonstrate that APOEe4 confers AD risk through early synaptic and metabolic disruptions and later-stage inflammatory and epigenetic changes, laying the groundwork for genotype-tailored biomarker development and therapeutic strategies. VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=156 HEIGHT=200 SRC="FIGDIR/small/682426v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1477a2forg.highwire.dtl.DTLVardef@1a6d173org.highwire.dtl.DTLVardef@1010821org.highwire.dtl.DTLVardef@bcde3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2025

Biological versus Technical Reliability of Epigenetic Clocks and Implications for Disease Prognosis and Intervention Response

DNA methylation-based aging biomarkers, or epigenetic clocks, are increasingly used to estimate biological age and predict health outcomes. Their translational utility, however, depends not only on predictive accuracy but also on reliability, the ability to provide consistent results across technical replicates and repeated biological measures. Here, we leveraged the TranslAGE platform to comprehensively evaluate the technical and biological reliability of 18 Epigenetic clocks, including chronological predictors, mortality predictors, pace-of-aging measures, reliable variants, and newer explainable clocks. Technical reliability was quantified across four independent datasets. For standard replicate assays on EPIC and 450K arrays, nearly all clocks achieved excellent technical reproducibility. However, some clocks showed dramatic drops in technical reliability based on differences in slide position and DNA extraction protocol. PC-based clocks, especially PCGrimAge and SystemsAge remained technically reliable in all cases. In contrast, biological reliability, measured across repeated samples collected within hours, before and after meals, under acute stress, across environmental exposures, and over days, was markedly lower, with most clocks showing only moderate stability. PCGrimAge was the only clock with good ICC > 0.75 for biological reliability. Importantly, technical reproducibility did not predict biological reliability; clocks that were technically robust often proved biologically unreliable. We further demonstrated that reliability directly constrains downstream applications. Clocks with higher ICCs produced more stable prognostic associations with cognitive decline and more consistent responsiveness to a vegan diet intervention, whereas unreliable clocks yielded highly variable or spurious effects. Together, these findings reveal that technical reliability is not enough: biological reliability remains a critical limitation for many DNA methylation clocks that constrains their utility, and our work provides a roadmap for prioritizing next-generation clocks most suited for clinical translation.

bioinformatics↗

bioRxiv2024

CpGPT: a Foundation Model for DNA Methylation

DNA methylation is a type of epigenetic modification that plays a significant role in development, aging, and disease. Despite extensive research, how genome-wide DNA methylation patterns collectively encode and influence complex phenotypes such as aging and disease remains difficult to characterize with conventional approaches. Foundation models are a class of machine learning model that leverage vast quantities of data to make sense of complex data types, such as genome sequences or single-cell transcriptomes. Here, we present the Cytosine-phosphate-Guanine Pretrained Transformer (CpGPT), a novel foundation model pretrained on CpGCorpus, a novel database with more than 2,000 DNA methylation datasets encompassing over 150,000 samples from diverse conditions. CpGPT lever-ages an improved transformer architecture to learn comprehensive representations of methylation patterns, allowing it to impute and reconstruct genome-wide methylation profiles from limited input data. By capturing sequence, positional, and epigenetic contexts, CpGPT outperforms specialized models when finetuned for aging-related tasks, including the state-of-the-art GrimAge2 and PCGrimAge for mortality and morbidity estimation. The model is highly adaptable and can impute beta values across different methylation platforms, tissue types, mammalian species, and even single-cell data. As a foundation model, CpGPT can be leveraged as a new tool for biological discovery in the field of epigenetics. The open-source code and model can be found at http://github.com/lucascamillomd/CpGPT. HighlightsO_LICpGPT is a novel foundation model for DNA methylation analysis, pretrained on over 2,000 datasets encompassing 150,000+ samples. C_LIO_LIThe model demonstrates strong performance in zero-shot tasks including imputation, array conversion, and reference mapping. C_LIO_LICpGPT achieves state-of-the-art results in mortality prediction and chronological age estimation. C_LI

systems biology↗

bioRxiv2024

DNAm aging biomarkers are responsive: Insights from 51 longevity interventional studies in humans

Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging. Epigenetic clocks are aging biomarkers based on DNA methylation with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically. Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock. With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers. For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other, pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects. Clocks with multiple sub-scores (i.e. "explainable clocks") provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks. Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.

systems biology↗

bioRxiv2024

When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions.

Recent human studies have suggested that aging interventions can reduce aging biomarkers related to morbidity and mortality risk. Such biomarkers may potentially serve as early, rapid indicators of effects on healthspan. An increasing number of studies are measuring intervention effects on epigenetic clocks, commonly used aging biomarkers based on DNA methylation profiles. However, with dozens of clocks to choose from, different clocks may not agree on the effect of an intervention. Furthermore, changes in some clocks may simply be the result of technical noise causing a false positive result. To address these issues, we measured the variability between 6 popular epigenetic clocks across a range of longitudinal datasets containing either an aging intervention or an age-accelerating event. We further compared them to the same clocks re-trained to have high test-retest reliability. We find the newer generation of clocks, trained on mortality or rate-of-aging, capture aging events more reliably than those clocks trained on chronological age, as these show consistent effects (or lack thereof) across multiple clocks including high-reliability versions, and including after multiple testing correction. In contrast, clocks trained on chronological age frequently show sporadic changes that are not replicable when using high-reliability versions of those same clocks, or when using newer generations of clocks and these results do not survive multiple-testing correction. These are likely false positive results, and we note that some of these clock changes were previously published, suggesting the literature should be re-examined. This work lays the foundation for future clinical trials that aim to measure aging interventions with epigenetic clocks, by establishing when to attribute a given change in biological age to a bona fide change in the aging process.

genomics↗

bioRxiv2023

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

systems biology↗

bioRxiv2021

Efficacy of longevity interventions in C. elegans is determined by early life activity of RNA splicing factors

Geroscience aims to target the aging process to extend healthspan. However, even isogenic individuals show heterogeneity in natural aging rate and responsiveness to pro-longevity interventions, limiting translational potential. Using in vivo mini gene reporters in isogenic C. elegans, we show that alternative splicing of mRNAs related to lipid metabolism in young animals is coupled to subsequent life expectancy. Further, activity of RNA splicing factors REPO-1 and SFA-1 early in life modulates effectiveness of specific longevity interventions via POD-2/ACC1 and regulation of lipid utilization. In addition, early inhibition of REPO-1 renders animals refractory to late onset suppression of the TORC1 pathway. Together these data suggest that activity of RNA splicing factors and the metabolic landscape early in life can modulate responsiveness to longevity interventions and may explain variance in efficacy between individuals. One Sentence SummaryEfficacy of pro-longevity interventions in C. elegans is determined by the activity of splicing factors and the lipid metabolic landscape early in the life of the individual.

genetics↗

bioRxiv2021

Neuronal mTORC1 inhibition promotes longevity without suppressing anabolic growth and reproduction in C. elegans

One of the most robust and reproducible methods to prolong lifespan in a variety of organisms is inhibition of the mTORC1 (mechanistic target of rapamycin complex 1) pathway. mTORC1 is a metabolic sensor that promotes anabolic growth when nutrients are abundant. Inhibition of mTORC1 extends lifespan, but also frequently has other effects such as stunted growth, slowed development, reduced fertility, and disrupted metabolism. It has long been assumed that suppression of anabolism and resulting phenotypes such as impaired growth and reproduction may be causal to mTORC1 longevity, but this hypothesis has not been directly tested. RAGA-1 is an upstream activator of TORC1. Previous work from our lab using a C. elegans model of mTORC1 longevity, the long-lived raga-1 null mutant, found that the presence of raga-1 only in the neurons suppresses longevity of the null mutant. Here, we use the auxin-inducible degradation (AID) system to test whether neuronal mTORC1 inhibition is sufficient for longevity, and whether any changes in lifespan are also linked to stunted growth or fertility. We find that life-long AID of RAGA-1 either in all somatic tissue or only in the neurons of C. elegans is sufficient to extend lifespan. We also find that AID of RAGA-1 or LET-363/mTOR beginning at day 1 of adulthood extends lifespan to a similar extent. Unlike somatic degradation of RAGA-1, neuronal degradation of RAGA-1 doesnt impair growth, slow development, or decrease the reproductive capacity of the worms. Lastly, while AID of LET-363/mTOR in all somatic cells shortens lifespan, neuronal AID of LET-363/mTOR slows aging. This work demonstrates that targeting mTORC1 specifically in the neurons uncouples longevity from growth and reproductive impairments, challenging previously held ideas about the mechanisms of mTORC1 longevity and elucidating the promise of tissue-specific aging therapeutics.

genetics↗

bioRxiv2021

Pyruvate kinase M1 suppresses development and progression of prostate adenocarcinoma

Most cancers, including prostate cancers, express the M2 splice isoform of pyruvate kinase (Pkm2). This isoform can promote anabolic metabolism to support cell proliferation; however, Pkm2 expression is dispensable for many cancers in vivo. Pyruvate kinase M1 (Pkm1) isoform expression is restricted to relatively few tissues and has been reported to promote growth of select tumors, but the role of PKM1 in cancer has been less studied. Pkm1 is expressed in normal prostate tissue; thus, to test how differential pyruvate kinase isoform expression affects cancer initiation and progression we generated mice harboring a conditional allele of Pkm1 and crossed this allele, as well as a Pkm2 conditional allele, to a Pten loss-driven prostate cancer model. We found that Pkm1 loss leads to Pkm2 expression and accelerates prostate cancer, while deletion of Pkm2 leads to increased Pkm1 expression and suppresses cancer. Consistent with these data, a small molecule pyruvate kinase activator that mimics a PKM1-like state suppresses progression of established prostate tumors. PKM2 expression is retained in most human prostate cancers, arguing that pharmacological PKM2 activation may be beneficial for some prostate cancer patients.

cancer biology↗

bioRxiv2021

Secretome of senescent hepatoma cells modulate macrophage polarization and neutrophil extracellular traps formation

Presence of dysfunctional senescent hepatocyte is a hallmark feature of cirrhosis. We now report the presence of senescent hepatocytes (p21 and p53 positive) in vicinity of infiltrated immune cells in hepatocellular carcinoma. Hence, we checked if senescent cells can alter fate of macrophage polarization and neutrophil extracellular trap (NETs) formation. Using an in vitro assay, senescence was induced in hepatoma cells (HepG2 and Huh7 cells) by doxorubicin treatment and senescent cell showed secretory phenotype with strong expression of cytokines (IL1{beta}, IL6, IL8 and IL13) as evaluated by Flow cytometry. The senescent secretome from hepatoma cells induced macrophage differentiation predominantly with M2 markers (CD80, CD86) while that of non-senescent cell induced M1 phenotype (CD163, CD206) as analysed by flow cytometry. Human hepatocellular carcinoma harbouring senescent hepatocytes showed presence of M2 macrophages, while M1 macrophages were predominant in non-tumorous region. Additionally, the senescent secretome from Huh7 cells (p53mut) enhanced the NETs formation, while HepG2 (p53+/+) secretome had an inhibitory affect In conclusion, the "pro-inflammatory" senescent secretome drives non-inflammatory type M2 macrophage polarization and modulate neutrophil traps thereby modulating the microenvironment towards tumor promotion. Targeting senescent hepatocyte secretome appears a promising therapeutic target in liver cancer in future. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/423905v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1df4256org.highwire.dtl.DTLVardef@7c4f53org.highwire.dtl.DTLVardef@13bfe80org.highwire.dtl.DTLVardef@1c1c4b5_HPS_FORMAT_FIGEXP M_FIG Work Highlight (Diagrammatic Representation). C_FIG

cancer biology↗

bioRxiv2018

Localisation and hypersecretion of Nerve Growth Factor in breast Phyllodes tumours: evidence from a preliminary study

BackgroundThe pathophysiology of the breast phyllodes tumours is uncertain. Currently, wide surgical removal is the only available treatment option. The histopathological diagnosis of phyllodes tumours is often confused with that of fibroadenomas due to a striking histological resemblance; hence a distinctive biomarker for this tumour type is warranted.\n\nMaterial & MethodsFresh human breast tissue was obtained from surgically excised breast phyllodes and fibroadenoma tumours (test, 2 cases each), breast cancer (positive control, 2 cases) and normal breast tissue (negative control, 1 case). Immunohistochemistry was performed for the detection of nerve growth factor (NGF) on frozen sections of the test and control tissues fixed in 4% paraformaldehyde, using the indirect streptavidin-biotin-peroxidase complex method. Sandwich ELISA on tissue homogenates of the same test and control cases was also performed to validate the immunohistochemical findings.\n\nResultsA marked difference in NGF expression was detected in phyllodes tumours compared to fibroadenomas. The maximum NGF expression was observed in phyllodes tissue followed by cancer tissue, and the least expression in fibroadenomas (3-5 times less than in phyllodes; comparable with normal breast tissue).\n\nConclusionNGF is known for its growth inducing potential in breast cancer, but its secretion by a benign breast tumour is not known in literature. This study reports abundant NGF secretion by breast phyllodes, raising the possibility of its potential role in tumour pathogenesis and progression that can be exploited therapeutically in future. We also propose that NGF may be used as a distinct biomarker of phyllodes tumours, for differentiating them from fibroadenomas during histopathology.

cancer biology↗