Biology subjects

Higgins-Chen, A. T.

Publications and source records attributed to Higgins-Chen, A. T..

12 recordsLinked to original sources

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

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↗

bioRxiv2024

Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes--those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.

genomics↗

bioRxiv2023

Stochastic Epigenetic Mutations: Reliable Detection and Cardiovascular Associations

Stochastic Epigenetic Mutations (SEMs) have been proposed as novel aging biomarkers that have the potential to capture heterogeneity in age-related DNA methylation (DNAme) changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide (CpG) sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. While individual SEMs are rarely consistent across subjects, the SEM load - the total number of SEMs - increases with age. However, given poor technical reliability of measurement for many DNA methylation sites, we posited that many outliers might represent technical noise. Our study of whole blood samples from 36 individuals, each measured twice, found that 23.3% of hypoSEM and 45.6% hyperSEM are not shared between replicates. This diminishes the reliability of SEM loads, where intraclass correlation coefficients are 0.96 for hypoSEM and 0.90 for hyperSEM. We linked SEM reliability to multiple factors, including blood cell type composition, probe beta-value statistics, and presence of SNPs. A machine learning approach, leveraging these factors, filtered unreliable SEMs, enhancing reliability in a separate dataset of technical replicates from 128 individuals. Analysis of the Framingham Heart Study confirmed previously reported SEM association with mortality and revealed novel connections to cardiovascular disease. We discover that associations with aging outcomes are primarily driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations are preserved after filtering out unreliable SEMs and are enhanced after adjusting for blood cell composition. Finally, we utilize these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which utilizes parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

bioinformatics↗

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↗

bioRxiv2022

R methylCIPHER: A Methylation Clock Investigational Package for Hypothesis-Driven Evaluation & Research

BackgroundEpigenetic clocks are promising tools for the study of aging in humans. The clocks quantify biological aging above and beyond chronological age, demonstrate systematic associations with risk factors that accelerate aging, and predict age-related morbidity and mortality. There is interest in using them as surrogate endpoints in intervention studies. However, the large number of clocks, decentralized publication and explosive popularity in the last decade has made for poor accessibility and standardization. This has hampered the abilities of new researchers to conduct truly hypothesis driven research--whether by not knowing about the best available clocks for a given question, or by systematically testing many or all as they become available. ResultsWe report a centralized R package which can be installed and run locally on the users machine, and provides a standardized syntax for epigenetic clock calculation. The package includes a set of helper functions to assist with navigating clock literature and selecting clocks for analysis, as well as affording the user with the details of clock calculation. We describe each clocks resilience to missing CpG information, combined with functionality to assess the need for imputation in the users own data. Furthermore, we demonstrate that while CpGs may not be shared among clocks with similar outputs, many clocks have highly correlated outputs. ConclusionsDue to the previous decentralization of epigenetic clocks, gathering code and performing systematic analysis, particularly in protected datasets, has required significant information gathering effort. Here, we offer an R package with standardized implementation and potential for future growth and clock incorporation to assist with hypothesis driven investigation of aging as measured by epigenetic clocks. We show the potential of this package to drive the user to think globally about signals captured by epigenetic clocks, as well as to properly identify the potential and limitations of each clock in their current research.

bioinformatics↗

bioRxiv2022

Aging the Brain: Multi-Region Methylation Principal Component Based Clock in the Context of Alzheimer's Disease

Alzheimers disease (AD) risk increases exponentially with age and is associated with multiple molecular hallmarks of aging, one of which is epigenetic alterations. Epigenetic age predictors based on 5 cytosine methylation (DNAm) have previously suggested that biological age acceleration may occur in AD brain tissue. To further investigate brain epigenetic aging in AD, we generated a novel age predictor termed PCBrainAge that was trained solely in cortical samples. This predictor utilizes a combination of principal components analysis and regularized regression, which reduces technical noise and greatly improves test-retest reliability. For further testing, we generated DNAm data from multiple brain regions in a sample from the Religious Orders Study and Rush Memory & Aging Project. PCBrainAge captures meaningful heterogeneity of aging, calculated according to an individuals age acceleration beyond expectation. Its acceleration demonstrates stronger associations with clinical AD dementia, pathologic AD, and APOE {varepsilon}4 carrier status compared to extant epigenetic age predictors. It does so across multiple cortical and subcortical regions. Overall, PCBrainAge is useful for investigating heterogeneity in brain aging, as well as epigenetic alterations underlying AD risk and resilience.

bioinformatics↗

bioRxiv2022

Clock Work: Deconstructing the Epigenetic Clock Signals in Aging, Disease, and Reprogramming

Epigenetic clocks have come to be regarded as powerful tools for estimating aging. However, a major drawback in their application is our lack of mechanistic understanding. We hypothesize that uncovering the underlying biology is difficult due to the fact that epigenetic clocks are multifactorial composites: They are comprised of disparate parts, each with their own causal mechanism and functional consequences. Thus, only by deconstructing epigenetic clock signals will it be possible to glean biological insight. Here we clustered 5,717 clock CpGs into twelve distinct modules using multi-tissue and in-vitro datasets. We show that epigenetic clocks are comprised of different proportions of modules, which may explain their discordance when simultaneously applied in a given study. We also observe that epigenetic reprogramming does not reset the entire clock and instead the observed rejuvenation is driven by a subset of modules. Overall, two modules stand-out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The light-second module is moderately responsive to reprogramming; is very accelerated in tumor vs. normal tissues; and tracks with passaging in vitro even as population doublings decelerate. Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.

genetics↗

bioRxiv2021

OxPhos Dysfunction Causes Hypermetabolism and Reduces Lifespan in Cells and in Patients with Mitochondrial Diseases

Patients with primary mitochondrial diseases present with fatigue and multi-system disease, are often lean, and die prematurely, but the mechanistic basis for this clinical picture remains unclear. Integrating data from 17 cohorts of patients with mitochondrial diseases (n=690), we find that clinical mitochondrial disorders increase resting energy expenditure, a state termed hypermetabolism. In a longitudinal cellular model of primary patient-derived fibroblasts from multiple donors, we show that genetic and pharmacological disruptions of oxidative phosphorylation (OxPhos) similarly trigger increased energy consumption in a cell-autonomous manner, despite near-normal OxPhos coupling efficiency. Hypermetabolism is associated with mtDNA instability, activation of the integrated stress response, increased extracellular secretion of age-related cytokines and metabokines including GDF15, as well as an accelerated rate of telomere erosion and epigenetic aging, and a reduced Hayflick limit. Together with these dynamic measures, we have generated a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations in response to OxPhos dysfunction. The increased energetic cost of living, or hypermetabolism, in cells and organisms with OxPhos defects has important biological and clinical implications.

cell biology↗

bioRxiv2021

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

bioinformatics↗