Biology subjects

Chen, B. H.

Publications and source records attributed to Chen, B. H..

5 recordsLinked to original sources

bioRxiv2025

The blood proteome predicts the impact of circulating factors on age-related mitochondrial health

Circulating non-cellular factors, such as plasma proteins, contribute to various features of aging. To determine the impacts of endogenous circulating factors on human age-related bioenergetic decline, we treated primary human fibroblasts with serum samples representing the adult life-course. Our results demonstrate that the maximal mitochondrial bioenergetic capacity of fibroblasts treated with serum is negatively correlated with the chronological and epigenetic age of the serum donor. Using targeted proteomics, we identified plasma proteins associated with the bioenergetic effects of serum. We then utilized elastic net, a linear regression modeling technique, to derive a novel proteomic signature of age-related mitochondrial differences. MitoAge is a 25-protein signature of age-related mitochondrial health that predicts the systemic bioenergetic effects of circulating factors and is related to differences in physical function across human aging. Signatures that report on cellular hallmarks of aging, such as mitochondrial function, represent a new generation of mechanistically-informed biomarkers of biological aging. GRAPHICAL ABSTRACTIn this study, we describe the development of a novel proteomic signature, MitoAge. This signature was developed by utilizing human primary fibroblasts treated with serum samples representing the adult human life-course and analyzing how resulting respirometry correlated with chronological age, epigenetic age, and abundance of serum proteins. Utilizing machine learning techniques, we derived a 25-protein signature which can predict bioenergetics and physical features related to aging. Application and utility of this signature may be used to identify novel drivers of health and longevity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/687483v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@905c12org.highwire.dtl.DTLVardef@18ca82forg.highwire.dtl.DTLVardef@175d397org.highwire.dtl.DTLVardef@966faa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Cross Tissue DNAm Biomarker Prediction using Transfer Learning

DNA methylation (DNAm) is an epigenetic mechanism vital for regulating gene expression and influencing disease states. Developing accurate DNAm biomarkers often requires data from specific tissues, which are sometimes difficult to access. This study explores the use of Transfer Learning (TL) to predict blood DNAm biomarkers using saliva DNAm data, aiming to overcome limitations posed by sample size and tissue accessibility. We developed TL-based algorithms that integrate DNAm data from multiple tissues. These algorithms were evaluated against traditional Lasso regression and direct saliva DNAm estimates. Our results show that TL significantly improves the prediction accuracy of DNAm biomarkers, outperforming traditional methods in 20 out of 26 biomarkers. We further validated our models using independent datasets, demonstrating that TL-derived predictions reflect known biological relationships, such as sex differences in telomere length and the impact of smoking on DNAm biomarkers. Our findings highlight the potential of TL in enhancing DNAm biomarker prediction across tissues, providing a valuable tool for epigenetic research. The developed algorithms and methodologies are accessible to researchers, fostering advancements in personalized medicine and aging research. This study establishes a framework for utilizing TL to bridge the gap between accessible and pertinent tissue data, paving the way for more accurate and versatile DNAm biomarker applications. ACM Reference FormatKristen M McGreevy, Brian H Chen, Steve Horvath, and Donatello Telesca. 2024. Cross Tissue DNAm Biomarker Prediction using Transfer Learning. 1, 1 (June 2024), 43 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2024

mLiftOver: Harmonizing Data Across Infinium DNA Methylation Platforms

Infinium DNA methylation BeadChips are widely used for genome-wide DNA methylation profiling at the population scale. Recent updates to probe content and naming conventions in the EPIC version 2 (EPICv2) arrays have complicated integrating new data with previous Infinium array platforms, such as the EPIC and the HumanMethylation450 (HM450) BeadChip. We present mLiftOver, a user-friendly tool that transfers probe ID, methylation level, and signal intensity data across different Infinium platforms. It manages probe replicates, missing data imputation, and platform-specific bias for accurate data conversion. We validated the tool by applying HM450-based cancer classifiers to EPICv2 cancer data, achieving high accuracy. Additionally, we successfully integrated EPICv2 healthy tissue data with legacy HM450 data for tissue identity analysis and produced consistent copy number profiles in cancer cells. Availability and implementationmLiftOver is implemented R and available in the Bioconductor package SeSAMe (version 3.21.13+): https://bioconductor.org/packages/release/bioc/html/sesame.html Analysis of EPIC and EPICv2 platform-specific bias and high-confidence mapping is available at https://github.com/zhou-lab/InfiniumAnnotationV1/blob/main/Anno/EPICv2/EPICv2ToEPIC_conversion.tsv.gz The source code is available at https://github.com/zwdzwd/sesame/blob/devel/R/mLiftOver.R under the MIT license.

bioinformatics↗

bioRxiv2022

DNA Methylation Dynamics and Dysregulation Delineated by High-Throughput Profiling in the Mouse

We have developed a mouse Infinium DNA methylation array that contains 297,415 probes to capture the diversity of mouse DNA methylation biology. We present a mouse DNA methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, age, sex, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, PDX assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. ApcMin/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This MM285 mouse array is widely accessible to the research community, and will accelerate future high sample-throughput studies in this important model organism.

genomics↗

bioRxiv2017

GWAS of epigenetic ageing rates in blood reveals a critical role for TERT

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9,907 individuals, we found gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in 3 loci associated extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggested causal influences of menarche and menopause on IEAA and lipid levels on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) locus at 5p15.33 confer higher IEAA (P<2.7x10-11). Causal modelling indicates TERT-specific and independent effects on LTL and IEAA. Experimental hTERT expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the DNA methylation clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.

genetics↗