January 04, 2025

DNA Methylation

Histone markers predict human age with accuracy comparable to DNA methylation clocks


In a recent study published in the journal Science Advances, a group of researchers investigated the role of histone modifications in human aging by developing and evaluating histone-specific age prediction models across tissues and cell types.

Background

Aging involves complex cellular and molecular changes, including epigenetic modifications like Deoxyribonucleic acid (DNA) methylation and histone marks. Age predictors, or "clocks," have been developed using DNA methylation, transcriptomics, and blood chemistry data, with methylation-based models achieving a median absolute error of ~4 years.

While histone marks offer an interpretable framework based on the histone code, their potential for constructing accurate age predictors remains underexplored. Research has shown age-related shifts in histone modifications, suggesting their utility in modeling aging. However, the study emphasizes that sample size plays a critical role in determining the accuracy and reliability of such predictors.

Further research is needed to fully understand their role and to establish histone-based clocks comparable to existing methylation-based predictors.

About the Study

Researchers collected 1,814 human tissue chromatin immunoprecipitation sequencing (ChIP-seq) samples from the Encyclopedia of DNA Elements (ENCODE) project in bigWig format to generate and interpret histone-based age predictors.

The samples included seven histone modifications: histone H3 lysine 4 trimethylation (H3K4me3), histone H3 lysine 27 acetylation (H3K27ac), histone H3 lysine 27 trimethylation (H3K27me3), histone H3 lysine 4 monomethylation (H3K4me1), histone H3 lysine 36 trimethylation (H3K36me3), histone H3 lysine 9 trimethylation (H3K9me3), and histone H3 lysine 9 acetylation (H3K9ac).

Data processing involved averaging the negative base-10 logarithm of P-values’ signals across gene bodies using Homo sapiens annotations from Ensembl release 105. Samples with substantial missing features were discarded, and missing values were encoded as zero.

Various genomic regions were analyzed, including intergenic regions and Cytosine-phosphate-Guanine (CpG) dinucleotides. Embryonic samples were encoded with gestational week adjustments, while anonymized samples over 90 were assigned an age of 90.

To test in vitro performance, 568 additional samples spanning 12 histone marks were collected. Imputed data from ENCODE’s Avocado dataset added 1,379 samples, enhancing the training dataset. Age predictors employed Elastic Net regularization-based feature selection, principal component analysis (PCA) with truncated support vector decomposition, and automatic relevance determination regression, all implemented in Python. Performance evaluation used 10-fold nested cross-validation to prevent artificially inflated accuracy metrics, excluding cancer samples.

Histone-based predictors were compared to DNA methylation-based predictors, with the study noting the impact of differences in sample size and dataset distributions on the comparison. Predictor interpretation involved gene set enrichment analysis (GSEA), selecting genes significantly contributing to age prediction accuracy. Statistical analyses employed Python packages, ensuring validation.

Study Results

Focusing on seven histone marks (H3K4me3, H3K27ac, H3K9ac, H3K9me3, H3K27me3, H3K36me3, and H3K4me1), researchers used data from 1,814 human tissue samples spanning 82 tissues and age groups ranging from embryonic stages to 90-plus years. The samples represented diverse biological contexts and were processed using standardized methods to ensure consistency and reliability.

To create age predictors, researchers reduced the dimensionality of the data by averaging negative log-transformed P-values for each histone modification across gene bodies. These values were then transformed to stabilize the variance. Uniform manifold approximation and projection (UMAP) and PCA revealed distinct clustering based on histone type, with some age-related trends emerging, particularly for samples over 70 years old.

Histone marks showed significant correlations with age, particularly the repressive marks H3K9me3 and H3K27me3, which decreased with age, and the activating mark H3K4me3, which increased. Notably, the study observed that signal variance for all histone marks increased with age, highlighting epigenetic drift as a key factor in declining regulation. These observations informed the development of multivariate age predictors using ElasticNet for feature selection, principal component analysis to reduce noise, and automatic relevance determination regression for age estimation.

The histone-specific age predictors demonstrated robust performance, with H3K4me3 achieving the highest accuracy (Pearson’s r = 0.94, median absolute error = 4.31 years). Comparisons with DNA methylation-based predictors indicated comparable accuracy, particularly for activating histone marks, though the paper notes that DNA methylation predictors often have a younger skew in sample age distributions, which can affect performance comparisons. Additional experiments with imputed and primary cell data confirmed the reliability and accuracy of the histone mark predictors.

GSEA and pathway analyses highlighted developmental processes, transcriptional regulation, and ribonucleic acid (RNA)-related pathways as key contributors to age prediction. Histone-coding genes and age-related genes such as Homeobox D8 (HOXD8), Thioredoxin Interacting Protein (TXNIP), and Period Circadian Regulator 1 (PER1) were strongly associated with histone mark changes.

The study also introduced a pan-histone, pan-tissue age predictor, which leverages shared age-related trends across histone marks. This model not only performed comparably to histone-specific predictors but also emphasized the shared epigenetic patterns across the genome that underpin aging.
Conclusions

Since the development of DNA methylation-based age predictors, biohorology has rapidly expanded, offering biomarkers like telomere length, transcriptomics, and proteomics. While DNA methylation clocks are accurate, interpreting them is often challenging due to ambiguous gene associations. In contrast, histone mark-based predictors reveal genes linked to development, circadian regulation, and aging. Using ChIP-seq data, researchers created age predictors from seven histone marks.

Crucially, the study demonstrated that models trained on one histone mark could predict age using another, underscoring the shared epigenetic information across histone modifications. This research highlights the interpretability and potential of histone mark-based predictors as a robust tool for understanding epigenetic aging and developing age estimation models.

DNA methylation, epigenetics, gene expression, cytosine modification, methyltransferases, CpG islands, histone modification, gene silencing, transcription regulation, genome stability, 5-methylcytosine, DNA demethylation, epigenomic profiling, chromatin remodeling, DNA methylation biomarkers, environmental epigenetics, DNA methylation and aging, epigenetic inheritance, epigenetic therapy, cancer epigenetics

#DNAmethylation, #Epigenetics, #GeneExpression, #CytosineModification, #Methyltransferases, #CpGIslands, #HistoneModification, #GeneSilencing, #TranscriptionRegulation, #GenomeStability, #5mC, #DNADeMethylation, #Epigenomics, #ChromatinRemodeling, #DNABiomarkers, #EnvironmentalEpigenetics, #EpigeneticsAndAging, #EpigeneticInheritance, #EpigeneticTherapy, #CancerEpigenetics

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January 03, 2025

Mitochondrial Mutations

Sweet Spot of Mitochondrial Mutations Fuels Cancer Growth

Moderate mitochondrial DNA mutations enhance leukemia growth, while high mutation levels halt tumor development.

Mitochondria are vital to energy production in cells and so play a key role in fueling cancer growth. However, how mitochondrial DNA (mtDNA) contributes to cancer has been unclear. Scientists at St. Jude Children’s Research Hospital studied varying levels of mutated mtDNA to see their effect on leukemia cells. They found that while cancer growth was blocked in cells in which all mitochondria contained mutated mtDNA, it was notably increased in cells with moderate amounts of mutated mtDNA. By amplifying an enzyme vital to energy production, the researchers we also able to restart cancer growth in leukemia cells with fully mutated mtDNA. Collectively, these findings highlight an unexplored connection between mitochondrial DNA and cancer cells’ metabolic function. The findings were published today in Science Advances.

mtDNA is found exclusively within mitochondria and contains just 37 genes, which are largely responsible for energy production. Mutations occur to this DNA in the same way as DNA found in the nucleus, but studying the effect these mutations have on cancer is much more challenging. Recent advances have allowed Mondira Kundu, MD, PhD, St. Jude Department of Cell & Molecular Biology, to begin to address this knowledge gap.

“The role of mitochondrial DNA mutations in cancer is controversial,” said Kundu. “Some papers suggest they are pro-tumorigenic, and others say they have no impact. It’s essentially been unknown.”

Leukemia thrives in mtDNA mutation ‘sweet spot’

Introducing individual mutations to mtDNA is challenging due to the large number of mitochondria within each cell. Instead, the researchers used a leukemia mouse model with a defective genetic proofreading system called Polg, which gradually accrues mtDNA mutations. By disrupting Polg’s proofreading function in either one (heterozygous) or both (homozygous) parental lines, the researchers could look at the burden that mtDNA mutations place on tumor growth based on the number of mitochondria with mutated mtDNA.

The researchers found that heterozygous mice (those with a moderate number of mutated mitochondria) seemed to amplify leukemia growth. Homozygous mice with a high number of mutations had the opposite effect, blocking tumor growth.

“Until now, researchers have been focusing on an all-or-nothing approach, thinking that a lot of mutation impairs tumor function,” Kundu explained, “but in terms of leukemia, our findings suggest that an intermediate level of mitochondrial mutations might promote leukemogenesis.”

This effect may be related to the ability of leukemia cells to reprogram their metabolism to thrive in a harsh tumor microenvironment (their plasticity). “The amount of metabolic stress [from mtDNA mutation] increases the plasticity of the cells,” she explained. “So, exposure to a little bit of metabolic stress in the heterozygous mice may increase the susceptibility to transformation by different oncogenes, whereas in the homozygous mice, they are basically shutting down. The impact on metabolism was so severe that it could not be overcome.”

Metabolic plasticity connects mtDNA and tumor growth

To explore the mechanisms behind this, the researchers looked at an enzyme called pyruvate dehydrogenase. This enzyme links the two stages of cellular respiration: glycolysis and the citric acid cycle. In doing so, pyruvate dehydrogenase helps regulate the metabolic plasticity of cells. The researchers found that by blocking the kinase “off switch” of pyruvate dehydrogenase, they could restore leukemia cells’ plasticity in the homozygous (high mutation) mice. These results suggest that the citric acid cycle shuts down in the homozygous models, so promoting it restores the growth of those cells.

Collectively, the findings provide clear evidence that low to medium levels of mtDNA mutations can contribute to leukemogenesis and that complete disruption of mitochondrial function can have the opposite effect, essentially halting tumor growth.

Mitochondrial DNA mutations, oxidative phosphorylation, mitochondrial genome, heteroplasmy, mtDNA copy number, mitochondrial dysfunction, mitochondrial diseases, somatic mutations, inherited mutations, oxidative stress, ATP production, bioenergetics, mitochondrial biogenesis, electron transport chain, mitochondrial ROS, mitochondrial repair mechanisms, mitochondrial dynamics, mitochondrial fusion, mitochondrial fission, mitochondrial quality control.

#Mitochondria #GeneticMutations #MitochondrialDNA #MitoDiseases #OxidativeStress #EnergyProduction #MitochondrialHealth #Bioenergetics #MitochondrialResearch #mtDNA #CellMetabolism #Genomics #MitochondrialFunction #MitoBiology #DNARepair #MitochondrialFusion #MitochondrialFission #OxPhos #ReactiveOxygenSpecies #HealthandGenetics

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January 02, 2025

Genetical News

Why some shorter parents have surprisingly tall kids ?


Height is often considered a straightforward inheritance from parents, but sometimes, shorter parents end up with unexpectedly tall children. This phenomenon may seem surprising at first, but it offers a fascinating glimpse into the complexities of genetics and ancestral traits.

This was highlighted by content creator Chirag Barjatya in a reel posted on Instagram, where he mentions, “You may have noticed that many celebrities who are not very tall have children who are much taller. For example, Saif Ali Khan’s children, Sachin Tendulkar’s son, Ronit Roy’s son, or Shah Rukh Khan’s children. We often assume that our height solely depends on our parents’ genetics, but that’s not entirely true. (sic).”

Factors like genetic recombination, environmental influences, and even recessive traits passed down through generations play a significant role in determining a child’s height. But how exactly do these elements combine to produce taller offspring from shorter parents? Let’s delve deeper with insights from an expert.

How does genetic recombination lead to a child being significantly taller than their parents?

“Genetic recombination is a critical process during the formation of reproductive cells (sperm and egg), where genetic material is shuffled and rearranged,” explains Dr Vinutha. This can result in unique combinations of height-related genes that may not have been expressed in the parents, leading to a taller child.

Height is a polygenic trait influenced by multiple genes. Recombination can consolidate favourable height-related genes, sometimes from ancestors, resulting in a noticeable height increase in offspring. Additionally, random genetic variations during recombination can activate latent genes that promote growth, according to studies in Nature Genetics.

Role ancestry plays in influencing traits like height

Ancestry significantly impacts height through genetic diversity across populations. “Scandinavian populations, for instance, tend to have taller average heights, possibly due to evolutionary adaptations favoring robust physical builds,” says Dr Vinutha. On the other hand, populations from resource-scarce regions often evolved shorter statures for metabolic efficiency.

Recessive genes for height can indeed skip generations. “If a grandparent carried recessive genes for tall stature that weren’t expressed in the parents, these genes might combine in the child, leading to unexpected height.” Similarly, mixed ancestry may produce traits that deviate from immediate family patterns.



Environmental or lifestyle factors that could amplify genetic predispositions for height

While genetics are primary, Dr Vinutha stresses the importance of the environment: “Adequate nutrition, hormonal health, physical activity, quality sleep, and avoiding growth inhibitors like smoking or alcohol are essential for reaching genetic height potential.”Nutrition: A diet rich in protein, calcium, and vitamins like D and C supports bone growth. Malnutrition during growth periods can stunt height.
Hormonal health: Growth hormone (GH) is crucial; disruptions like hypothyroidism or chronic stress can hinder growth.

Physical activity: Sports like swimming or basketball stimulate GH release and improve bone density.
Sleep: Most GH is released during deep sleep, making consistent rest critical.

Absence of Growth Inhibitors: Avoiding factors like smoking, alcohol, or chronic illnesses during adolescence can prevent the suppression of genetic height potential.

Could advancements in genetic studies one day allow parents to predict or influence their child’s height more accurately?

Dr Vinutha points to emerging technologies like polygenic risk scores (PRS), which analyze the cumulative effect of multiple genes on traits like height. “Studies in Cell show PRS can predict adult height with reasonable accuracy when combined with environmental data.”

Gene-editing tools like CRISPR could theoretically modify height-related genes, but this remains experimental and raises ethical concerns. “Epigenetics also offers hope, as optimising a child’s nutrition and health can influence the expression of growth-promoting genes,” she adds.

genetic diversity, epigenetics, inheritance, genes, recessive traits, genetic recombination, polygenic traits, environmental factors, height variability, epigenetic modifications, nutrition, exercise, hormonal health, growth spurts, puberty, genetic research, height predictors, heritability, height potential, surprising outcomes.

#Genetics #HeightInheritance #Epigenetics #GrowthSpurt #ChildHeight #HeightPotential #NutritionMatters #EnvironmentalFactors #GeneticResearch #HeightHeritability #PolygenicTraits #ParentalGenes #TallKids #UnexpectedGrowth #EpigeneticInfluence #RecessiveGenes #GeneticDiversity #HeightPredictors #SurprisingOutcomes #Heritability

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