How to silence an X chromosome

Trotman, Jackson B.; Calabrese, J. Mauro

doi:10.1038/d41586-020-00207-0

Cited by 5 publications

(3 citation statements)

References 19 publications

(19 reference statements)

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“…This imprinting event in females is established through epigenetic mechanisms including DNA and histone methylation (especially H3K27 trimethylation, H3K27me3), mediated by the action of PCR2. It has been shown that the PRC2 subunit binds to a non-coding RNA (ncRNA) within the Xist RNA and is thereby targeted to the female X chromosome, mediating its inactivation [ 2 , 20 , 25 – 28 ].…”

Section: Introductionmentioning

confidence: 99%

Epigenetic mechanisms regulate sex-specific bias in disease manifestations

et al. 2022

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Sex presents a vital determinant of a person’s physiology, anatomy, and development. Recent clinical studies indicate that sex is also involved in the differential manifestation of various diseases, affecting both clinical outcome as well as response to therapy. Genetic and epigenetic changes are implicated in sex bias and regulate disease onset, including the inactivation of the X chromosome as well as sex chromosome aneuploidy. The differential expression of X-linked genes, along with the presence of sex-specific hormones, exhibits a significant impact on immune system function. Several studies have revealed differences between the two sexes in response to infections, including respiratory diseases and COVID-19 infection, autoimmune disorders, liver fibrosis, neuropsychiatric diseases, and cancer susceptibility, which can be explained by sex-biased immune responses. In the present review, we explore the input of genetic and epigenetic interplay in the sex bias underlying disease manifestation and discuss their effects along with sex hormones on disease development and progression, aiming to reveal potential new therapeutic targets. Key messages Sex is involved in the differential manifestation of various diseases. Epigenetic modifications influence X-linked gene expression, affecting immune response to infections, including COVID-19. Epigenetic mechanisms are responsible for the sex bias observed in several respiratory and autoimmune disorders, liver fibrosis, neuropsychiatric diseases, and cancer.

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Section: Introductionmentioning

confidence: 99%

Epigenetic mechanisms regulate sex-specific bias in disease manifestations

et al. 2022

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“…DNAm refers to the process by which methyl groups are added to the C-5 position of the cytosine ring of DNA by DNA methyltransferases. This process plays a role in cellular development and aging, gene imprinting, X inactivation, and gene silencing to ensure genomic stability 10 , 11 . DNAm is a reversible biological process 12 and methylation patterns have been linked to treatment response in rheumatoid arthritis 13 , neurological disorders 14 , and cancer 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

“…SARS-CoV-2-mediated epigenetic changes have been implicated as a contributor to COVID-19 systemic toxicity and lethality 9 , suggesting DNA methylation (DNAm) signatures may provide a biomarker to predict disease severity and identify therapeutic targets.DNAm refers to the process by which methyl groups are added to the C-5 position of the cytosine ring of DNA by DNA methyltransferases. This process plays a role in cellular development and aging, gene imprinting, X inactivation, and gene silencing to ensure genomic stability 10,11 . DNAm is a reversible biological process 12 and methylation patterns have been linked to treatment response in rheumatoid arthritis 13 , neurological disorders 14 , and cancer 15,16 .…”

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confidence: 99%

A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity

Bowler

Παπουτσόγλου

Karanikas

et al. 2022

Sci Rep

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Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies.

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