Dataset Information

Advances in Nutritional Epigenetics-A Fresh Perspective for an Old Idea. Lessons Learned, Limitations, and Future Directions.

ABSTRACT: Nutritional epigenetics is a rapidly expanding field of research, and the natural modulation of the genome is a non-invasive, sustainable, and personalized alternative to gene-editing for chronic disease management. Genetic differences and epigenetic inflexibility resulting in abnormal gene expression, differential or aberrant methylation patterns account for the vast majority of diseases. The expanding understanding of biological evolution and the environmental influence on epigenetics and natural selection requires relearning of once thought to be well-understood concepts. This research explores the potential for natural modulation by the less understood epigenetic modifications such as ubiquitination, nitrosylation, glycosylation, phosphorylation, and serotonylation concluding that the under-appreciated acetylation and mitochondrial dependant downstream epigenetic post-translational modifications may be the pinnacle of the epigenomic hierarchy, essential for optimal health, including sustainable cellular energy production. With an emphasis on lessons learned, this conceptional exploration provides a fresh perspective on methylation, demonstrating how increases in environmental methane drive an evolutionary down regulation of endogenous methyl groups synthesis and demonstrates how epigenetic mechanisms are cell-specific, making supplementation with methyl cofactors throughout differentiation unpredictable. Interference with the epigenomic hierarchy may result in epigenetic inflexibility, symptom relief and disease concomitantly and may be responsible for the increased incidence of neurological disease such as autism spectrum disorder.

SUBMITTER: Sedley L

PROVIDER: S-EPMC7750768 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Advances in Nutritional Epigenetics-A Fresh Perspective for an Old Idea. Lessons Learned, Limitations, and Future Directions.

Sedley Lynda L

Epigenetics insights 20201218

Nutritional epigenetics is a rapidly expanding field of research, and the natural modulation of the genome is a non-invasive, sustainable, and personalized alternative to gene-editing for chronic disease management. Genetic differences and epigenetic inflexibility resulting in abnormal gene expression, differential or aberrant methylation patterns account for the vast majority of diseases. The expanding understanding of biological evolution and the environmental influence on epigenetics and natu ...[more]

PMID: 33415317

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Project description:Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Dataset Information

Advances in Nutritional Epigenetics-A Fresh Perspective for an Old Idea. Lessons Learned, Limitations, and Future Directions.

Publications

Advances in Nutritional Epigenetics-A Fresh Perspective for an Old Idea. Lessons Learned, Limitations, and Future Directions.

Similar Datasets

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