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Professor Asaf Hellman and his team at the Hebrew University-Hadassah Medical School have made a significant stride in understanding methylation-directed regulatory networks. Their research provides a deeper understanding of the activation and suppression of genes responsible for diseases, particularly glioblastoma. This work is pivotal in understanding disease expression variations among patients, offering a new perspective on disease research and clinical applications.
Understanding DNA Methylation in Gene Regulation
In the quest to understand why common diseases manifest differently among patients, Prof. Hellman's team centered their research on the potential role of gene transcriptional variation. They meticulously generated a comprehensive DNA methylation-oriented dataset, focusing on driver genes in human glioblastomas. This effort was aimed at investigating the impact of positive and negative regulatory elements on gene expression variation between individuals.
Key Findings and Techniques
1. Methylation-Directed Regulation: The study highlights the complex interplay between enhancers and silencers within gene regulatory domains, influenced by DNA methylation. This understanding is crucial for developing targeted disease therapies, especially in cancer treatment.
2. High-Resolution Mapping: By employing high-resolution mapping, the team was able to discern DNA methylation's effect on gene expression within intact genomes. This approach is vital for understanding gene control mechanisms and paves the way for precision medicine.
3. Use of Mathematical Modeling: The researchers applied mathematical modeling to identify critical methylation sites that drive gene expression variations. This method underscores the significant role of epigenetics in disease progression and opens avenues for more accurate diagnostics and personalized therapies.
Professor Hellman notes, "The research unveils the existence of complex cis-regulatory networks that determine gene expression by combining the effects of positive and negative transcriptional inputs." This finding is instrumental in understanding why gene expression varies among individuals and holds the key to new methods for disease monitoring and treatment. Furthermore, this work acts as a guide to more effective disease management strategies, particularly in the context of cancer and other prevalent diseases.
Read the full publication in Genome Biology.
Understanding DNA Methylation in Gene Regulation
In the quest to understand why common diseases manifest differently among patients, Prof. Hellman's team centered their research on the potential role of gene transcriptional variation. They meticulously generated a comprehensive DNA methylation-oriented dataset, focusing on driver genes in human glioblastomas. This effort was aimed at investigating the impact of positive and negative regulatory elements on gene expression variation between individuals.
Key Findings and Techniques
1. Methylation-Directed Regulation: The study highlights the complex interplay between enhancers and silencers within gene regulatory domains, influenced by DNA methylation. This understanding is crucial for developing targeted disease therapies, especially in cancer treatment.
2. High-Resolution Mapping: By employing high-resolution mapping, the team was able to discern DNA methylation's effect on gene expression within intact genomes. This approach is vital for understanding gene control mechanisms and paves the way for precision medicine.
3. Use of Mathematical Modeling: The researchers applied mathematical modeling to identify critical methylation sites that drive gene expression variations. This method underscores the significant role of epigenetics in disease progression and opens avenues for more accurate diagnostics and personalized therapies.
Professor Hellman notes, "The research unveils the existence of complex cis-regulatory networks that determine gene expression by combining the effects of positive and negative transcriptional inputs." This finding is instrumental in understanding why gene expression varies among individuals and holds the key to new methods for disease monitoring and treatment. Furthermore, this work acts as a guide to more effective disease management strategies, particularly in the context of cancer and other prevalent diseases.
Read the full publication in Genome Biology.