3D genomics and its applications in precision medicine

Chen, Mengjie; Liu, Xingyu; Liu, Qingyou; Shi, Deshun; Li, Hui

doi:10.1186/s11658-023-00428-x

Cited by 3 publications

(3 citation statements)

References 139 publications

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“…Changes in EPI due to structural variation in enhancers and promoters as well as mutations in the TFs and architectural proteins that associate with them, or alterations in their binding sites, have all been implicated as drivers of oncogenic transformation 112 , 162 . Furthermore, the preponderance of GWAS SNPs are located in non-coding intronic and intergenic regions that are putative enhancers or other regulatory sites 112 , consistent with the possibility that alterations in EPI contribute to a multitude of pathological conditions emanating from various tissues 112 , 163 .…”

Section: Discussionmentioning

confidence: 61%

“…While dysregulation of EPIs can cause transcriptional changes in disease, they also present a promising target for therapeutic interventions 112 , 163 . A few epigenetic therapies that may impact EPI are already clinically available, including hypomethylating agents (HMAs) that inhibit DNMTs and histone deacetylase inhibitors (HDACis), for treatment of specific hematological malignancies 11 .…”

Section: Discussionmentioning

confidence: 99%

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Enhancer–promoter specificity in gene transcription: molecular mechanisms and disease associations

Friedman,

Wagner,

Lee

et al. 2024

Exp Mol Med

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Although often located at a distance from their target gene promoters, enhancers are the primary genomic determinants of temporal and spatial transcriptional specificity in metazoans. Since the discovery of the first enhancer element in simian virus 40, there has been substantial interest in unraveling the mechanism(s) by which enhancers communicate with their partner promoters to ensure proper gene expression. These research efforts have benefited considerably from the application of increasingly sophisticated sequencing- and imaging-based approaches in conjunction with innovative (epi)genome-editing technologies; however, despite various proposed models, the principles of enhancer–promoter interaction have still not been fully elucidated. In this review, we provide an overview of recent progress in the eukaryotic gene transcription field pertaining to enhancer–promoter specificity. A better understanding of the mechanistic basis of lineage- and context-dependent enhancer–promoter engagement, along with the continued identification of functional enhancers, will provide key insights into the spatiotemporal control of gene expression that can reveal therapeutic opportunities for a range of enhancer-related diseases.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Enhancer–promoter specificity in gene transcription: molecular mechanisms and disease associations

Friedman,

Wagner,

Lee

et al. 2024

Exp Mol Med

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“…The spatial landscape is vital for understanding how genetic machinery works (Bouwman et al, 2022) -and can be used in e.g. precision medicine (Chen et al, 2023), for example, by improving gene expression prediction (Chiliński, Lipiński, et al, 2023). However, those spatial experiments are expensive and time-consuming to run.…”

Section: Introductionmentioning

confidence: 99%

HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences

Chiliński,

Plewczynski

2024

Preprint

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Prediction of chromatin interactions from DNA sequence has been a significant research challenge in the last couple of years. Several solutions have been proposed, most of which are based on encoder-decoder architecture, where 1D sequence is convoluted, encoded into the latent representation, and then decoded using 2D convolutions into the Hi-C pairwise chromatin spatial proximity matrix. Those methods, while obtaining high correlation scores and improved metrics, produce Hi-C matrices that are artificial - they are blurred due to the deep learning model architecture. In our study, we propose the HiCDiffusion model that addresses this problem. We first train the encoder-decoder neural network and then use it as a component of the diffusion model - where we guide the diffusion using a latent representation of the sequence, as well as the final output from the encoder-decoder. That way, we obtain the high-resolution Hi-C matrices that not only better resemble the experimental results - improving the Frechet inception distance by an average of 12 times, with the highest improvement of 35 times - but also obtain similar classic metrics to current state-of-the-art encoder-decoder architectures used for the task.

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The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Ozcelik,

Dundar,

Yildirim

et al. 2024

Funct Integr Genomics

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3D genomics and its applications in precision medicine

Cited by 3 publications

References 139 publications

Enhancer–promoter specificity in gene transcription: molecular mechanisms and disease associations

Enhancer–promoter specificity in gene transcription: molecular mechanisms and disease associations

HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

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