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Abstract <p>Hemophilia B is an X-linked bleeding disorder characterized by reduced expression or activity of coagulation Factor IX (FIX), commonly associated with mutations or regulatory alterations in the <i>F9</i> gene. Hepatocyte nuclear factor 4 alpha (HNF4α), a liver-enriched transcription factor, has been implicated in the regulation of several coagulation-related genes, including <i>F9</i>. To explore the potential of HNF4α overexpression and CRISPR-based epigenetic modulation as strategies to enhance <i>F9</i> expression in the context of hemophilia B. A multi-layered in silico framework was employed. Transcriptomic and epigenomic datasets from human liver tissues were analyzed to identify regulatory features within the <i>F9</i> promoter. CRISPR activation (CRISPRa) constructs targeting HNF4α binding motifs and hypermethylated promoter regions were computationally designed. Molecular docking, dynamic simulations, and gene circuit modeling were applied to simulate transcription factor binding, chromatin modification, and predicted transcriptional output. All analyses were performed in silico and represent simulated predictions rather than experimentally validated outcomes. Computational modeling predicted that HNF4α overexpression would be associated with increased modeled F9 transcription (approximately 3.2-fold, <i>P</i> &lt; 0.01). Simulated CRISPRa-based epigenetic editing approaches (dCas9-VP64, dCas9-TET1, and dCas9-p300) were predicted to produce additive activation effects in silico. Combined strategies were modeled to potentially enhance transcriptional output toward levels considered theoretically therapeutic. These findings support a computationally derived, hypothesis-generating concept that transcriptional upregulation of F9 through HNF4α modulation and targeted epigenetic editing may be plausible. However, the study is entirely computational, and these observations should be interpreted as predictive simulations requiring experimental and preclinical validation rather than definitive evidence of therapeutic efficacy.</p>

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Epigenetic Reactivation of F9 via HNF4α as a Therapeutic Strategy for Hemophilia B

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Y. Alyahyawi,
  • T. M. Belali

摘要

Abstract

Hemophilia B is an X-linked bleeding disorder characterized by reduced expression or activity of coagulation Factor IX (FIX), commonly associated with mutations or regulatory alterations in the F9 gene. Hepatocyte nuclear factor 4 alpha (HNF4α), a liver-enriched transcription factor, has been implicated in the regulation of several coagulation-related genes, including F9. To explore the potential of HNF4α overexpression and CRISPR-based epigenetic modulation as strategies to enhance F9 expression in the context of hemophilia B. A multi-layered in silico framework was employed. Transcriptomic and epigenomic datasets from human liver tissues were analyzed to identify regulatory features within the F9 promoter. CRISPR activation (CRISPRa) constructs targeting HNF4α binding motifs and hypermethylated promoter regions were computationally designed. Molecular docking, dynamic simulations, and gene circuit modeling were applied to simulate transcription factor binding, chromatin modification, and predicted transcriptional output. All analyses were performed in silico and represent simulated predictions rather than experimentally validated outcomes. Computational modeling predicted that HNF4α overexpression would be associated with increased modeled F9 transcription (approximately 3.2-fold, P < 0.01). Simulated CRISPRa-based epigenetic editing approaches (dCas9-VP64, dCas9-TET1, and dCas9-p300) were predicted to produce additive activation effects in silico. Combined strategies were modeled to potentially enhance transcriptional output toward levels considered theoretically therapeutic. These findings support a computationally derived, hypothesis-generating concept that transcriptional upregulation of F9 through HNF4α modulation and targeted epigenetic editing may be plausible. However, the study is entirely computational, and these observations should be interpreted as predictive simulations requiring experimental and preclinical validation rather than definitive evidence of therapeutic efficacy.