Sacharczuk, M. | bioRxiv Science

bioRxiv2025

The HNF4A Q164X Mutation Impairs Transcriptional Activation in Vitro but Its Heterozygosity Suppresses Liver Tumorigenesis in Vivo

Hepatocyte nuclear factor 4 alpha (HNF4A) is a master regulator of hepatic differentiation and metabolism. Here, we identify and characterize a truncating Q164X mutation that impairs HNF4A transcriptional activity in vitro and causes embryonic lethality when homozygous. Functional assays revealed that the Q164X protein retains nuclear localization but exhibits severely reduced DNA binding and transcriptional activation. CRISPR-generated Q164X mice showed no viable homozygotes, confirming the essential role of HNF4A in early embryogenesis. Unexpectedly, heterozygous Q164X mutants displayed reduced liver tumorigenesis following diethylnitrosamine and high-fat diet treatment, despite downregulation of HNF4A target genes such as ApoB and Hnf1a. These results suggest that partial HNF4A deficiency may trigger compensatory metabolic networks that protect against carcinogenic stress. Collectively, our study establishes Q164X as a loss-of-function HNF4A mutation with paradoxical tumor-suppressive effects in vivo.

cancer biology↗

bioRxiv2022

Adera: A drug repurposing workflow for neuro-immunological investigations using neural networks.

Drug repurposing in the context of neuro-immunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and rather focuses on discovering new usage for known medications. Neuro-immunological diseases such as Alzheimers, Parkinson, multiple sclerosis and depression include various pathologies that resulted from the interaction between the central nervous system and the immune system. However, repurposing of medications is hindered by the vast amount of information that needs mining. To challenge the need for repurposing known medications for neuro-immunological diseases, we built a deep neural network named Adera to perform drug repurposing. The model uses two deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network we explored the usage of two different loss function, binary cross entropy and means square error (MSE). Furthermore, we investigated the effect of ten different network architecture with each loss function. Our results show that for the binary cross entropy loss function, the best architecture consists of a two layers of convolution neural network and it achieves a loss of less than 0.001. In the case of MSE loss function a shallow network using aRelu activation achieved an accuracy of over 98 % and loss of 0.001. Additionally, Adera was able to predict various drug repurposing targets in agreement with DRUG Repurposing Hub. These results establish the ability of Adera to repurpose with high accuracy drug candidates that can shorten the development of the drug cycle. The software could be downloaded from https://github.com/michel-phylo/ADERA1.

bioinformatics↗