Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity

Jin, Yutong; Jiang, Jiahui; Wang, Ruixuan; Qin, Zhaohui

doi:10.3389/fgene.2021.667866

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…JCI Insight 2024;9(11):e175278 https://doi.org/10.1172/jci.insight.175278 site may exist, but these might not cause a defect in HNF-1A binding and β cell function and thus will not be reported by diabetes diagnostics laboratories (50).…”

Section: Discussionmentioning

confidence: 99%

Molecular mechanism of HNF-1A–mediated HNF4A gene regulation and promoter-driven HNF4A-MODY diabetes

Kind,

Molnes,

Tjora

et al. 2024

JCI Insight

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Monogenic diabetes is a gateway to precision medicine through molecular mechanistic insight. Hepatocyte nuclear factor 1A (HNF-1A) and HNF-4A are transcription factors that engage in crossregulatory gene transcription networks to maintain glucose-stimulated insulin secretion in pancreatic β cells. Variants in the HNF1A and HNF4A genes are associated with maturity-onset diabetes of the young (MODY). Here, we explored 4 variants in the P2- HNF4A promoter region: 3 in the HNF-1A binding site and 1 close to the site, which were identified in 63 individuals from 21 families of different MODY disease registries across Europe. Our goal was to study the disease causality for these variants and to investigate diabetes mechanisms on the molecular level. We solved a crystal structure of HNF-1A bound to the P2- HNF4A promoter and established a set of techniques to probe HNF-1A binding and transcriptional activity toward different promoter variants. We used isothermal titration calorimetry, biolayer interferometry, x-ray crystallography, and transactivation assays, which revealed changes in HNF-1A binding or transcriptional activities for all 4 P2- HNF4A variants. Our results suggest distinct disease mechanisms of the promoter variants, which can be correlated with clinical phenotype, such as age of diagnosis of diabetes, and be important tools for clinical utility in precision medicine.

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Section: Discussionmentioning

confidence: 99%

Molecular mechanism of HNF-1A–mediated HNF4A gene regulation and promoter-driven HNF4A-MODY diabetes

Kind,

Molnes,

Tjora

et al. 2024

JCI Insight

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Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges

Chan

Choi

Rangan

et al. 2022

Methods in Molecular Biology

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Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data

Han,

Li,

Wang

et al. 2024

Briefings in Bioinformatics

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Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)–deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

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Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity

Cited by 4 publications

References 42 publications

Molecular mechanism of HNF-1A–mediated HNF4A gene regulation and promoter-driven HNF4A-MODY diabetes

Molecular mechanism of HNF-1A–mediated HNF4A gene regulation and promoter-driven HNF4A-MODY diabetes

Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data

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