Biology subjects

Messon-Bird, J. L.

Publications and source records attributed to Messon-Bird, J. L..

3 recordsLinked to original sources

bioRxiv2025

Global Evaluation of Congenital Heart Disease-Associated Non-Coding Variants

Abstract (Summary)Genome-wide association studies (GWAS) have mapped thousands of congenital heart disease (CHD)-associated variants within non-coding regions of the genome. Non-coding variants can alter regulatory mechanisms, such as transcription factor (TF) binding control of gene expression, potentially contributing human diseases. However, with the increasing number of disease-associated variants, comprehensive functional validation remains a significant challenge. In this work, we developed a novel method called SNP Bind-n-Seq to evaluate >3,000 CHD-risk variants for allelic binding for the cardiac TFs NKX2-5, GATA4, and TBX5 in a high-throughput manner. These binding affinity data sets were coupled with a massively parallel reporter assay (MPRA) to screen CHD-risk variant genotype-dependent regulatory activity. We identified 170 variants that exhibit allelic TF binding and 187 that modulate gene expression. Combining both approaches revealed three high-confidence variants with genotype-dependent TF binding, genotype-dependent transcriptional activity, and eQTL behavior in cardiac cells. Collectively, this study provides the first combined high-throughput biochemical and functional genomic evaluation of thousands of CHD-risk variants. HighlightsO_LIAllelic binding affinity measurements of [~]9,600 variants for NKX2-5, GATA4, and TBX5 C_LIO_LIEvaluaFon of >3,000 CHD-risk variants for genotype-dependent regulatory acFvity C_LIO_LIInteracFon networks idenFfy funcFonal variants and genes involving cardiac eQTLs C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/691900v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1f2dc96org.highwire.dtl.DTLVardef@1700929org.highwire.dtl.DTLVardef@696ec4org.highwire.dtl.DTLVardef@1e724d8_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Congenital heart disease missense mutations in the TBX5 DNA-binding domain alter thermal stability and DNA-binding affinity

Missense mutations can alter the biochemical properties of proteins, including stability, structure, and function, potentially contributing to the development of multiple human diseases. Mutations in TBX5, a transcription factor (TF) necessary for heart development, are among the causes of congenital heart diseases (CHD). However, further research on biophysical and biochemical mechanisms is needed to understand how missense mutations in TFs alter their function in regulating gene expression. In this work, we applied in vitro and in silico approaches to understand how five missense mutations in the TBX5 T-box DNA-binding domain (I54T, M74V, I101F, R113K, and R237W) impact protein structure, thermal stability, and DNA-binding affinity to known TBX5 cognate binding sites. Differential Scanning Fluorimetry showed that mutants I54T and M74V decreased thermal stability, whereas I101F and R113K had increased stability. Additionally, DNA-binding affinity decreased for all five missense mutants when evaluated in vitro for known TBX5 genomic binding sites within regulatory elements of Nppa and Camta1 genes. Structural modeling of the TBX5 T-box domain predicted altered protein conformation and stability due to the loss or gain of amino acid residue interactions. Together, our findings provide biophysical and biochemical mechanisms that can be further explored to establish causality between TBX5 missense mutations and the development of CHDs. Article SummaryMutations in genes crucial for heart development, such as TBX5, a cardiac transcription factor (TF), have been shown to cause congenital heart diseases. However, the functional consequences of missense mutations in cardiac TF have not been fully explored. In this work, we performed biochemical evaluations and found significant changes in thermal stability and DNA-binding of five TBX5 missense mutants (I54T, M74V, I101F, R113K, and R237W). Additionally, we generated in silico predictions of protein structure, stability, and pathogenicity for the five missense mutants. Our approach is scalable to other TF mutants crucial for organ development to further our understanding of congenital disease etiology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/654568v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@188cb2corg.highwire.dtl.DTLVardef@1d2e08corg.highwire.dtl.DTLVardef@19424fcorg.highwire.dtl.DTLVardef@1083f0d_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Cardiovascular Disease-Associated Non-Coding Variants Disrupt GATA4-DNA Binding and Regulatory Functions

Genome-wide association studies have mapped over 90% of cardiovascular disease (CVD)-associated variants within the non-coding genome. Non-coding variants in regulatory regions of the genome, such as promoters, enhancers, silencers, and insulators, can alter the function of tissue-specific transcription factors (TFs) proteins and their gene regulatory function. In this work, we used a computational approach to identify and test CVD-associated single nucleotide polymorphisms (SNPs) that alter the DNA binding of the human cardiac transcription factor GATA4. Using a gapped k-mer support vector machine (GKM-SVM) model, we scored CVD-associated SNPs localized in gene regulatory elements in expression quantitative trait loci (eQTL) detected in cardiac tissue to identify variants altering GATA4-DNA binding. We prioritized four variants that resulted in a total loss of GATA4 binding (rs1506537 and rs56992000) or the creation of new GATA4 binding sites (rs2941506 and rs2301249). The identified variants also resulted in significant changes in transcriptional activity proportional to the altered DNA-binding affinities. In summary, we present a comprehensive analysis comprised of in silico, in vitro, and cellular evaluation of CVD-associated SNPs predicted to alter GATA4 function. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/613959v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@127cab7org.highwire.dtl.DTLVardef@16df078org.highwire.dtl.DTLVardef@c65a17org.highwire.dtl.DTLVardef@44be40_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIAn integrative computational approach combining functional genomics data and machine learning was implemented to prioritize potential causal genetic variants associated with cardiovascular disease (CVD). C_LIO_LIWe prioritized and validated CVD-associated SNPs that created or destroyed genomic binding sites of the cardiac transcription factor GATA4. C_LIO_LIChanges in GATA4-DNA binding resulted in significant changes in GATA4-dependent transcriptional activity in human cells. C_LIO_LIOur results contribute to the mechanistic understanding of cardiovascular disease-associated non-coding variants impacting GATA4 function. C_LI

genomics↗