Functional analysis of LDLR promoter and 5′ UTR mutations in subjects with clinical diagnosis of familial hypercholesterolemia

Castro-Orós, Isabel De; Pampín, Sandra; Bolado-Carrancio, Alfonso; Cubas, Aguirre De; Palacios, Lourdes; Plana, Núria; Puzo, José; Martorell, Esperanza; Stef, Marianne; Civeira, Fernando; Rodríguez‐Rey, José C.; Pocovı́, Miguel

doi:10.1002/humu.21520

Cited by 31 publications

(27 citation statements)

References 22 publications

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“…While the prevalence of LDLR cryptic mutations is not clear, a recent study of LDLR promoter variants in a large cohort of European (Spanish) patients with ADH found their prevalence to be less than 1% 45 . Future studies with next generation sequencing techniques, such as whole-genome sequencing, may help elucidate the contribution of cryptic LDLR mutations in ADH patients.…”

Section: Discussionmentioning

confidence: 99%

Low Prevalence of Mutations in Known Loci for Autosomal Dominant Hypercholesterolemia in a Multiethnic Patient Cohort

Ahmad

Adams‐Huet

Chen

et al. 2012

Circ Cardiovasc Genet

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Background Autosomal dominant hypercholesterolemia (ADH), characterized by elevated plasma levels of low density lipoprotein-cholesterol (LDL-C), is caused by variants in at least three different genes:LDL receptor (LDLR), apolipoprotein B-100 (APOB), and proprotein convertase subtilisin-like kexin type 9 (PCSK9). There is paucity of data about the molecular basis of ADH among ethnic groups other than those of European or Japanese descent. Here, we examined the molecular basis of ADH in a multi-ethnic patient cohort from lipid clinics in a large urban U.S. city. Methods and Results A total of 38 males and 53 females, age 22 to 76 years, met modified Simon-Broome criteria for ADH and were screened for mutations in the exons and consensus splice sites of LDLR, and in selected exons of APOB and PCSK9. Deletions and duplications of LDLR exons were detected with multiplex ligation-dependent probe amplification. Heterozygous variants in LDLR were identified in 30 patients and in APOB in one patient. The remaining 60 patients (65%) had “unexplained ADH.” A higher proportion of African Americans (77%) than either non-Hispanic whites (57%) or Hispanics (53%) had “unexplained ADH.” As compared to patients with LDLR variants, those with “unexplained ADH” had lower levels of LDL-C (292 ± 47vs 239 ± 42 mg/dL, respectively; p < 0.0001) and higher levels of HDL-cholesterol (45 ± 12vs 54 ± 13 mg/dL, respectively, p = 0.003). Conclusions Our findings suggest that additional loci may contribute to ADH, especially in understudied populations such as African Americans.

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Section: Discussionmentioning

confidence: 99%

Low Prevalence of Mutations in Known Loci for Autosomal Dominant Hypercholesterolemia in a Multiethnic Patient Cohort

Ahmad

Adams‐Huet

Chen

et al. 2012

Circ Cardiovasc Genet

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“…Figure 4a shows how several Human Gene Mutation Database (HGMD) 34 single-nucleotide variants (SNVs) disrupted an SP1 binding site in the LDL-R promoter, leading to familial hypercholesterolemia 35 . It can be seen in the wild-type mutation map that all HGMD SNVs falling in the SP1 binding site decrease the score.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…Using the results from the staturated mutagenesis, both DeepSEA [76] and DeepBind [77] demonstrate they have effectively exploited the effects of the SNVs on certain disorders, which have been verified in the previous experiments [108][109][110][111][112][113][114][115][116][117][118][119].…”

Section: Prediction Causal Snvsmentioning

confidence: 59%

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Yang²,

Zhang

2017

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Backgrounds: With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings. Results: We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence. Conclusion: Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

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Functional analysis of LDLR promoter and 5′ UTR mutations in subjects with clinical diagnosis of familial hypercholesterolemia

Cited by 31 publications

References 22 publications

Low Prevalence of Mutations in Known Loci for Autosomal Dominant Hypercholesterolemia in a Multiethnic Patient Cohort

Low Prevalence of Mutations in Known Loci for Autosomal Dominant Hypercholesterolemia in a Multiethnic Patient Cohort

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

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