Genomics, epigenomics and pharmacogenomics of familial hypercholesterolemia (FHBGEP): A study protocol

Borges, Jéssica Bassani; Oliveira, Victor Fernandes de; Ferreira, Gláucio Monteiro; Los, Bruna; Barbosa, Thais Kristini Almendros Afonso; Marçal, Elisangela da Silva Rodrigues; Dagli-Hernandez, Carolina; Freitas, Renata Caroline Costa de; Bortolin, Raul Hernandes; Mori, Augusto Akira; Hirata, Thiago Dominguez Crespo; Nakaya, Helder Takashi Imoto; Bastos, Gisele Medeiros; Thurow, Helena Strelow; Gonçalves, Rodrigo Marques; Araújo, Daniel Branco de; Zatz, Henry Paulo; Bertolami, Adriana; Faludi, André Árpád; Bertolami, Marcelo Chiara; Sousa, Amanda Guerra de Moraes Rego; França, João Ítalo Dias; Jannes, Cinthia E.; Pereira, Alexandre da Costa; Nakazone, Marcelo Arruda; Souza, Dorotéia Rossi Silva; Carmo, Tayanne Silva; Sampaio, Marcelo Ferraz; Gorjão, Renata; Pithon-Curi, Tânia Cristina; Moriel, Patrícia; Silbiger, Vivian Nogueira; Luchessi, André Ducati; Araújo, Jéssica Nayara Góes de; Naslavsky, Michel Satya; Wang, Jaqueline Yu Ting; Kronenberger, Thales; Cerda, Álvaro; Lin-Wang, Hui Tzu; Garófalo, Adriana Regina; Fajardo, Cristina Moreno; Hirata, Rosário Dominguez Crespo; Hirata, Mário Hiroyuki

doi:10.1016/j.sapharm.2020.10.007

Cited by 20 publications

(18 citation statements)

References 60 publications

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“…This study is a part of the FHBGEP project that aims to investigate genomic, epigenomic, and pharmacogenomic factors associated with FH in the Brazilian population [ 21 ]. Two hundred unrelated adult FH patients were recruited at three Brazilian Medical Centers from October 2014 to January 2020.…”

Section: Methodsmentioning

confidence: 99%

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Genetic Variant ABCC1 rs45511401 Is Associated with Increased Response to Statins in Patients with Familial Hypercholesterolemia

et al. 2022

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Statins are the first-line treatment for familial hypercholesterolemia (FH), but response is highly variable due to genetic and nongenetic factors. Here, we explored the association between response and genetic variability in 114 Brazilian adult FH patients. Specifically, a panel of 84 genes was analyzed by exon-targeted gene sequencing (ETGS), and the functional impact of variants in pharmacokinetic (PK) genes was assessed using an array of functionality prediction methods. Low-density lipoprotein cholesterol (LDL-c) response to statins (reduction ≥ 50%) and statin-related adverse event (SRAE) risk were assessed in carriers of deleterious variants in PK-related genes using multivariate linear regression analyses. Fifty-eight (50.8%) FH patients responded to statins, and 24 (21.0%) had SRAE. Results of the multivariate regression analysis revealed that ABCC1 rs45511401 significantly increased LDL-c reduction after statin treatment (p < 0.05). In silico analysis of the amino-acid change using molecular docking showed that ABCC1 rs45511401 possibly impairs statin efflux. Deleterious variants in PK genes were not associated with an increased risk of SRAE. In conclusion, the deleterious variant ABCC1 rs45511401 enhanced LDL-c response in Brazilian FH patients. As such, this variant might be a promising candidate for the individualization of statin therapy.

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Section: Methodsmentioning

confidence: 99%

“…Genetic analyses were performed as previously described [ 21 ]. Briefly, genomic DNA was extracted from whole blood samples using QIAamp ® DNA Blood Maxi Kit (QIAGEN, Hilden, Germany).…”

Section: Methodsmentioning

confidence: 99%

Genetic Variant ABCC1 rs45511401 Is Associated with Increased Response to Statins in Patients with Familial Hypercholesterolemia

et al. 2022

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“…It is noteworthy that the significant phenotypic diversity of lipids and coronary artery disorders depends on the nature of FH genetic defects. These defects are modulated, however, by various genetic and epigenetic factors and, thus, various pathological genotypes can differentially impact the circulating levels of LDL-C [ 7 , 8 ]. For instance, a nonsense variant in the LDLR (c.2043 C>A, p. cys681X) was predominantly combined with familial hypercholesterolemia in nearly 82% of Lebanese cases.…”

Section: Introductionmentioning

confidence: 99%

Pharmacogenomics Variability of Lipid-Lowering Therapies in Familial Hypercholesterolemia

Hindi

Alenbawi

Nemer

2021

JPM

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The exponential expansion of genomic data coupled with the lack of appropriate clinical categorization of the variants is posing a major challenge to conventional medications for many common and rare diseases. To narrow this gap and achieve the goals of personalized medicine, a collaborative effort should be made to characterize the genomic variants functionally and clinically with a massive global genomic sequencing of “healthy” subjects from several ethnicities. Familial-based clustered diseases with homogenous genetic backgrounds are amongst the most beneficial tools to help address this challenge. This review will discuss the diagnosis, management, and clinical monitoring of familial hypercholesterolemia patients from a wide angle to cover both the genetic mutations underlying the phenotype, and the pharmacogenomic traits unveiled by the conventional and novel therapeutic approaches. Achieving a drug-related interactive genomic map will potentially benefit populations at risk across the globe who suffer from dyslipidemia.

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“…More broadly, PyInteraph has been used to study protein dynamics and allostery (Lambrughi, De Gioia, et al ., 2016; Sora and Papaleo, 2019; Marino and Dell’Orco, 2016, 2019; Galochkina et al ., 2019; Faienza et al ., 2020; Abbas et al ., 2019; Bonì et al ., 2020; Borsatto et al ., 2019). PyInteraph is also included in planned protocols from scientific consortia to study the genetics and the molecular mechanisms at the base of different diseases (Borges et al ., 2020). Due to the large and increasing number of applications, it becomes thus of paramount importance to maintain and develop further the package.…”

Section: Introductionmentioning

confidence: 99%

PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles

Sora

Tiberti

Robbani

et al. 2020

Preprint

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MotivationProtein dynamic is essential for cellular functions. Due to the complex nature of non-covalent interactions and their long-range effects, the analysis of protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) rely on different philosophies, and the currently available tools suffer from limitations in terms of input formats, supported network models, and version control. Another issue is the precise definition of cutoffs for the network calculations and the assessment of the stability of the parameters, which ultimately affect the outcome of the analyses.ResultsWe provide two open-source software packages, i.e., PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a harmonized, reproducible, and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and calculates a diverse range of network models with the possibility to integrate them into a macro-network and perform further downstream graph analyses. PyInKnife2 is a standalone package that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. Several functionalities are based on MDAnalysis and NetworkX, including parallelization, and are available for Python 3.7. PyInteraph2 underwent a massive restructuring in terms of setup, installation, and test support compared to the original PyInteraph software.ConclusionsWe foresee that the modular structure of the code and the version control system of GitHub will promote the transition to a community-driven effort, boost reproducibility, and establish harmonized protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and maintenance of the package.AvailabilityThe packages are available at https://github.com/ELELAB/pyinteraph2 and https://github.com/ELELAB/PyInKnife2 with guides provided within the packages.

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Genomics, epigenomics and pharmacogenomics of familial hypercholesterolemia (FHBGEP): A study protocol

Cited by 20 publications

References 60 publications

Genetic Variant ABCC1 rs45511401 Is Associated with Increased Response to Statins in Patients with Familial Hypercholesterolemia

Genetic Variant ABCC1 rs45511401 Is Associated with Increased Response to Statins in Patients with Familial Hypercholesterolemia

Pharmacogenomics Variability of Lipid-Lowering Therapies in Familial Hypercholesterolemia

PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles

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