Uncovering the genetic and molecular features of huntington’s disease in northern Colombia

datacite.rightshttp://purl.org/coar/access_right/c_abf2
dc.contributor.authorAhmad, Mostapha
dc.contributor.authorRíos Anillo, Margarita Rosa
dc.contributor.authorAcosta-Lopez, Johan E.
dc.contributor.authorCervantes-Henríquez, Martha L.
dc.contributor.authorMartinez-Banfi, Martha Luz
dc.contributor.authorPineda-Alhucema, Wilmar
dc.contributor.authorPuentes-Rozo, Pedro
dc.contributor.authorSánchez Barros, Cristian Manuel
dc.contributor.authorPinzón, Andrés
dc.contributor.authorPatel, Hardip
dc.contributor.authorVélez, Jorge
dc.contributor.authorVillarreal-Camacho, Jose Luis
dc.contributor.authorPineda, David A
dc.contributor.authorArcos-Burgos, Mauricio
dc.contributor.authorSánchez Rojas, Manuel
dc.date.accessioned2024-11-26T20:07:27Z
dc.date.available2024-11-26T20:07:27Z
dc.date.issued2024
dc.description.abstractHuntington’s disease (HD) is a genetic disorder caused by a CAG trinucleotide expansion in the huntingtin (HTT) gene. Juan de Acosta, Atlántico, a city located on the Caribbean coast of Colombia, is home to the world’s second-largest HD pedigree. Here, we include 291 descendants of this pedigree with at least one family member with HD. Blood samples were collected, and genomic DNA was extracted. We quantified the HTT CAG expansion using an amplicon sequencing protocol. The genetic heterogeneity was measured as the ratio of the mosaicism allele’s read peak and the slippage ratio of the allele’s read peak from our sequence data. The statistical and bioinformatic analyses were performed with a significance threshold of p < 0.05. We found that the average HTT CAG repeat length in all participants was 21.91 (SD = 8.92). Of the 291 participants, 33 (11.3%, 18 females) had a positive molecular diagnosis for HD. Most affected individuals were adults, and the most common primary and secondary alleles were 17/7 (CAG/CCG) and 17/10 (CAG/CCG), respectively. The mosaicism increased with age in the participants with HD, while the slippage analyses revealed differences by the HD allele type only for the secondary allele. The slippage tended to increase with the HTT CAG repeat length in the participants with HD, but the increase was not statistically significant. This study analyzed the genetic and molecular features of 291 participants, including 33 with HD. We found that the mosaicism increased with age in the participants with HD, particularly for the secondary allele. The most common haplotype was 17/7_17/10. The slippage for the secondary allele varied by the HD allele type, but there was no significant difference in the slippage by sex. Our findings offer valuable insights into HD and could have implications for future research and clinical management.eng
dc.format.mimetypepdf
dc.identifier.citationAhmad, M.; Ríos-Anillo, M.R.; Acosta-López, J.E.; Cervantes-Henríquez, M.L.; Martínez-Banfi, M.; Pineda-Alhucema,W.; Puentes-Rozo, P.; Sánchez-Barros, C.; Pinzón, A.; Patel, H.R.; et al. Uncovering the Genetic and Molecular Features of Huntington’s Disease in Northern Colombia. Int. J. Mol. Sci. 2023, 24, 16154. https://doi.org/10.3390/ijms242216154
dc.identifier.doihttps://doi.org/10.3390/ijms242216154
dc.identifier.issn14220067 (Electrónico)
dc.identifier.urihttps://hdl.handle.net/20.500.12442/15984
dc.identifier.urlhttps://www.mdpi.com/1422-0067/24/22/16154
dc.language.isoeng
dc.publisherMDPIspa
dc.publisherFacultad de Ciencias de la Saludspa
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Stateseng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subject.keywordsHuntington’s diseaseeng
dc.subject.keywordsHTTeng
dc.subject.keywordsCAG repeatseng
dc.subject.keywordsMosaicismeng
dc.subject.keywordsSlippageeng
dc.titleUncovering the genetic and molecular features of huntington’s disease in northern Colombiaeng
dc.type.driverinfo:eu-repo/semantics/other
dc.type.spaOtros
dcterms.referencesMacDonald, M.E.; Ambrose, C.M.; Duyao, M.P.; Myers, R.H.; Lin, C.; Srinidhi, L.; Barnes, G.; Taylor, S.A.; James, M.; Groot, N.; et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 1993, 72, 971–983. [CrossRef]eng
dcterms.referencesMarchina, E.; Misasi, S.; Bozzato, A.; Ferraboli, S.; Agosti, C.; Rozzini, L.; Borsani, G.; Barlati, S.; Padovani, A. Gene expression profile in fibroblasts of Huntington’s disease patients and controls. J. Neurol. Sci. 2014, 337, 42–46. [CrossRef]eng
dcterms.referencesPotter, N.T.; Spector, E.B.; Prior, T.W. Technical Standards and Guidelines for Huntington Disease Testing. Genet. Med. 2004, 6, 61–65. [CrossRef]eng
dcterms.referencesGusella, J.F.; MacDonald, M.E. Huntington’s disease: Seeing the pathogenic process through a genetic lens. Trends Biochem. Sci. 2006, 31, 533–540. [CrossRef]eng
dcterms.referencesBurton, A. Hope, humanity, and Huntington’s disease in Latin America. Lancet Neurol. 2013, 12, 133–134. [CrossRef]eng
dcterms.referencesDe Castro, M.; Restrepo, C.M. Genetics and genomic medicine in colombia. Mol. Genet. Genomic Med. 2015, 3, 84–91. [CrossRef]eng
dcterms.referencesDaza, B.; Caiaffa, R.H.; Arteta, B.J.; Echeverría, R.V.; Ladrón de Guevara, Z.; Escamilla, M. Estudio neuroepidemiológico en Juande Acosta, Atlántico, Colombia. Acta Méd. Colomb. 1991, 17, 324.spa
dcterms.referencesSánchez-Castañeda, C.; Squitieri, F.; Di Paola, M.; Dayan, M.; Petrollini, M.; Sabatini, U. The role of iron in gray matter degeneration in huntington’s disease: A magnetic resonance imaging study. Hum. Brain Mapp. 2015, 36, 50–66. [CrossRef]eng
dcterms.referencesMaxwell, A. ScaleHD Documentation. 2022. Available online: https://scalehd.readthedocs.io/_/downloads/en/latest/pdf/ (accessed on 20 March 2022).eng
dcterms.referencesPulkes, T.; Papsing, C.; Wattanapokayakit, S.; Mahasirimongkol, S. Cag-expansion haplotype analysis in a population with a low prevalence of huntington’s disease. J. Clin. Neurol. 2014, 10, 32–36. [CrossRef]eng
dcterms.referencesWarby, S.C.; Visscher, H.; Collins, J.A.; Doty, C.N.; Carter, C.; Butland, S.L.; Hayden, A.R.; Kanazawa, I.; Ross, C.J.; Hayden, M.R. HTT haplotypes contribute to differences in Huntington disease prevalence between Europe and East Asia. Eur. J. Hum. Genet. 2011, 19, 561–566. [CrossRef]eng
dcterms.referencesStephens, M.; Smith, N.J.; Donnelly, P. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet. 2001, 68, 978–989. [CrossRef] [PubMed]eng
dcterms.referencesKremer, B.; Goldberg, P.; Andrew, S.E.; Theilmann, J.; Telenius, H.; Zeisler, J.; Squitieri, F.; Lin, B.; Bassett, A.; Almqvist, E.; et al. A worldwide study of the Huntington’s disease mutation. The sensitivity and specificity of measuring CAG repeats. N. Engl. J. Med. 1994, 330, 1401–1406. [CrossRef] [PubMed]eng
dcterms.referencesPringsheim, T.; Wiltshire, K.; Day, L.; Dykeman, J.; Steeves, T.; Jette, N. The incidence and prevalence of Huntington’s disease: A systematic review and meta-analysis. Mov. Disord. 2012, 27, 1083–1091. [CrossRef]eng
dcterms.referencesHayden, M.R.; Berkowicz, A.L.; Beighton, P.H.; Yiptong, C. Huntington’s chorea on the island of Mauritius. S. Afr. Med. J. 1981, 60, 1001–1002. [PubMed]eng
dcterms.referencesApolinário, T.A.; Silva, I.d.S.d.; Agostinho, L.d.A.; Paiva, C.L.A. Investigation of intermediate CAG alleles of the HTT in the general population of Rio de Janeiro, Brazil, in comparison with a sample of Huntington disease-affected families. Mol. Genet. Genomic Med. 2020, 8, e1181. [CrossRef] [PubMed]eng
dcterms.referencesAgostinho, L.D.A.; Rocha, C.F.; Medina-Acosta, E.; Barboza, H.N.; da Silva, A.F.A.; Pereira, S.P.; da Silva, I.D.S.; Paradela, E.R.; Figueiredo, A.L.D.S.; Nogueira, E.D.M.; et al. Haplotype analysis of the CAG and CCG repeats in 21 Brazilian families with Huntington’s disease. J. Hum. Genet. 2012, 57, 796–803. [CrossRef]eng
dcterms.referencesMasuda, N.; Goto, J.; Murayama, N.; Watanabe, M.; Kondo, I. Kanazawa Analysis of triplet repeats in the huntingtin gene in Japanese families affected with Huntington’s disease. J. Med. Genet. 1995, 32, 701–705. [CrossRef]eng
dcterms.referencesRuiz de Sabando, A.; Urrutia Lafuente, E.; Galbete, A.; Ciosi, M.; García Amigot, F.; García Solaesa, V.; Spanish HD Collaborative Group; Monckton, D.G.; Ramos-Arroyo, M.A. Spanish HTT gene study reveals haplotype and allelic diversity with possible implications for germline expansion dynamics in Huntington disease. Hum. Mol. Genet. 2022, 32, 897–906. [CrossRef]eng
dcterms.referencesWalker, R.H.; Gatto, E.M.; Bustamante, M.L.; Bernal-Pacheco, O.; Cardoso, F.; Castilhos, R.M.; Chana-Cuevas, P.; Cornejo-Olivas, M.; Estrada-Bellmann, I.; Jardim, L.B.; et al. Huntington’s disease-like disorders in Latin America and the Caribbean. Park. Relat. Disord. 2018, 53, 10–20. [CrossRef]eng
dcterms.referencesCampbell, I.M.; Shaw, C.A.; Stankiewicz, P.; Lupski, J.R. Somatic mosaicism: Implications for disease and transmission genetics. Trends Genet. 2015, 31, 382–392. [CrossRef]eng
dcterms.referencesClever, F.; Cho, I.K.; Yang, J.; Chan, A.W.S. Progressive Polyglutamine Repeat Expansion in Peripheral Blood Cells and Sperm of Transgenic Huntington’s Disease Monkeys. J. Huntingt. Dis. 2019, 8, 443–448. [CrossRef] [PubMed]eng
dcterms.referencesSemaka, A.; Kay, C.; Doty, C.N.; Collins, J.A.; Tam, N.; Hayden, M.R. High frequency of intermediate alleles on Huntington disease-associated haplotypes in British Columbia’s general population. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2013, 162B, 864–871. [CrossRef] [PubMed]eng
dcterms.referencesPalareti, G.; Legnani, C.; Cosmi, B.; Antonucci, E.; Erba, N.; Poli, D.; Testa, S.; Tosetto, A.; DULCIS (D-dimer-ULtrasonography in Combination Italian Study) Investigators; De Micheli, V.; et al. Comparison between different D-Dimer cutoff values to assess the individual risk of recurrent venous thromboembolism: Analysis of results obtained in the DULCIS study. Int. J. Lab. Hematol. 2016, 38, 42–49. [CrossRef] [PubMed]eng
dcterms.referencesKacher, R.; Lejeune, F.X.; Noel, S.; Cazeneuve, C.; Brice, A.; Humbert, S.; Durr, A. Propensity for somatic expansion increases over the course of life in huntington disease. Elife 2021, 10, e64674. [CrossRef] [PubMed]eng
dcterms.referencesViguera, E.; Canceill, D.; Ehrlich, S.D. Replication slippage involves DNA polymerase pausing and dissociation. EMBO J. 2001, 20, 2587–2595. [CrossRef] [PubMed]eng
dcterms.referencesSathe, S.;Ware, J.; Levey, J.; Neacy, E.; Blumenstein, R.; Noble, S.; Mühlbäck, A.; Rosser, A.; Landwehrmeyer, G.B.; Sampaio, C. Enroll-HD: An Integrated Clinical Research Platform and Worldwide Observational Study for Huntington’s Disease. Front. Neurol. 2021, 12, 667420. [CrossRef]eng
dcterms.referencesEspinoza, F.A.; Turner, J.A.; Vergara, V.M.; Miller, R.L.; Mennigen, E.; Liu, J.; Misiura, M.B.; Ciarochi, J.; Johnson, H.J.; Long, J.D.; et al. Whole-Brain Connectivity in a Large Study of Huntington’s Disease Gene Mutation Carriers and Healthy Controls. Brain Connect. 2018, 8, 166–178. [CrossRef]eng
dcterms.referencesVélez, J.I. Machine Learning based Psychology: Advocating for A Data-Driven Approach. Int. J. Psychol. Res. 2021, 14, 6–11. [CrossRef]eng
dcterms.referencesMohan, A.; Sun, Z.; Ghosh, S.; Li, Y.; Sathe, S.; Hu, J.; Sampaio, C. A Machine-Learning Derived Huntington’s Disease Progression Model: Insights for Clinical Trial Design. Mov. Disord. 2022, 37, 553–562. [CrossRef]eng
dcterms.referencesRiad, R.; Lunven, M.; Titeux, H.; Cao, X.N.; Hamet Bagnou, J.; Lemoine, L.; Montillot, J.; Sliwinski, A.; Youssov, K.; Cleret de Langavant, L.; et al. Predicting clinical scores in Huntington’s disease: A lightweight speech test. J. Neurol. 2022, 269, 5008–5021. [CrossRef]eng
dcterms.referencesOdish, O.F.F.; Johnsen, K.; van Someren, P.; Roos, R.A.C.; van Dijk, J.G. EEG may serve as a biomarker in Huntington’s disease using machine learning automatic classification. Sci. Rep. 2018, 8, 16090. [CrossRef] [PubMed]eng
dcterms.referencesBradley, M.; Pinto, A.J.; Guest, J.S. Gene-Specific Primers for Improved Characterization of Mixed Phototrophic Communities. Appl. Environ. Microbiol. 2016, 82, 5878–5891. [CrossRef] [PubMed]eng
dcterms.referencesCiosi, M.; Cumming, S.A.; Alshammari, A.M.; Symeonidi, E.; Herzyk, P.; McGuinness, D.; Galbraith, J.; Hamilton, G.; Monckton, D.G. Library Preparation and MiSeq Sequencing for the Genotyping-by-Sequencing of the Huntington Disease HTT Exon One Trinucleotide Repeat and the Quantification of Somatic Mosaicism; Springer: Berlin/Heidelberg, Germany, 2018. [CrossRef]eng
dcterms.referencesFadrosh, D.W.; Ma, B.; Gajer, P.; Sengamalay, N.; Ott, S.; Brotman, R.M.; Ravel, J.; Fadrosh, D.W.; Ma, B.; Gajer, P.; et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014, 2, 6. [CrossRef] [PubMed]eng
dcterms.referencesIhaka, R.; Gentleman, R. R: A Language for Data Analysis and Graphics. J. Comput. Graph. Stat. 1996, 5, 299–314. [CrossRef]eng
oaire.versioninfo:eu-repo/semantics/publishedVersion
sb.programaEspecialización en Neurologíaspa
sb.sedeSede Barranquillaspa

Archivos

Bloque original
Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
PDF.pdf
Tamaño:
1.56 MB
Formato:
Adobe Portable Document Format
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
2.93 KB
Formato:
Item-specific license agreed upon to submission
Descripción: