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Doctoral Thesis
DOI
https://doi.org/10.11606/T.5.2020.tde-28102020-181629
Document
Author
Full name
Levi Higino Jales Neto
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2020
Supervisor
Committee
Pereira, Rosa Maria Rodrigues (President)
Domiciano, Diogo Souza
Figueiredo, Camille Pinto
Szejnfeld, Vera Lucia
Title in Portuguese
Estudo genético por microarray em mulheres idosas com fratura vertebral por osteoporose
Keywords in Portuguese
Análise de sequência com séries de oligonucleotide
Biologia computacional
Expressão gênica
Fraturas da coluna vertebral
Idoso
Mulheres
Osteoporose
Abstract in Portuguese
Introdução: As fraturas vertebrais são as manifestações clínicas mais comuns da osteoporose e estão associadas a elevada morbimortalidade. Vários fatores, incluindo história pessoal ou familiar de fraturas, densidade mineral óssea (DMO) e idade aumentam o risco de fraturas. No entanto, o mecanismo molecular subjacente pelo qual esses fatores levam ao aumento do risco não é totalmente compreendido. Portanto, o objetivo deste estudo é identificar possíveis marcadores genéticos moleculares associados a fraturas vertebrais osteoporóticas. Método: A análise de microarray transcriptômico foi realizada em amostras de sangue periférico de mulheres idosas com fratura vertebral (VF) e sem fratura vertebral (NVF), todas com critérios densitométrico de osteoporose. Elas foram pareadas por idade, doenças crônica, uso de medicação crônicas, tabagismo e DMO. O chip de microarray Affymetrix HTA 2.0 foi usado para análise de RNA a partir de amostras de sangue total de pacientes, dos 2 grupos (24 VF vs. 12 NVF). Na análise estatística dos microarrays, aplicamos o modelo de regressão logística com a idade utilizada como covariável. O processamento de dados e análise de genes diferencialmente expressos foram realizados usando os pacotes affy e multtest da bioconductor. Finalmente, a lista de transcrição foi carregada no software Ingenuity Pathway Analysis para explorar e identificar as vias de enriquecimento e funções biológicas. A análise da bioinformática para avaliar a interação gênica foi realizada pelo software cytoscape. As alterações de expressão gênica foram validadas usando o método qPCR-RT (reação em cadeia da polimerase quantitativa em tempo real). Genes com p < 0,01 foram considerados estatisticamente significantes no estudo de microarrays e p < 0,05 na qPCR-RT. Resultados: Comparando os grupos VF e NVF, a análise dos dados de microarray foram identificados 142 genes diferencialmente expressos (DEG), 57 com a expressão aumentada e 85 com a expressão reduzida no grupo com fratura vertebral. Entre os top 10: O DEG com maior expressão foi o Gamma2-Syntrophin (SNTG2) (Beta = 31,88, p < 0,01), o TRAF3IP2 também mostrou uma expressão significativa (Beta = 12,18, p < 0,006). Na análise do enriquecimento da bioinformática foi encontrada as vias de sinalização do mTOR1 (PNO1, PDK1), Adesão focal (ITGA6, TNXB), interação do receptor da matrix extra-celular (ITGA6, GP5, TNXB), da linhagem de células hematopoiéticas (ITGA6, GP5) como vias significativas (p <= 0,05). Na análise da base da dados do Gene Ontology (GO), no processo biológico, a via mais significativa foi a regulação do processo metabólico do fósforo com 6 genes envolvidos (WNK1, ITGA6, PDK1, PLCL1, TNXB, ENPP2), no componente celular a via da matriz extracelular com 4 genes envolvidos (CD248, FBLN7, ITGA6, TNXB) e na função molecular foram encontrados 5 genes (SNTG2, ITGA6, PLCL1, TNXB, TRAF3IP2) envolvidos com a via do receptor ligante. Os principais genes achados pelo estudo da bioinformática foram analisados e validados por outro método (qPCR- RT), onde foi confirmado a expressão aumentada no grupo VF da Proteína 2 Interagindo TRAF3 (TRAF3IP2, fold change = 2,79, desvio padrão = 0,83, p = 0,02), Subunidade Alfa 6 de Integrina (ITGA6, fold change = 2,86, desvio padrão = 1,14, p = 0,04) e Sintrofina (SNTG2, fold change = 2,79, desvio padrão = 0,86, p = 0,01). Conclusão: Identificamos e validamos os genes transcritos SNTG2, TRAF3IP2 e ITGA6 na fratura vertebral osteoporótica em mulheres idosas, de forma independente da densidade mineral óssea. Estes genes podem ser utilizados como potenciais biomarcadores ou novos alvos terapêuticos de fratura vertebral osteoporótica
Title in English
Genetic study by microarray in elderly women with osteoporosis vertebral fracture
Keywords in English
Aged
Computational biology
Gene expression
Oligonucleotide array sequence analysis
Osteoporosis
Spinal fractures
Women
Abstract in English
Background: Vertebral fractures are the most common clinical manifestation of osteoporosis and are associated with high morbidity and mortality. Several factors, including personal or family history of fractures, bone mineral density (BMD) and age increase the risk of fractures. However, the underlying molecular mechanism by which these factors lead to increased risk is not fully understood. Therefore, the aim of this study is to identify possible molecular genetic markers associated with osteoporotic vertebral fractures. Subjects and Methods: An analysis of the transcriptomic microarray was performed on peripheral blood samples from elderly women with vertebral fracture (VF) and without vertebral fracture (NVF), all with densitometric osteoporosis classification. They were matched for age, chronic illness, chronic medication use, smoking and BMD. The Affymetrix HTA 2.0 microarray chip was used for RNA analysis from samples of whole blood from patients in the 2 groups (24 VF vs. 12 NVF). In the statistical analysis of microarrays, the logistic regression model with age used as a covariate is applied. Data processing and analysis of differentially expressed genes were performed using the bioconductor's affy and multtest packages. Finally, a transcript list was loaded into the Ingenuity Pathway Analysis software to explore and identify ways of enhancement and biological functions. The analysis of bioinformatics to assess gene interaction was performed using the cytoscape software. The changes in gene expression were validated using the qPCR-RT method (real-time quantitative polymerase chain reaction). Genes with p < 0.01 were considered statistically significant in the study of microarrays and p < 0.05 in qPCR-RT. Results: Comparing the VF and NVF groups, the analysis of the microarray data identified 142 differentially expressed genes (DEG), 57 with increased expression and 85 with reduced expression in the group with vertebral fracture. Among the top 10: The most expressive DEG was Gamma2-Syntrophin (SNTG2) (Beta = 31.88, p < 0.01), TRAF3IP2 also showed significant expression (Beta = 12.18, p < 0.006). In the analysis of bioinformatics enrichment, the signaling pathways of mTOR1 (PNO1, PDK1), focal adhesion (ITGA6, TNXB), interaction of the extra-cellular matrix receptor (ITGA6, GP5, TNXB), of the hematopoietic cell line (ITGA6, GP5) as significant pathways (p < 0.05). In the analysis of the Gene Ontology (GO) database, in the biological process, the most significant route was the regulation of the phosphorus metabolic process with 6 genes involved (WNK1, ITGA6, PDK1, PLCL1, TNXB, ENPP2), in the cellular component the extracellular matrix pathway with 4 genes involved (CD248, FBLN7, ITGA6, TNXB) and in molecular function, 5 genes (SNTG2, ITGA6, PLCL1, TNXB, TRAF3IP2) were found involved with the ligand receptor pathway. The main genes found by the bioinformatics study were analyzed and validated by another method (qPCR-RT), which confirmed the increased expression in the VF group of Protein 2 Interacting TRAF3 (TRAF3IP2, fold change = 2.79, standard deviation = 0.83, p = 0.02), Alpha 6 Subunit of Integrin (ITGA6, fold change = 2.86, standard deviation = 1.14, p = 0.04) and Syntrophin (SNTG2, fold change = 2.79, deviation standard = 0.86, p = 0.01). Conclusion: Our data identified and validated the association of SNTG2, TRAF3IP2 and ITGA6 with osteoporotic vertebral fractures in elderly women, independently of bone mineral density. These genes can be used as potential biomarkers or new therapeutic targets for osteoporotic vertebral fractures
 
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Publishing Date
2020-10-28
 
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