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Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate Chronic Obstructive Pulmonary Disease.
Zhang, Yu-Hang; Cho, Michael H; Morrow, Jarrett D; Castaldi, Peter J; Hersh, Craig P; Midha, Mukul K; Hoopmann, Michael R; Lutz, Sharon M; Moritz, Robert L; Silverman, Edwin K.
Afiliación
  • Zhang YH; Channing Division of Network Medicine, Harvard Medical School, and.
  • Cho MH; Channing Division of Network Medicine, Harvard Medical School, and.
  • Morrow JD; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts; and.
  • Castaldi PJ; Channing Division of Network Medicine, Harvard Medical School, and.
  • Hersh CP; Channing Division of Network Medicine, Harvard Medical School, and.
  • Midha MK; Channing Division of Network Medicine, Harvard Medical School, and.
  • Hoopmann MR; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts; and.
  • Lutz SM; Institute for Systems Biology, Seattle, Washington.
  • Moritz RL; Institute for Systems Biology, Seattle, Washington.
  • Silverman EK; Channing Division of Network Medicine, Harvard Medical School, and.
Am J Respir Cell Mol Biol ; 68(6): 651-663, 2023 06.
Article en En | MEDLINE | ID: mdl-36780661
ABSTRACT
The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis-quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad Pulmonar Obstructiva Crónica / Proteómica Límite: Humans Idioma: En Revista: Am J Respir Cell Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad Pulmonar Obstructiva Crónica / Proteómica Límite: Humans Idioma: En Revista: Am J Respir Cell Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article