Journal of Periodontal and Implant Science

  • 제51권1호
  • /
  • Pages.18-29
  • /
  • 2021
  • /
  • 2093-2278(pISSN)
  • /
  • 2093-2286(eISSN)
대한치주과학회 (Korean Academy of Periodontology)
권호 표지
DOI QR코드

DOI QR Code

Reliability of microarray analysis for studying periodontitis: low consistency in 2 periodontitis cohort data sets from different platforms and an integrative meta-analysis

  • Jeon, Yoon-Seon (Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry) ;
  • Shivakumar, Manu (Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania) ;
  • Kim, Dokyoon (Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania) ;
  • Kim, Chang-Sung (Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry) ;
  • Lee, Jung-Seok (Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry)
  • 투고 : 2020.03.30
  • 심사 : 2020.09.24
  • 발행 : 2021.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets. Methods: Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed. Results: This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2. Conclusions: The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying periodontitis.

키워드

과제정보

This study was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (grant No. NRF-2019R1A2C4069942).

참고문헌

  1. Gresham D, Dunham MJ, Botstein D. Comparing whole genomes using DNA microarrays. Nat Rev Genet 2008;9:291-302. https://doi.org/10.1038/nrg2335
  2. Meyle J, Chapple I. Molecular aspects of the pathogenesis of periodontitis. Periodontol 2000 2015;69:7-17. https://doi.org/10.1111/prd.12104
  3. Jonsson D, Ramberg P, Demmer RT, Kebschull M, Dahlen G, Papapanou PN. Gingival tissue transcriptomes in experimental gingivitis. J Clin Periodontol 2011;38:599-611. https://doi.org/10.1111/j.1600-051X.2011.01719.x
  4. Taiete T, Casarin RCV, Ruiz KGS, Nociti FH Jr, Sallum EA, Casati MZ. Transcriptome of healthy gingival tissue from edentulous sites in patients with a history of generalized aggressive periodontitis. J Periodontol 2018;89:93-104.
  5. Offenbacher S, Barros SP, Paquette DW, Winston JL, Biesbrock AR, Thomason RG, et al. Gingival transcriptome patterns during induction and resolution of experimental gingivitis in humans. J Periodontol 2009;80:1963-82. https://doi.org/10.1902/jop.2009.080645
  6. Davanian H, Stranneheim H, Bage T, Lagervall M, Jansson L, Lundeberg J, et al. Gene expression profiles in paired gingival biopsies from periodontitis-affected and healthy tissues revealed by massively parallel sequencing. PLoS One 2012;7:e46440. https://doi.org/10.1371/journal.pone.0046440
  7. Guzeldemir-Akcakanat E, Sunnetci-Akkoyunlu D, Orucguney B, Cine N, Kan B, Yilmaz EB, et al. Gene-expression profiles in generalized aggressive periodontitis: a gene network-based microarray analysis. J Periodontol 2016;87:58-65. https://doi.org/10.1902/jop.2015.150175
  8. Kim YG, Kim M, Kang JH, Kim HJ, Park JW, Lee JM, et al. Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns. Hum Genomics 2016;10:28. https://doi.org/10.1186/s40246-016-0084-0
  9. Sawle AD, Kebschull M, Demmer RT, Papapanou PN. Identification of master regulator genes in human periodontitis. J Dent Res 2016;95:1010-7. https://doi.org/10.1177/0022034516653588
  10. Kebschull M, Demmer RT, Grun B, Guarnieri P, Pavlidis P, Papapanou PN. Gingival tissue transcriptomes identify distinct periodontitis phenotypes. J Dent Res 2014;93:459-68. https://doi.org/10.1177/0022034514527288
  11. Lundmark A, Gerasimcik N, Bage T, Jemt A, Mollbrink A, Salmen F, et al. Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics. Sci Rep 2018;8:9370. https://doi.org/10.1038/s41598-018-27627-3
  12. Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002;18:405-12. https://doi.org/10.1093/bioinformatics/18.3.405
  13. Russ J, Futschik ME. Comparison and consolidation of microarray data sets of human tissue expression. BMC Genomics 2010;11:305. https://doi.org/10.1186/1471-2164-11-305
  14. Turnbull AK, Kitchen RR, Larionov AA, Renshaw L, Dixon JM, Sims AH. Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis. BMC Med Genomics 2012;5:35. https://doi.org/10.1186/1755-8794-5-35
  15. Pavlou MP, Dimitromanolakis A, Martinez-Morillo E, Smid M, Foekens JA, Diamandis EP. Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer. J Proteome Res 2014;13:2897-909. https://doi.org/10.1021/pr500352e
  16. Irigoyen A, Jimenez-Luna C, Benavides M, Caba O, Gallego J, Ortuno FM, et al. Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers. PLoS One 2018;13:e0194844. https://doi.org/10.1371/journal.pone.0194844
  17. Waldron L, Haibe-Kains B, Culhane AC, Riester M, Ding J, Wang XV, et al. Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Cancer Inst 2014;106:dju049.
  18. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics 2008;24:1547-8. https://doi.org/10.1093/bioinformatics/btn224
  19. Benito M, Parker J, Du Q, Wu J, Xiang D, Perou CM, et al. Adjustment of systematic microarray data biases. Bioinformatics 2004;20:105-14. https://doi.org/10.1093/bioinformatics/btg385
  20. Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform 2013;14:469-90. https://doi.org/10.1093/bib/bbs037
  21. Demmer RT, Behle JH, Wolf DL, Handfield M, Kebschull M, Celenti R, et al. Transcriptomes in healthy and diseased gingival tissues. J Periodontol 2008;79:2112-24. https://doi.org/10.1902/jop.2008.080139
  22. Becker ST, Beck-Broichsitter BE, Graetz C, Dorfer CE, Wiltfang J, Hasler R. Peri-implantitis versus periodontitis: functional differences indicated by transcriptome profiling. Clin Implant Dent Relat Res 2014;16:401-11. https://doi.org/10.1111/cid.12001
  23. Suzuki A, Horie T, Numabe Y. Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in gene expression omnibus (GEO). BMC Oral Health 2019;19:52. https://doi.org/10.1186/s12903-019-0738-0
  24. Papapanou PN, Sanz M, Buduneli N, Dietrich T, Feres M, Fine DH, et al. Periodontitis: consensus report of workgroup 2 of the 2017 World Workshop on the classification of periodontal and peri-implant diseases and conditions. J Periodontol 2018;89 Suppl 1:S173-82. https://doi.org/10.1002/JPER.17-0721
  25. Bartold PM, Van Dyke TE. An appraisal of the role of specific bacteria in the initial pathogenesis of periodontitis. J Clin Periodontol 2019;46:6-11. https://doi.org/10.1111/jcpe.13046
  26. Kebschull M, Guarnieri P, Demmer RT, Boulesteix AL, Pavlidis P, Papapanou PN. Molecular differences between chronic and aggressive periodontitis. J Dent Res 2013;92:1081-8. https://doi.org/10.1177/0022034513506011
  27. Fine DH, Patil AG, Loos BG. Classification and diagnosis of aggressive periodontitis. J Clin Periodontol 2018;45 Suppl 20:S95-111. https://doi.org/10.1111/jcpe.12942