Transcriptome of healthy gingival tissue from edentulous sites in patients with a history of generalized aggressive periodontitis

Taiete, Tiago; Casarin, Renato Corrêa Viana; Silvério, Karina Gonzáles; Nociti, Francisco Humberto; Sallum, Enílson Antônio; Casati, Márcio Zaffalon

doi:10.1902/jop.2017.170221

Cited by 11 publications

(15 citation statements)

References 49 publications

(78 reference statements)

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“…Sherlock identifies disease‐associated genes with the assumption that the expression level of a specific gene may have an influence on disease pathogenesis. To evaluate whether the risk genes identified by Sherlock are differentially expressed in periodontitis sites compared with unaffected site, we used publicly available expression data sets, including http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134 (Kebschull et al, ), http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10334 (Demmer et al, ), http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27993 (Schminke et al, ), and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79705 (Taiete et al, ) from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) database. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134 contains 310 gingival tissue samples (241 samples from affected site and 69 samples from unaffected site), obtained from 120 systemically healthy non‐smokers with periodontitis.…”

Section: Methodsmentioning

confidence: 99%

“…Expression data were normalized and summarized using the Robust Multi‐Array (RMA) analysis, and differences between cases and controls were tested using Wilcoxon rank‐sum test. Further details about sample selection, RNA isolation, quantification, quality control, and statistical analysis can be found in original papers (Demmer et al, ; Kebschull et al, ; Schminke et al, ; Taiete et al, ).…”

Section: Methodsmentioning

confidence: 99%

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Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Zheng

Meng

et al. 2020

J Clinic Periodontology

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Aim:To identify risk variants associated with gene expression in peripheral blood and to identify genes whose expression change may contribute to the susceptibility to periodontitis. Material and Methods:We systematically integrated the genetic associations from a recent large-scale periodontitis GWAS and blood expression quantitative trait loci (eQTL) data using Sherlock, a Bayesian statistical framework. We then validated the potential causal genes in independent gene expression data sets. Gene co-expression analysis was used to explore the functional relationship for the identified causal genes.Results: Sherlock analysis identified 10 genes (rs7403881 for MT1L, rs12459542 for SIGLEC5, rs12459542 for SIGLEC14, rs6680386 for S100A12, rs10489524 for TRIM33, rs11962642 for HIST1H3E, rs2814770 for AIM2, rs7593959 for FASTKD2, rs10416904 for PKN1, and rs10508204 for WDR37) whose expression may influence periodontitis. Among these genes, AIM2 was consistent significantly upregulated in periodontium of periodontitis patients across four data sets. The cis-eQTL (rs2814770, ~16 kb upstream of AIM2) showed significant association with AIM2 (p = 6.63 × 10 -6 ) and suggestive association with periodontitis (p = 7.52 × 10 -4 ). We also validated the significant association between rs2814770 and AIM2 expression in independent expression data set. Pathway analysis revealed that genes co-expressed with AIM2 were significantly enriched in immune-and inflammation-related pathways. Conclusion:Our findings implicate that AIM2 is a susceptibility gene, expression of which in gingiva may influence periodontitis risk. Further functional investigation of AIM2 may provide new insight for periodontitis pathogenesis. K E Y W O R D SAIM2, eQTL, GWAS, periodontitis, Sherlock

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Zheng

Meng

et al. 2020

J Clinic Periodontology

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“…However, their expression decreased in tissues affected by chronic periodontitis. Chronic periodontitis was characterized by increased expression of genes involved in responses to external stimuli, and underexpression of immune system‐related genes . The altered expression of these immune response genes suggests an important and differential role for the innate and adaptive immune response (B and T cells) in various states of periodontal disease .…”

Section: Transcriptomicsmentioning

confidence: 99%

“…Chronic periodontitis was characterized by increased expression of genes involved in responses to external stimuli, and underexpression of immune system-related genes. 10,12 The altered expression of these immune response genes suggests an important and differential role for the innate and adaptive immune response (B and T cells) in various states of periodontal disease. 12 A high level of B cell and plasmacyte gene expression was also found in a study examining periodontitis-related transcriptomes in human and nonhuman primates.…”

Section: Host Transcriptomicsmentioning

confidence: 99%

Host‐microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Nguyen

Sedghi

Ganther

et al. 2019

Periodontology 2000

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Periodontal studies using transcriptomics, proteomics, and metabolomics encompass the collection of mRNA transcripts, proteins, and small‐molecule chemicals in the context of periodontal health and disease. The number of studies using these approaches has significantly increased in the last decade and they have provided new insight into the pathogenesis and host‐microbe interactions that define periodontal diseases. This review provides an overview of current molecular findings using ‐omic approaches that underlie periodontal disease, including modulation of the host immune response, tissue homeostasis, and complex metabolic processes of the host and the oral microbiome. Integration of these ‐omic approaches will broaden our perspective of the molecular mechanisms involved in periodontal disease, advancing and improving the diagnosis and treatment of various stages and forms of periodontal disease.

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“…Most of those studies indicated that genes related to inflammation and defense mechanisms are up‐regulated in tissue affected by periodontitis, whereas genes related to cytoskeletal protein, structural proteins, extracellular matrix organization, and extracellular structure organization are down‐regulated . Interestingly, edentulous sites with a history of chronic periodontitis have shown a down‐regulation of immune system‐related genes . Transcriptomic studies of peri‐implantitis are, however, still scarce .…”

Section: Introductionmentioning

confidence: 99%

Long non‐coding RNA and mRNA expression profiles in peri‐implantitis vs periodontitis

Liu

et al. 2019

J of Periodontal Research

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Background and Objective:Peri-implantitis is a biofilm-mediated infectious disease that results in progressive loss of implant-supporting bone. As compared to its analogue periodontitis, peri-implantitis is generally known to be more aggressive, with comparatively rapid progression and less predictable treatment outcomes, especially when advanced. An understanding of molecular mechanisms underpinning the similarities and differences between peri-implantitis and periodontitis is essential to develop novel management strategies. This study aimed to compare long non-coding RNAs (lncRNAs) and messenger RNA (mRNA) expression profiles between peri-implantitis and periodontitis.Methods: Inflamed soft tissue from peri-implantitis and periodontitis lesions, and healthy gingival tissue controls were analyzed by microarray. Cluster graphs, gene ontology (GO) analysis, and pathway analysis were performed. Quantitative real-time PCR was employed to verify microarray results. The expression levels of RANKL and OPG in the three tissue types were also evaluated, using qRT-PCR. Coding non-coding (CNC) network analyses were performed.Results: Microarray analyses revealed 1079 lncRNAs and 1003 mRNAs as differentially expressed in peri-implantitis when compared to periodontitis. The cyclooxygenase-2 pathway was the most up-regulated biological process in peri-implantitis as compared to periodontitis, whereas hemidesmosome assembly was the most down-regulated pathway. Osteoclast differentiation was relatively up-regulated, and RANKL/OPG ratio was higher in peri-implantitis than in periodontitis. Conclusions:The study demonstrated that peri-implantitis and periodontitis exhibit significantly different lncRNA and mRNA expression profiles, suggesting that osteoclast differentiation-related pathways are comparatively more active in periimplantitis. These data highlight potential molecular targets for periodontitis and peri-implantitis therapy development. K E Y W O R D SlncRNA, microarray, peri-implantitis, periodontitis, transcriptome | 343 LIU et aL.

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Transcriptome of healthy gingival tissue from edentulous sites in patients with a history of generalized aggressive periodontitis

Abstract: This study demonstrates differences in the transcriptome of healthy gingival tissue from edentulous sites from GAgP when compared with that of H or CP patients.

Cited by 11 publications

References 49 publications

Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Integration of genome‐wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

Host‐microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Long non‐coding RNA and mRNA expression profiles in peri‐implantitis vs periodontitis

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