Dataset Information

Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns.

ABSTRACT:

Background

Periodontitis is the most common chronic inflammatory disease caused by complex interaction between the microbial biofilm and host immune responses. In the present study, high-throughput RNA sequencing was utilized to systemically and precisely identify gene expression profiles and alternative splicing.

Methods

The pooled RNAs of 10 gingival tissues from both healthy and periodontitis patients were analyzed by deep sequencing followed by computational annotation and quantification of mRNA structures.

Results

The differential expression analysis designated 400 up-regulated genes in periodontitis tissues especially in the pathways of defense/immunity protein, receptor, protease, and signaling molecules. The top 10 most up-regulated genes were CSF3, MAFA, CR2, GLDC, SAA1, LBP, MME, MMP3, MME-AS1, and SAA4. The 62 down-regulated genes in periodontitis were mainly cytoskeletal and structural proteins. The top 10 most down-regulated genes were SERPINA12, MT4, H19, KRT2, DSC1, PSORS1C2, KRT27, LCE3C, AQ5, and LCE6A. The differential alternative splicing analysis revealed unique transcription variants in periodontitis tissues. The EDB exon was predominantly included in FN1, while exon 2 was mostly skipped in BCL2A1.

Conclusions

These findings using RNA sequencing provide novel insights into the pathogenesis mechanism of periodontitis in terms of gene expression and alternative splicing.

SUBMITTER: Kim YG

PROVIDER: S-EPMC4988046 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Publications

Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns.

Kim Yong-Gun YG Kim Minjung M Kang Ji Hyun JH Kim Hyo Jeong HJ Park Jin-Woo JW Lee Jae-Mok JM Suh Jo-Young JY Kim Jae-Young JY Lee Jae-Hyung JH Lee Youngkyun Y

Human genomics 20160817 1

<h4>Background</h4>Periodontitis is the most common chronic inflammatory disease caused by complex interaction between the microbial biofilm and host immune responses. In the present study, high-throughput RNA sequencing was utilized to systemically and precisely identify gene expression profiles and alternative splicing.<h4>Methods</h4>The pooled RNAs of 10 gingival tissues from both healthy and periodontitis patients were analyzed by deep sequencing followed by computational annotation and qua ...[more]

PMID: 27531006

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Project description:The epithelial barrier at mucosal sites comprises an important mechanical protective feature of innate immunity, and is intimately involved in communicating signals of infection/tissue damage to inflammatory and immune cells in these local environments. A wide array of antimicrobial factors (AMF) exist at mucosal sites and in secretions that contribute to this innate immunity. A non-human primate model of ligature-induced periodontitis was used to explore characteristics of the antimicrobial factor transcriptome (n = 114 genes) of gingival biopsies in health, initiation and progression of periodontal lesions, and in samples with clinical resolution. Age effects and relationship of AMF to the dominant members of the oral microbiome were also evaluated. AMF could be stratified into 4 groups with high (n = 22), intermediate (n = 29), low (n = 18) and very low (n = 45) expression in healthy adult tissues. A subset of AMF were altered in healthy young, adolescent and aged samples compared with adults (e.g., APP, CCL28, DEFB113, DEFB126, FLG2, PRH1) and were affected across multiple age groups. With disease, a greater number of the AMF genes were affected in the adult and aged samples with skewing toward decreased expression, for example WDC12, PGLYRP3, FLG2, DEFB128, and DEF4A/B, with multiple age groups. Few of the AMF genes showed a >2-fold increase with disease in any age group. Selected AMF exhibited significant positive correlations across the array of AMF that varied in health and disease. In contrast, a rather limited number of the AMF significantly correlated with members of the microbiome; most prominent in healthy samples. These correlated microbes were different in younger and older samples and differed in health, disease and resolution samples. The findings supported effects of age on the expression of AMF genes in healthy gingival tissues showing a relationship to members of the oral microbiome. Furthermore, a dynamic expression of AMF genes was related to the disease process and showed similarities across the age groups, except for low/very low expressed genes that were unaffected in young samples. Targeted assessment of AMF members from this large array may provide insight into differences in disease risk and biomolecules that provide some discernment of early transition to disease.

Dataset Information

Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns.

Background

Methods

Results

Conclusions

Publications

Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns.

Similar Datasets

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