|Year : 2019 | Volume
| Issue : 2 | Page : 114-118
Identifying salivary transcriptome signatures for periodontal diagnosis
KJ Nisha1, Presanthila Janam2, K Harshakumar3
1 Department of Periodontics, Vydehi Institute of Dental Sciences and Research Centre, Bengaluru, Karnataka, India
2 Department of Periodontics, PMS College of Dental Sciences and Research, Thiruvananthapuram, Kerala, India
3 Department of Prosthodontics, Government Dental College, Thiruvananthapuram, Kerala, India
|Date of Web Publication||18-Jul-2019|
K J Nisha
C-1003, Rohan Avriti Apartment, ITPL Main Road, Mahadevapura Po, Bengaluru - 560 048, Karnataka
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Periodontal disease is one of the most prevalent diseases in the human population worldwide. It is the major cause of tooth loss in adults above 40 years. Advanced forms are characterized by inflammation that extends deep into the tissues of the periodontium, a process that eventually causes the loss of the supporting connective tissue and the alveolar bone leading to tooth loss. Application of salivary biomarkers for periodontal diagnostics is promising and could facilitate the diagnosis and treatment in a clinical practice by dental practitioners. The salivary transcriptome offers the combined advantages of high-throughput marker discovery in a noninvasive biofluid with very high patient compliance. Identifying alterations in salivary transcriptomic signatures using microarray or sequencing technologies will help to find novel biomarkers in periodontitis. This narrative review intends to provide a highlight on the potential application of salivary transcriptomics in periodontal diagnosis.
Keywords: MicroRNAs, periodontal diseases, saliva, transcriptome
|How to cite this article:|
Nisha K J, Janam P, Harshakumar K. Identifying salivary transcriptome signatures for periodontal diagnosis. J Nat Sc Biol Med 2019;10:114-8
|How to cite this URL:|
Nisha K J, Janam P, Harshakumar K. Identifying salivary transcriptome signatures for periodontal diagnosis. J Nat Sc Biol Med [serial online] 2019 [cited 2021 May 15];10:114-8. Available from: http://www.jnsbm.org/text.asp?2019/10/2/114/262957
| Introduction|| |
Saliva reflects virtually the entire spectrum of normal and disease states and its use as a diagnostic fluid meets the demands for an inexpensive, noninvasive, and accessible diagnostic tool. Regarded as the mirror of the body, saliva is an ideal translational research tool and diagnostic medium and is being used in novel ways to provide molecular biomarkers for a variety of oral and systemic diseases and conditions. It opens up the potential for nonexpert medical and paramedical staff to assess biomarkers of periodontitis and also offer a cost-effective approach for the screening of large population. Human saliva has been used increasingly for biomarker development to enable noninvasive detection of diseases. The term “salivaomics” was coined to highlight the omics constituents in saliva that can be used for biomarker development and personalized medicine [Figure 1].
Proteomic and transcriptomic studies conducted in human whole saliva in the recent years have identified many promising proteomic and messenger RNA (mRNA) biomarkers for detecting and diagnosing a variety of oral and systemic diseases, including oral cancer,,, primary Sjögren's syndrome, and breast cancer.
Periodontitis is a chronic noncommunicable disease (NCD) that shares social determinants and risk factors with the major NCDs that cause around two-thirds of deaths such as heart disease, diabetes, cancer, and chronic respiratory disease. Early diagnosis, prevention, and management of periodontal disease are important for improvement in oral health as well as systemic health. In the field of periodontal diagnosis, there has been a steady growing trend during the last two decades to develop tools to monitor periodontitis. Currently, diagnosis of periodontal disease relies primarily on clinical and radiographic parameters. These measures are useful in detecting evidence of past disease, or verifying periodontal health, but provide only limited information about patients and sites at risk for future periodontal breakdown. This narrative review intends to focus on the potential application of salivary transcriptomics in periodontal diagnosis.
| Search Strategy|| |
Articles were searched and selected for the related topic in PubMed; MEDLINE from 2000 to 2018 using the keywords salivary transcriptome, miRNA and periodontal disease. Article search included only those published in the English literature. An internet search was also done to obtain the relevant articles of our interest. The title of the articles and abstracts was reviewed.
| Transcriptome|| |
Transcriptome refers to the set of all RNA molecules from protein-coding mRNA to noncoding RNA, including ribosomal RNA, transfer RNA, long noncoding RNA, miRNA, and others. The term “transcriptome” may apply to an entire organism or a specific cell type. Total cellular RNAs are classified on the basis of their functions as presented in [Table 1].
In general, transcriptomes are a powerful means of generating comprehensive genome-level data sets on complex diseases , and have provided enormous insights in cancer research and also in conditions such as muscular dystrophy, Alzheimer's disease and dementia,, rheumatologic disorders,, and asthma.,
| Transcriptomics|| |
Transcriptomics refers to the study of the complete set of RNAs (transcriptome) encoded by the genome of a specific cell or organism at a specific time or under a specific set of conditions. Methods to comprehensively and systematically interrogate the expression of virtually all RNA species have been developed and complement global approaches to studying genome sequence, structure, and its variability.
| Transcriptomic Technologies|| |
Microarray (or “chip”) technology, and more recently high-throughput next-generation sequencing (NGS), has made assessing the transcriptome a routine laboratory practice. These transcriptomic techniques measure the abundance of thousands of transcripts in parallel. With their high potential, microarrays have several limitations including working with a predefined probe set and a high rate of false positives necessitating confirmation of results with reverse transcription-polymerase chain reaction (RT-PCR) or other techniques. The advent of high-throughput sequencing has helped us to overcome some of the limitations such as sensitivity, resolution, and narrow probe sets.
Owing to the development of high-throughput methods to study transcriptome expression profiles, there has been an exponential increase in the amount of data generated in this field over the last decade. Compared to microarray or quantitative PCR (qPCR)-based expression profiling techniques, NGS technology offers several advantages, including high sensitivity to measure transcriptome biomarker levels over a wide dynamic range, ability to identify novel biomarkers, and to detect their expression levels in species for which complete genomes are not yet available. It also allows multiplexing of samples by tagging libraries with barcodes during library preparation.,
| Salivary Transcriptome|| |
Cell-free saliva (CFS) has been found to contain over a thousand proteins that are thought to be involved in a wide range of biological functions, as well as mRNA and miRNA transcripts, and metabolites. Certain miRNA and mRNA molecules were shown to be highly stable, possible owing to protection by exosomes or protein complexes., Detecting changes in the salivary concentrations of these molecules have allowed the detection of oral and systemic diseases.
| Salivary Messenger Rnas as Biomarkers in Oral and Systemic Diseases|| |
mRNA is the direct precursor of proteins and, in general, the corresponding levels are correlated in cells and tissue samples. Characterization of mRNA profiles in body fluids provides insights into gene transcription in normal and disease states. Li et al. in 2004 discovered that human mRNAs are present in cell-free form in saliva. Based on this finding, the same group of scientists profiled salivary RNA of healthy individuals on gene expression arrays, establishing the “normal salivary core transcripts” (NSCT), a set of 185 mRNAs which were detected in each saliva supernatant of the studied ten healthy individuals. The translational utility of salivary transcriptome analysis was established by the array-based discovery of several oral cancer mRNA biomarkers. By looking at four genes from the NSCT (IL8, OAZ1, SAT, and IL1B), they were able to determine whether a saliva sample is from cancer or normal individual with a sensitivity and specificity of 91% each (receiver operating characteristic = 0.95).
Transcriptomic biomarkers for primary Sjögren's syndrome have undergone preclinical validation, and mRNA biomarkers for oral squamous cell carcinoma (OSCC) previously discovered in a US cohort  have been found to detect OSCC in a cohort of different ethnicity. Discovery and validation of transcriptomic salivary biomarkers has been performed for breast cancer using Affymetrix HG-U133-Plus 2.0 array discovery and preclinical validation in an independent cohort using RT-qPCR. Applying PRoBE design, Zhang et al. identified salivary mRNA biomarkers for pancreatic cancer with excellent sensitivity and specificity. Importantly, these biomarkers could also distinguish pancreatic cancer from chronic pancreatitis with excellent sensitivity and specificity of 96.7%. Furthermore, the ovarian cancer salivary transcriptome profile was discovered in a clinical case–control study using Affymetrix HG-U133-Plus 2.0 array and then validated with qPCR by Lee et al.
| Salivary Mirnas as Biomarkers in Oral and Systemic Diseases|| |
miRNAs are a class of small noncoding RNAs of about 18–25 nucleotides in length, which are highly conserved during evolution. These miRNAs posttranscriptionally regulate gene expression by binding to 3'-untranslated regions of target mRNAs by binding to its complementary base pair. This results in RNA degradation and/or translational inhibition. These RNAs have been well characterized and found to play roles in cell growth, differentiation, apoptosis, pathogen-host interactions, and stress responses and immune function and are found in saliva., The similarity between miRNA profiles of saliva and other body fluids strongly supports the potential of using miRNAs (and possibly other ncRNAs) from human CFS as biomarkers for various human diseases.,,
Salivary miRNAs were assessed as putative biomarkers for OSCC, parotid gland tumors, esophageal cancer, Sjögren's syndrome, and pancreatic cancer.
| Salivary Transcriptomics in Periodontal Diagnostics|| |
Although many putative biomarkers have been discovered using proteomic, transcriptomic, epigenetic, and metabolomic technologies, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity and translation into clinical practice. Recent advances in transcriptomic high-throughput technologies are shedding new light on salivary biomarker discovery, which can elevate salivary diagnosis of periodontal diseases to a higher level. Profiling of healthy and periodontal transcriptomes will help to find the most significant candidate genes for the onset and progression of periodontal diseases. Those discriminatory candidate genes must be validated for their sensitivity and specificity as salivary biomarkers.
The results from the approaches that have been successfully applied to the detection of cancer-associated biomarkers in saliva have prompted David Wong and his team to do investigation for discovering gene signatures in periodontitis patients by performing multiplex transcriptomic analysis of mRNA in human saliva. They believe that biomarkers found in saliva may actually predict bursts of periodontal disease activity.
Recent studies have investigated the profile of miRNAs in inflamed gingiva in comparison with healthy tissue., A large-scale genome-wide microarray study that assessed over 1000 miRNAs in 198 gingival tissues showed that the expression of 159 miRNAs was significantly different when compared between healthy and inflamed gingiva. Further studies are still required to determine the function of miRNAs in the complex processes of periodontal tissue homeostasis and pathogenesis.
The use of saliva as a diagnostic medium for detection of miRNA biomarkers is noninvasive and accessible. Exosomes might play a key role in miRNA transport from different cells into saliva. In periodontal tissue, the junctional epithelial layers allow transport of several molecules between tissue and gingival crevicular fluid (GCF). In addition, an enlarged permeability with an increase of GCF flow is observed during gingival and periodontal inflammation. Consequently, high amounts of miRNA might pass the junctional epithelium, arriving GCF, and thus saliva. Two recently published studies have reported identification and dysregulation of miRNAs in GCF sample from periodontitis patients., Salivary miRNA diagnostic methods seem feasible and salivary miRNA diagnosis for periodontal disease is indeed a revolutionary idea.
| Conclusion|| |
Last two decades of research in the field of periodontal diagnostics have seen an enormous growth with new tools being developed with the help of bioinformatics for noninvasive and cost-effective diagnosis of periodontal diseases. Salivary biomarkers for detection and prediction of oral and systemic diseases are gaining wide popularity. Identifying alterations in salivary transcriptomic signatures using microarray or sequencing technologies will help to find novel biomarkers for diagnosing periodontal disease and will also help in understanding the molecular intricacies of periodontal pathogenesis. This may also improve personalized approaches that can be used to individualize treatment plans for periodontitis patients. However, these technologies will take a while to be standardized, and it will be a while before these are available as a standard practice for diagnosing people with a risk for periodontitis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Hu S, Loo JA, Wong DT. Human saliva proteome analysis and disease biomarker discovery. Expert Rev Proteomics 2007;4:531-8.
Jou YJ, Lin CD, Lai CH, Chen CH, Kao JY, Chen SY, et al.
Proteomic identification of salivary transferrin as a biomarker for early detection of oral cancer. Anal Chim Acta 2010;681:41-8.
Fábián TK, Fejérdy P, Csermely P. Salivary genomics, transcriptomics and proteomics: The emerging concept of the oral ecosystem and their use in the early diagnosis of cancer and other diseases. Curr Genomics 2008;9:11-21.
Hu S, Wang J, Meijer J, Ieong S, Xie Y, Yu T, et al.
Salivary proteomic and genomic biomarkers for primary Sjögren's syndrome. Arthritis Rheum 2007;56:3588-600.
Zhang L, Xiao H, Karlan S, Zhou H, Gross J, Elashoff D, et al.
Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. PLoS One 2010;5:e15573.
Thompson SD, Prahalad S, Colbert RA. Integrative genomics. In: Petty RE, Laxer RM, Lindsley CB, Wedderburn LR, editors. Textbook of Pediatric Rheumatology. 7th
ed., Ch. 5. Philadelphia, PA: W.B. Saunders; 2016. p. 43-53.e3.
Chung CH, Bernard PS, Perou CM. Molecular portraits and the family tree of cancer. Nat Genet 2002;32 Suppl: 533-40.
Quackenbush J. Microarray analysis and tumor classification. N Engl J Med 2006;354:2463-72.
Haslett JN, Kunkel LM. Microarray analysis of normal and dystrophic skeletal muscle. Int J Dev Neurosci 2002;20:359-65.
Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG, Lukiw WJ. Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: Transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. J Neurosci Res 2002;70:462-73.
Haroutunian V, Katsel P, Schmeidler J. Transcriptional vulnerability of brain regions in alzheimer's disease and dementia. Neurobiol Aging 2009;30:561-73.
Thornton S, Sowders D, Aronow B, Witte DP, Brunner HI, Giannini EH, et al.
DNA microarray analysis reveals novel gene expression profiles in collagen-induced arthritis. Clin Immunol 2002;105:155-68.
van der Pouw Kraan TC, van Baarsen LG, Rustenburg F, Baltus B, Fero M, Verweij CL. Gene expression profiling in rheumatology. Methods Mol Med 2007;136:305-27.
Burke W. Genomics as a probe for disease biology. N Engl J Med 2003;349:969-74.
Izuhara K, Saito H. Microarray-based identification of novel biomarkers in asthma. Allergol Int 2006;55:361-7.
Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, et al.
Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 2010;16:991-1006.
Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: Approaches and considerations. Nat Rev Genet 2012;13:358-69.
Bonne NJ, Wong DT. Salivary biomarker development using genomic, proteomic and metabolomic approaches. Genome Med 2012;4:82.
Palanisamy V, Sharma S, Deshpande A, Zhou H, Gimzewski J, Wong DT, et al.
Nanostructural and transcriptomic analyses of human saliva derived exosomes. PLoS One 2010;5:e8577.
Park NJ, Zhou H, Elashoff D, Henson BS, Kastratovic DA, Abemayor E, et al.
Salivary microRNA: Discovery, characterization, and clinical utility for oral cancer detection. Clin Cancer Res 2009;15:5473-7.
Li Y, Zhou X, St John MA, Wong DT. RNA profiling of cell-free saliva using microarray technology. J Dent Res 2004;83:199-203.
Li Y, St John MA, Zhou X, Kim Y, Sinha U, Jordan RC, et al.
Salivary transcriptome diagnostics for oral cancer detection. Clin Cancer Res 2004;10:8442-50.
Hu S, Gao K, Pollard R, Arellano-Garcia M, Zhou H, Zhang L, et al.
Preclinical validation of salivary biomarkers for primary Sjögren's syndrome. Arthritis Care Res (Hoboken) 2010;62:1633-8.
Brinkmann O, Kastratovic DA, Dimitrijevic MV, Konstantinovic VS, Jelovac DB, Antic J, et al.
Oral squamous cell carcinoma detection by salivary biomarkers in a Serbian population. Oral Oncol 2011;47:51-5.
Zhang L, Farrell JJ, Zhou H, Elashoff D, Akin D, Park NH, et al.
Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology 2010;138:949-570.
Lee YH, Kim JH, Zhou H, Kim BW, Wong DT. Salivary transcriptomic biomarkers for detection of ovarian cancer: For serous papillary adenocarcinoma. J Mol Med (Berl) 2012;90:427-34.
Bartel DP. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004;116:281-97.
Zeng Y. Principles of micro-RNA production and maturation. Oncogene 2006;25:6156-62.
Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al.
MicroRNA expression profiles classify human cancers. Nature 2005;435:834-8.
Weber JA, Baxter DH, Zhang S, Huang DY, Huang KH, Lee MJ, et al.
The microRNA spectrum in 12 body fluids. Clin Chem 2010;56:1733-41.
Bahn JH, Zhang Q, Li F, Chan TM, Lin X, Kim Y, et al.
The landscape of microRNA, piwi-interacting RNA, and circular RNA in human saliva. Clin Chem 2015;61:221-30.
Patel RS, Jakymiw A, Yao B, Pauley BA, Carcamo WC, Katz J, et al.
High resolution of microRNA signatures in human whole saliva. Arch Oral Biol 2011;56:1506-13.
Matse JH, Yoshizawa J, Wang X, Elashoff D, Bolscher JG, Veerman EC, et al.
Discovery and prevalidation of salivary extracellular microRNA biomarkers panel for the noninvasive detection of benign and malignant parotid gland tumors. Clin Cancer Res 2013;19:3032-8.
Xie Z, Chen G, Zhang X, Li D, Huang J, Yang C, et al.
Salivary microRNAs as promising biomarkers for detection of esophageal cancer. PLoS One 2013;8:e57502.
Alevizos I, Alexander S, Turner RJ, Illei GG. MicroRNA expression profiles as biomarkers of minor salivary gland inflammation and dysfunction in Sjögren's syndrome. Arthritis Rheum 2011;63:535-44.
Xie Z, Yin X, Gong B, Nie W, Wu B, Zhang X, et al.
Salivary microRNAs show potential as a noninvasive biomarker for detecting resectable pancreatic cancer. Cancer Prev Res (Phila) 2015;8:165-73.
Kim JJ, Kim CJ, Camargo PM. Salivary biomarkers in the diagnosis of periodontal diseases. J Calif Dent Assoc 2013;41:119-24.
Lee YH, Na HS, Jeong SY, Jeong SH, Park HR, Chung J, et al.
Comparison of inflammatory microRNA expression in healthy and periodontitis tissues. Biocell 2011;35:43-9.
Na HS, Park MH, Song YR, Kim S, Kim HJ, Lee JY, et al.
Elevated microRNA-128 in periodontitis mitigates tumor necrosis factor-α response via p38 signaling pathway in macrophages. J Periodontol 2016;87:e173-82.
Stoecklin-Wasmer C, Guarnieri P, Celenti R, Demmer RT, Kebschull M, Papapanou PN, et al.
MicroRNAs and their target genes in gingival tissues. J Dent Res 2012;91:934-40.
Saito A, Horie M, Ejiri K, Aoki A, Katagiri S, Maekawa S, et al.
MicroRNA profiling in gingival crevicular fluid of periodontitis – A pilot study. FEBS Open Bio 2017;7:981-94.
Bosshardt DD, Lang NP. The junctional epithelium: From health to disease. J Dent Res 2005;84:9-20.
Radović N, Nikolić Jakoba N, Petrović N, Milosavljević A, Brković B, Roganović J, et al.
MicroRNA-146a and microRNA-155 as novel crevicular fluid biomarkers for periodontitis in non-diabetic and type 2 diabetic patients. J Clin Periodontol 2018;45:663-71.