Contribution of Multi-Omics in Understanding of Periodontal Disease

Post-Graduate Theory Exam Notes

1. INTRODUCTION AND DEFINITION OF MULTI-OMICS

• The term "omics" refers to technologies that analyse biological molecules comprehensively by appending the suffix "-omics" to the type of molecule studied - genomics (genes), transcriptomics (RNA transcripts), proteomics (proteins), metabolomics (metabolites), and metagenomics/microbiomics (microbiota). (Newman & Carranza's 14th Ed., Ch. 1 - Precision Medicine)
• Periodontal disease is a complex, multifactorial condition; its onset and progression result from disruption of homeostasis between the resident microbiota and the host. Multi-omics approaches allow simultaneous study of multiple biological layers that contribute to this disruption. (Biomarkers in Periodontal Health and Disease - Buduneli)
• The five major branches of salivary/biological fluid analysis in periodontics are: proteomics, transcriptomics, microRNA (miRNA), metabolomics, and microbiomics. (Biomarkers in Periodontal Health and Disease - Buduneli)
• One of the current scientific challenges is to integrate proteomic, transcriptomic, metabolic, and interactomic data to provide a more complete picture of disease in living organisms. (Essential Microbiology for Dentistry - Samaranayake, 5th Ed.)

2. GENOMICS

A. Definition and Tools

• Genomics refers to the study of all genes within the chromosome of a cell. Human Genome Project data has provided a rich genetic resource to understand oral diseases including periodontal disease. (Essential Microbiology for Dentistry - Samaranayake)
• Key tools include: DNA microarray technology, Next-Generation Sequencing (NGS), polymerase chain reaction (PCR), Genome-Wide Association Studies (GWAS), and bioinformatics platforms. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• NGS uses massively parallel sequencing, generating millions of short reads in a single machine run, making genomics analysis faster and more affordable than Sanger sequencing, and applicable clinically. (Newman & Carranza's 14th Ed., Ch. 1)

B. Heritability of Periodontitis

• Twin studies demonstrate heritability estimates of ~38-50% for chronic periodontitis, confirming a significant genetic component. (Clinical Periodontology and Implant Dentistry - Lindhe, 6th Ed.)
• Genetic risk is influenced by gene polymorphisms, which are known to be associated with periodontal disease. (Newman & Carranza's 14th Ed., Ch. 8 - Biologic Systems Model)

C. Candidate Gene Studies and SNPs

• Single nucleotide polymorphisms (SNPs) in cytokine genes - notably IL-1α, IL-1β, IL-1RN (IL-1 receptor antagonist), TNF-α, IL-6, IL-10, CD14 - have been associated with increased susceptibility to periodontal disease. (Clinical Periodontology and Implant Dentistry - Lindhe, 6th Ed.)
• The IL-1 composite genotype (carrying specific alleles of IL-1α and IL-1β) was one of the first reported genetic risk factors for severe periodontitis; however, subsequent studies showed limited reproducibility across populations. (Newman & Carranza's 14th Ed.)
• Polymorphisms in Fc-gamma receptor (FcγR) genes influence the capacity of neutrophils and macrophages to clear periodontal pathogens, thus modulating disease susceptibility. (Clinical Periodontology and Implant Dentistry - Lindhe, 6th Ed.)

D. Genome-Wide Association Studies (GWAS)

• GWAS investigates genetic variation across the entire genome simultaneously using hundreds of thousands of SNP markers. It is a non-hypothesis-driven approach, not requiring a prior guess about which genes are implicated. (Newman & Carranza's 14th Ed., Ch. 9)
• Advantages: Can identify novel susceptibility loci not predicted by existing hypotheses about disease pathways.
• Limitations: Requires large clinical sample sizes; to date, no single, strongly replicated GWAS finding exists for periodontitis. Most statistically significant associations found are of marginal effect size. (Newman & Carranza's 14th Ed., Ch. 9)
• GWAS meta-analyses have been used by combining datasets across multiple study populations to identify relevant loci; a few loci of borderline significance have been reported. (Clinical Periodontology and Implant Dentistry - Lindhe, 6th Ed.)
• GWAS also revealed the non-coding RNA ANRIL as a locus associated with both periodontitis and atherosclerosis, suggesting shared genetic pathways for systemic disease associations. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

E. Rare Variants and Monogenic Periodontitis

• Exome sequencing and linkage studies have identified rare gene variants responsible for monogenic (Mendelian) forms of periodontitis, e.g., mutations in the cathepsin C (CTSC) gene in Papillon-Lefèvre syndrome. (Newman & Carranza's 14th Ed., Ch. 9)
• Advances in NGS (whole-exome and whole-genome sequencing) can now detect rare, high-impact mutations in patients with early-onset, severe forms of periodontitis not explained by common variants. (Newman & Carranza's 14th Ed., Ch. 9)

F. Infectogenomics

• "Infectogenomics" describes the relationship between host genetic factors and the oral microbiota. Genetic factors influencing innate immunity modulate the relative capacity for pathogenic invasion, proliferation, and clearance. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Immune dysfunction may have genetic underpinnings; an inappropriate host response to microbial encounter may result in: (i) chronic inflammatory states driven by chemokine networks, (ii) autoimmunity, (iii) breakdown in innate immune signalling in the oral cavity. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

3. EPIGENOMICS (EPIGENETICS)

A. Definition

• Epigenetics refers to changes in phenotype or gene expression caused by mechanisms other than changes in the DNA sequence itself - i.e., changes in which the gene is expressed rather than what the gene is. (Newman & Carranza's 14th Ed., Ch. 9)
• Epigenetics can be defined as all meiotically and mitotically heritable changes in gene expression not encoded in the DNA sequence itself. Two major epigenetic mechanisms are:
  1. Post-translational modification of histone proteins (chromatin remodelling)
  2. DNA methylation (methylation of cytosines in CpG dinucleotides) (Newman & Carranza's 14th Ed., Ch. 8; Clinical Periodontology and Implant Dentistry - Lindhe)

B. Epigenetic Changes in Periodontitis

• Gingival tissues from periodontitis patients have altered epigenetic patterns, particularly at inflammation-related genes (Barros and Offenbacher 2014). (Biomarkers in Periodontal Health and Disease - Buduneli)
• The methylation pattern of the IL-8 gene promoter in individuals with chronic periodontitis was altered compared to healthy subjects (Oliveira et al. 2009). (Biomarkers in Periodontal Health and Disease - Buduneli)
• TLR-2 and TLR-4 gene promoter methylation: Major unmethylation of TLR-4 promoter found in periodontitis. P. gingivalis induces de novo DNA methylation in TLR-2 promoter in gingival epithelial cells, blunting the inflammatory response - a mechanism by which keystone pathogens evade host immunity. (Biomarkers in Periodontal Health and Disease - Buduneli)
• Wide epigenetic alterations in chemokine and cytokine genes (including TNF-α, IFN-γ, COX-2) are reported in epithelial cells from periodontitis-affected gingival tissue (hypermethylation of TNF-α and IFN-γ promoters). (Biomarkers in Periodontal Health and Disease - Buduneli)
• Epigenetic regulation appears most important at the stage of immune activation: upregulation of pro-inflammatory cytokines and downregulation of anti-inflammatory cytokines are epigenetically mediated. (Biomarkers in Periodontal Health and Disease - Buduneli)
• "Periodontal Mosaicism": Coined by Benakanakere et al. (2019) - refers to epigenetic variation in gingiva leading to variation in disease susceptibility at different sites in the same individual; explains the high site-specificity of periodontitis. (Biomarkers in Periodontal Health and Disease - Buduneli)
• Periodontal disease promotes DNA methylation, and is closely linked with diabetes mellitus complications, which are also related to epigenetic changes (Newman & Carranza's 14th Ed., Ch. 9)
• Low concordance in monozygotic twins for periodontitis susceptibility raised the possibility of epigenetic differences acquired during development and aging. (Clinical Periodontology and Implant Dentistry - Lindhe, 6th Ed.)

C. Clinical Relevance of Epigenomics

• Epigenetic modifications are potentially reversible with environmental interventions (e.g., smoking cessation, treatment of diabetes) - this opens therapeutic targets. (Newman & Carranza's 14th Ed.; Robbins and Cotran Pathologic Basis)
• In a broader context, many drugs targeting the cancer epigenome (e.g., DNMT inhibitors, HDAC inhibitors) are moving into clinical practice; similar approaches may be applicable in periodontitis management. (Robbins and Cotran Pathologic Basis of Disease)

4. TRANSCRIPTOMICS

A. Definition

• Transcriptomics is the study of messenger RNA molecules produced by a particular cell type - the complete set of RNA transcripts produced by the genome at any given time. (Essential Microbiology for Dentistry - Samaranayake)
• The human salivary transcriptome consists mainly of mRNA and microRNA (miRNA). Salivary mRNAs and endogenous miRNAs are protected from ribonuclease degradation by exosomes, making them stable diagnostic targets. (Biomarkers in Periodontal Health and Disease - Buduneli)

B. RNA Sequencing (RNA-seq)

• RNA-seq (mass parallel sequencing of complementary DNA/cDNA) is used to discover and quantify novel RNA transcripts in human and microbial cells, shedding light on immune response in oral health and disease. Enabled by NGS platforms. (Essential Microbiology for Dentistry - Samaranayake)
• Single-cell transcriptomics allows identification of novel cell populations and characterisation of cell heterogeneity; it is now being applied to gingival and periodontal tissues, opening the opportunity to find pathogenic cellular populations and dissect dysbiotic molecular targets. (Newman & Carranza's 14th Ed., Ch. 28)

C. MicroRNA (miRNA) in Periodontitis

• miRNAs are small, endogenous, non-coding RNAs ~20-22 nucleotides in length. They regulate gene expression at the post-transcriptional level through cleavage or translational repression of target mRNAs. (Biomarkers in Periodontal Health and Disease - Buduneli; Robbins and Cotran)
• miRNAs are involved in periodontal tissue homeostasis and regulation of osteoclast differentiation. mir-21 is highly expressed in gingiva during periodontitis and during osteoclastogenesis. (Biomarkers in Periodontal Health and Disease - Buduneli)
• Saliva and serum exosomes carry miRNAs; exosomes play prominent roles in immune system modulation and inflammation. (Biomarkers in Periodontal Health and Disease - Buduneli)
• miRNAs in periodontal disease represent potential biomarkers but require further standardised studies before routine clinical use. (Biomarkers in Periodontal Health and Disease - Buduneli)

D. Metatranscriptomics

• Metatranscriptomics is the analysis of microbial gene expression within the biofilm - what genes are actually being expressed by the microbiome. (Newman & Carranza's 14th Ed., Ch. 10)
• Metatranscriptomic studies revealed that microorganisms present in periodontitis - including classical pathogens - express more virulence factors (motility genes, enzymes involved in iron metabolism, antibiotic resistance genes) compared to those in health. (Newman & Carranza's 14th Ed., Ch. 10)
• Studies of the healthy-to-gingivitis transition revealed a network of regulatory genes in proteolytic and nucleolytic processes that are key for microbial virulence. This cannot be determined by 16S rRNA sequencing alone (composition only). (Newman & Carranza's 14th Ed., Ch. 15)
• Key reference: Jorth P et al. "Metatranscriptomics of the human oral microbiome during health and disease." mBio. 2014;5(2):e01012-14. (cited in Newman & Carranza's 14th Ed.; Biomarkers - Buduneli)

5. PROTEOMICS

A. Definition and Technologies

• Proteomics is the large-scale study of the entire complement of proteins expressed by a genome, present in a cell, tissue, or biofluid. The human proteome is estimated to contain >20,000 proteins. (Biomarkers in Periodontal Health and Disease - Buduneli)
• The proteome is more complex than the genome because protein number is orders of magnitude greater than the number of genes, and the proteome changes dynamically in response to the environment. (Essential Microbiology for Dentistry - Samaranayake)
• Key techniques: Two-dimensional gel electrophoresis, liquid chromatography-tandem mass spectrometry (LC-MS/MS), MALDI-TOF mass spectrometry, SELDI-TOF-MS, tandem mass tags (TMT). (Biomarkers in Periodontal Health and Disease - Buduneli; Newman & Carranza's 14th Ed.)

B. Salivary and GCF Proteomics

• More than 3,000 proteins have been identified in human saliva, sharing approximately 51% with plasma proteins. (Biomarkers in Periodontal Health and Disease - Buduneli)
• Proteomics has demonstrated its value for biomarker identification in GCF (gingival crevicular fluid) of periodontitis patients. (Biomarkers in Periodontal Health and Disease - Buduneli; Newman & Carranza's 14th Ed.)
• A MALDI-TOF MS study demonstrated that GCF mass spectra data could model and predict attachment loss at a site with 97% specificity (Ngo et al., Carneiro et al.). (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• MMP-9 (matrix metalloproteinase 9) and LCN2 (neutrophil gelatinase-associated lipocalin) levels were higher in GCF from periodontitis patients vs. healthy subjects; identified via tandem mass tags-based quantitative proteomics. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Stable isotope-labelled mass spectrometry studies of the GCF proteome found 180 proteins common to health and disease, 26 proteins unique to health, and 32 unique to periodontitis; proteins detected for the first time included bacterial virulence factor OMP85. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Proteomics of P. gingivalis: A proteomics approach was used to probe the phenotype of P. gingivalis, identifying its protein expression in the context of a model oral microbial community; this approach identified proteins that mediate community-level virulence. (Newman & Carranza's 14th Ed., Ch. 17)

C. Single-Cell Proteomics

• Emerging single-cell proteomics provides cellular-level resolution, allowing identification of novel pathogenic cell populations within periodontal tissues that contribute to dysbiosis and tissue destruction. (Newman & Carranza's 14th Ed., Ch. 28)

D. Challenges

• The major challenge in GCF/salivary proteomics is the dynamic range of protein concentrations (spanning several orders of magnitude); highly abundant proteins (e.g., albumin) can mask low-abundance proteins of interest. (Biomarkers in Periodontal Health and Disease - Buduneli)

6. METABOLOMICS

A. Definition

• Metabolomics is the comprehensive identification, quantification, and analysis of metabolites (small molecules generated during metabolism), providing an instantaneous snapshot of the physiology of an organism. (Essential Microbiology for Dentistry - Samaranayake; Biomarkers in Periodontal Health and Disease - Buduneli)
• Key techniques: Capillary electrophoresis-mass spectrometry (CE-MS), time-of-flight mass spectrometry, NMR spectroscopy, liquid chromatography-MS.

B. Metabolomic Insights in Periodontitis

• Metabolomics studies revealed elevated macromolecular degradation in periodontal disease - specifically elevated metabolites related to collagen and extracellular matrix breakdown (Barnes et al. 2011: "Metabolomics reveals elevated macromolecular degradation in periodontal disease." J Dent Res. 90:1293-7). (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Salivary metabolomics can aid in the diagnosis of oral cancer and periodontal diseases (Mikkonen et al. 2016, J Periodontal Res. 51:431-437). (Essential Microbiology for Dentistry - Samaranayake)
• Metabolomics is regarded as a promising technology for: (i) discovering biomarkers, (ii) monitoring oral health status, (iii) guiding treatment planning, and (iv) monitoring treatment response. (Biomarkers in Periodontal Health and Disease - Buduneli)

C. Interactomics

• Interactomics (a related field) studies all interactions between proteins and other molecules within an organism, comparing interaction networks between health and disease states. It helps elucidate how metabolic networks are disrupted in periodontitis. (Essential Microbiology for Dentistry - Samaranayake)

7. METAGENOMICS AND MICROBIOMICS

A. Definition and Significance

• Metagenomics is the culture-independent direct analysis of total DNA extracted from a microbial community, allowing identification of all organisms including those not cultivable by conventional methods. (Essential Microbiology for Dentistry - Samaranayake; Clinical Periodontology and Implant Dentistry - Lindhe)
• Approximately 50% of oral taxa associated with periodontal disease are uncultivatable - these were first identified via metagenomic approaches. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

B. Key Techniques

1. 16S rRNA gene sequencing: Universal, cost-effective technique to profile microbiome based on variation in the bacterial 16S rRNA gene. Can identify bacterial species and measure relative abundance in plaque samples from periodontitis patients. Identifies genera and species but not virulence genes or resistance patterns. (Newman & Carranza's 14th Ed., Ch. 1; Clinical Periodontology and Implant Dentistry - Lindhe)
2. Shotgun whole-genome sequencing: Amplifies all genes in a sample, enabling comprehensive evaluation of biofilm composition (bacteria, viruses, phages, archaea, fungi) and metabolic functionality. Goes beyond 16S by providing virulence and resistance gene data. (Newman & Carranza's 14th Ed., Ch. 10)
3. Human Oral Microbe Identification Microarray (HOMIM): Allows simultaneous detection of ~300 key bacterial species including uncultivated ones, using 16S rRNA-based oligonucleotide probes on glass slides. Described the full diversity of periodontal microbiota. (Clinical Periodontology and Implant Dentistry - Lindhe)
4. Pyrosequencing and next-generation sequencing: Dramatically expanded the known diversity of oral microbiota - up to 16,000 species identified in subgingival plaque using these tools. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.; Essential Microbiology for Dentistry - Samaranayake)

C. Key Findings from Metagenomics in Periodontitis

• Metagenomic studies confirmed species associated with periodontal health (e.g., Actinomyces spp.) and disease (e.g., Prevotella, Fusobacterium, Treponema, Selenomonas, Porphyromonas). (Newman & Carranza's 14th Ed., Ch. 10)
• 16S rRNA diversity analysis revealed that samples cluster according to disease severity and oral hygiene status. (Newman & Carranza's 14th Ed., Ch. 15)
• P. gingivalis was the first oral microbe to have its complete genome sequenced (Sanger sequencing), enabling proteomics-based interrogation of its virulence mechanisms. (Newman & Carranza's 14th Ed., Ch. 15)
• Metagenomics (16S rRNA amplification, cloning, and sequencing) described 13 bacterial phyla in the human oral cavity including Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes, Synergistetes, TM7, and methanogenic Archaea. (Clinical Periodontology and Implant Dentistry - Lindhe)
• Novel periodontal pathogen candidates identified by metagenomic approaches include: Eubacterium saphenum, Filifactor alocis, Catonella morbi, Megasphaera spp., Dialister spp., Selenomonas sputigena - all detected in disease but rarely in health. (Clinical Periodontology and Implant Dentistry - Lindhe)
• The concept of dysbiosis: Metagenomic approaches confirmed that periodontal disease is not caused by simple acquisition of specific pathogens, but by a community-level dysbiotic shift - a relative overrepresentation of pathobionts with altered metabolic activity. (Biomarkers in Periodontal Health and Disease - Buduneli; Newman & Carranza's 14th Ed.)
• Metagenomics revealed that the oral microbiome composition is influenced by systemic conditions (e.g., diabetes mellitus type 2, Crohn's disease), demonstrating the role of host disease in shaping microbial community dynamics. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

D. Subgingival Fungi, Archaea, and Viruses

• Beyond bacteria, omics approaches (under "Subgingival fungi, Archaea, and viruses under the omics lens") have redefined the subgingival microbiome to include these non-bacterial organisms, whose roles in periodontal disease pathogenesis are being reevaluated. (Newman & Carranza's 14th Ed., Ch. 10)

8. INTEGRATION OF MULTI-OMICS: SYSTEMS BIOLOGY APPROACH

• Systems biology integrates data from all omics layers to understand how biological components interact across scales (person level → genetic/epigenetic level → biologic phenotype → clinical phenotype). (Newman & Carranza's 14th Ed., Ch. 8 - Offenbacher's biologic systems model)
• The biologic systems model of periodontitis (Offenbacher, Barros, Beck, 2008) recognises three interacting levels:
  1. Person Level: subgingival biofilm composition, smoking, diabetes, other risk factors
  2. Genetic/Epigenetic Level: gene polymorphisms, epigenetic modifications
  3. Biologic Phenotype: immune-inflammatory cellular and molecular responses → leading to the Clinical Phenotype (Newman & Carranza's 14th Ed., Ch. 8)
• With the advent of metabolomics and proteomics, a new level of resolution is now possible - providing details about the function of microbes versus only their composition. (Newman & Carranza's 14th Ed., Ch. 15)
• The GCF molecular profile can be clustered with salivary molecular profiles, subgingival microbial profile data, and/or clinical signs to increase overall diagnostic accuracy - leading to more precise treatment planning. (Newman & Carranza's 14th Ed., Ch. 1)
• Multi-omic complementary approaches (proteomic + metabolomic + metagenomic) are beginning to provide increased granularity into complex host-microbe and inter-microbe interactions; however, at the time of a recent meta-analysis, only ~12 proteomic studies and 1 metabolomic study had been applied to characterising periodontitis. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Pharmacogenomics - the application of genomics to predict drug response and toxicity - is a growing area that will eventually personalise the antimicrobial and host-modulatory therapies used in periodontics. (Newman & Carranza's 14th Ed., Ch. 9)

9. BIOINFORMATICS: THE BACKBONE OF MULTI-OMICS

• Bioinformatics forms the crucial pivotal point for all omics technologies. The avalanche of data generated by NGS and similar techniques cannot be sorted using traditional methods; computational methods for mining biological databases are indispensable. (Essential Microbiology for Dentistry - Samaranayake)
• Key databases: Human ORAL Microbiome Database (eHOMD), OralCard (bioinformatic tool for oral proteome study), PubMed/NCBI repositories. (Newman & Carranza's 14th Ed.; Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• The "core microbiome" concept - defining a healthy oral microbiome - has been an important thrust of metagenomic research using deep sequencing and bioinformatics, though definition remains a challenge. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

10. BIOMARKERS IN PERIODONTICS: MULTI-OMICS DERIVED

• MMP-8 lateral-flow POC immunotest is a commercially available chair-side test (5 min) derived from proteomic biomarker research - detects, predicts, and monitors periodontitis. (Biomarkers in Periodontal Health and Disease - Buduneli)

11. CLINICAL APPLICATIONS AND PRECISION PERIODONTICS

• Multi-omics is the foundation of Precision Periodontics (also called Personalised Dentistry) - the concept of tailoring treatment to the individual patient's genetic, microbial, and molecular profile. (Newman & Carranza's 14th Ed., Ch. 1)
• Identification of microbial profiles using 16S rRNA sequencing in plaque samples can help customise the treatment plan for individual periodontitis patients. (Newman & Carranza's 14th Ed., Ch. 1)
• NGS-enabled omics analyses, completable in hours, will allow clinicians to understand inherent differences in host response and plaque microbial composition at a deeper level and plan treatments accordingly. (Newman & Carranza's 14th Ed., Ch. 1)
• Future multi-omics data integration will enable: (i) early disease detection, (ii) risk stratification, (iii) prognosis prediction, (iv) identification of therapeutic targets (bacterial protein products from functional metatranscriptomics), and (v) monitoring treatment response. (Newman & Carranza's 14th Ed.; Biomarkers - Buduneli)
• The concept of the "oral-systemic axis" - understanding how the oral microbiome and periodontal inflammation influence systemic health (cardiovascular disease, diabetes, preterm birth, cancer) - is being increasingly defined through multi-omics integration. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.; Biomarkers - Buduneli)
• Multi-omic approaches to the oral microbiome have demonstrated highly heterogeneous patient responses (e.g., during cancer treatment), reinforcing the need for a personalised approach in periodontal treatment planning. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)

12. LIMITATIONS AND FUTURE DIRECTIONS

• Levels of transcripts, proteins, and metabolites reflect not only the genetic programming but also the consequences of host response to extrinsic factors - interpretation of multi-omics data requires contextual clinical correlation. (Biomarkers in Periodontal Health and Disease - Buduneli)
• Complementary multi-omic approaches to microbiome analysis are still in early stages for periodontitis; large-scale studies integrating all omics layers simultaneously are needed. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Approximately 50% of the oral microbiome remains uncultivatable, and the pathogenic contribution of many identified phylotypes is still unknown. (Genomics, Personalized Medicine and Oral Disease - Sonis Ed.)
• Robust, standardised, validated point-of-care tests based on multi-omics biomarkers are yet to achieve widespread clinical adoption due to high costs and validation challenges. (Biomarkers in Periodontal Health and Disease - Buduneli)

SUMMARY TABLE

REFERENCES USED

1. Newman and Carranza's Clinical Periodontology and Implantology, 14th Ed. - Chapters 1, 8, 9, 10, 15, 17, 28
2. Clinical Periodontology and Implant Dentistry (Lindhe), 6th Ed. - Chapters on genetics, microbiome, periodontitis
3. Biomarkers in Periodontal Health and Disease - Buduneli N. (Springer, 2020) - Chapters 5, 7
4. Genomics, Personalized Medicine and Oral Disease - Sonis ST (Ed.) (Springer, 2015) - Chapter on oral microbiome and genomics
5. Essential Microbiology for Dentistry - Samaranayake L. 5th Ed. - Chapter on '-omics' era
6. Oral Microbiology at a Glance - Lamont RJ & Jenkinson HF (Wiley-Blackwell, 2010)
7. Essentials of Clinical Periodontology and Periodontics - Reddy S. 5th Ed.
8. Robbins and Cotran Pathologic Basis of Disease - miRNA mechanisms, epigenomics, molecular pathology
9. Jawetz, Melnick, and Adelberg's Medical Microbiology - Oral microbiome, 16S rRNA, periodontal disease microbiology

1. Introduction - defines multi-omics and the five salivary omics branches
2. Genomics - covers heritability, SNPs, GWAS (with advantages/limitations), rare variants, exome sequencing, infectogenomics
3. Epigenomics - DNA methylation, histone modification, TLR-2/4 methylation, periodontal mosaicism, diabetes-epigenetics link
4. Transcriptomics - RNA-seq, miR-21, exosomal miRNAs, single-cell transcriptomics, metatranscriptomics
5. Proteomics - GCF/salivary proteomics, MMP-9/LCN2, MALDI-TOF, P. gingivalis proteomics, challenges
6. Metabolomics - macromolecular degradation metabolites, salivary diagnostics, interactomics
7. Metagenomics/Microbiomics - 16S rRNA, shotgun sequencing, HOMIM, dysbiosis, novel pathogens, archaea/viruses
8. Systems Biology Integration - Offenbacher's biologic systems model, multi-level integration
9. Bioinformatics - core microbiome, eHOMD, OralCard
10. Biomarkers Table - all omics layers with key markers, biofluids, and references
11. Precision Periodontics / Clinical Applications
12. Limitations and Future Directions