Ivaturi Sri Sai Meghana, Amitha Ramesh Bhat, Rahul Bhandary
1Postgraduate Student, Periodontology, A B Shetty Memorial Institute of Dental Sciences,
NITTE Deemed to be University, Deralakatte, Mangaluru, Karnataka
2Professor, Head of the Department, Periodontology, A B Shetty Memorial Institute of Dental Sciences,
NITTE Deemed to be University, Deralakatte, Mangaluru, Karnataka
3Professor, Periodontology, A B Shetty Memorial Institute of Dental Sciences,
NITTE Deemed to be University, Deralakatte, Mangaluru, Karnataka
ABSTRACT:
KEYWORDS: Metabolomics, Periodontal Medicine, Mass Spectroscopy, Saliva, Diagnosis.
1. INTRODUCTION:
Periodontitis is a chronic inflammatory condition characterized by the development and exponential proliferation of colonies of putative bacteria in the periodontal pocket. Bacteria contribute in the formation or progression of periodontitis and are frequently identified at active periodontitis sites.1 In particular, Koch's principle must be applied whenever an infection is induced by a specific pathogenic microbe. However, this principle was developed for grave infections and is difficult to apply to periodontal disease. Hence, Socransky et al. published a slightly different interpretation of this principle as a parameter for determining the aetiology of periodontal illness.2
Regarding that, the American Academy of Periodontology summarized the conditions of periodontopathogenic bacteria in 1996 and categorized the majority of them as orange or red complex, with the exception of A. actionmycetemcomitans (A.a). Trypsin-like enzymes are metabolised by all bacteria in the red complex. Bacteria including Porphyromonas gingivitis (P.g), Tannerella forsythia (T.f), and Treponema denticola (T.d) are frequently found in chronic periodontitis, but A.a and others are frequently seen in invasive or aggressive periodontitis. T.f is thought to have the most impact on severe periodontitis. The eradication of these bacteria can inhibit the progression of periodontitis.3
Personalized periodontics is one component of the 4 P therapeutic method. Personalized periodontics is described as categorizing patients and adapting therapeutic choices, therapies, and/or products for every patient. Individual variations in lifestyle, genetic factors, environment are all considered in personalized periodontics.
· A Predictive approach based on advanced diagnostic tools will enable us to identify at-risk patients and treat periodontitis early, when it is easier.
· It is a personalized Prevention based on the microbiological and genetic state of an individual patient.
· Management that is Personalized to the Patient's specific medical circumstances.
· The advent of Participatory periodontology, a notion in which networked citizens take a prominent role in their own health care, would highlight the patient's active commitment.4
The most fundamental disadvantage of standard diagnostic approaches is that this process is time intensive and may lead to delayed disease diagnosis, resulting in irreparable injury to periodontal tissues and eventually to tooth loss. Despite other approaches, examination of the metabolites in the body, or the "metabolome," can draw evident correlations to biological function and assist customize diagnosis and therapeutic effects in the domain of personalized periodontics.
Metabolomics is defined as "the objective measurement and identification of all metabolites present in a biological system," but in practice, the phrase is applied to a range of study fields”. It is a discipline that aims to identify and quantify all endogenous constituents that provide distinct chemical fingerprints in an organism or biofluid. It is a method that allows for a thorough examination of small molecule metabolites in live organisms.5
The untargeted metabolic characterization framework utilized for this evaluation is a hybrid of three separate platforms including
i) Gas Chromatography/ Mass Spectroscopy (GC/MS) optimized for acidic species,
ii) Ultra High-Performance Liquid Chromatography/ tandem Mass Spectroscopy (UHPLS/MS/MS) optimized for basic species.
The metabolites were identified by comparing mass spectral fragmentation signatures and the ions' chromatographic retention index in reference to library entries derived from predetermined conventional metabolites.
Liquid chromatography is a selective, sensitive, rugged and high throughput high performance and when coupled with tandem mass spectroscopy gives an accurate, sensitive results in routine quality control of the drug formulation.6 Owing to their distinct retention times and ion profiles, additional library entries were added for ions that won't be addressed by the specifications.7 The compounds predominantly phenolic compounds and flavonoids derivatives including carbohydrate, glycoside and different chemicals have been found to possess a wide range of activities which may help in the protection against non-curable diseases.8
3. TYPES:
1. Targeted Approach – The sample is biology driven and give more accurate and sensitive results. Examples are Liquid/gas chromatography and Mass spectroscopy
2. Non-Targeted Approach – Analysis of the whole saliva is done and it does not require any form of pre-treatment. Examples are Nuclear Magnetic Resonance (NMR) spectroscopy.
4.1 SAMPLE MATERIAL:
Following the documentation of each person's preclinical evaluation, plasma samples were taken from patients with clinically diagnosed metabolic/periodontitis illnesses.
4.2 SAMPLE PREPARATION:
Through the use of an automatic liquid handler and 100 mL of human plasma, proteins precipitate. Four standards in the methanol allowed for the estimation of extraction potential. The precipitate is split into four equivalent volumes and dried subject to nitrogen and in vacuum later. 2 aliquotes, reconstituted with 50 mL of water with each aliquotes containing 0.1% formic acid and another containing 6.5 mM ammonium bicarbonate at pH 8 are obtained for UHPLC technique whereas the aliquot is reconstituted in 50 mL of 0.1 percent formic acid in 10% methanol for the standard HPLC analysis. All reconstitution solvents included instrument standards for use as retention index markers and to track the functionality of the instruments. In case a repeat run was required, the residual aliquot was dried and kept at -80 °C for no more than two days. Concentrates will be assessed using two distinct linear trap quadrupole apparatuses, each containing an HPLC and a UHPLC.9
5.1 UHPLC/MS/MS ANALYSIS:
The presence of bioactive components with known anti-oxidant and cell death potential, validates the experimental flow for the development and/or refinement of crude extract-based drugs.10 The aliquots were divided, and the gradient isolated extracts were formic acid-reconstituted at 350L/min using 0.1 percent formic acid in H2O and 0.1 percent formic acid in methanol and 98 percent B for 0.9 minutes, while the ammonium bicarbonate-constituted extracts used 6.5 mM ammonium.
Figure 1
An LTQ mass spectrometer (Figure 1) was used to provide a 5 L aliquot of the sample for electrospray ionization (ESI) analysis. An LTQ mass spectrometer was used to provide a 5 L aliquot of the sample for electrospray ionization (ESI) analysis.MS. In independent injections utilizing separate acid/base specialized at 1.7 µm particle columns heated to 40 °C, the acidic extracts were evaluated for positive ions and the basic extracts for negative ions. The MS interface capillary was maintained at 350 °C, with sheath and aux gas flow for both positive and negative injections. The detector is usually switched among MS and MS/MS scans usually from 99 to 1000 m/z.
The MS-scan should be configured to an ion-trap with a cut-off time of roughly 200ms. The MS/MS scan is also set at an ion-trap target and ion-trap fill time, cut off time, the isolation window, which activates the normalised collision energy.11 Dynamic exclusion duration is often used to acquire MS/MS scans. Once a scan of a particular m/z gets received, it is added to a transitory MS/MS exclusion line-up for a user specified amount of time. This protocol is known as dynamic exclusion. Even though the instrument will not activate an MS/MS scan of the ion over and over again, dynamic exclusion enables greater MS/MS coverage of the ions expressed in the MS/MS scan.12
The samples appear to have gradient-eluted after applying 0.1% formic acid in water and 0.1% formic acid in methanol to the divided aliquots. A 15-microliter sample aliquot is loaded onto a 10-microliter sample and subjected to ESI-LTQ MS analysis as well. Within one specific injection, the apparatus cycled between positive and negative polarity while scanning the range of 99 to 1500 m/z.13
It was sometimes important to collect precise mass MS data. In these conditions, the ionization and chromatographic settings mentioned above are applied to develop a hybrid LTQ- Fourier transform ion cyclotron resonance (FTICR) MS. The mass resolution capacity is set to 50,000 and the apparatus cycled between scanning ion-trap and FTICR MS information (99-1000 m/z). Relying on internal standards, mass accuracy is estimated to be less than 5ppm.14
Employing Magnetic Resonance Spectroscopy (MRS) and Magnetic Resonance Imaging (MRI). NMR also aids in imaging and evaluating the metabolites of living samples. Contrarily, due to their intrinsic destructiveness, LC-MS based and GC-MS based technologies cannot be implemented to evaluate biological samples.15 (LC–MS) is an analytical technique that amalgamate the physical separation capability of liquid chromatography with the mass analysis capability of mass-spectrometry (MS).16. Furthermore, NMR spectroscopy can only be used to monitor metabolic fluxes in real time. More so than any other technique, NMR enables users to investigate chemistry in exquisite precision. It enables users to examine whole molecules at the atomic level and observe numerous different types of atoms, (13C, 15N) or reactive groups such as phosphate atoms, in addition to the 1H atoms (31P). Additionally, NMR spectroscopy can be performed to evaluate groups of metabolites, particularly protein-bound metabolites like lipoprotein particles, as well as to assess specific inorganic metabolites or complexes such as H+ and metal ions which could not be probably detected by PH using GC-MS or LC-MS.
With its lack of sensitivity, NMR does, however, have a number of disadvantages. Typically, LC-MS and GC-MS have a sensitivity 10 to 100 times higher than NMR spectroscopy.[17] This means that an LC-MS investigation will often return information on 1000+ recognised metabolites with concentrations greater than 10 to 100 nM, whereas an NMR-based metabolomic study will typically yield data on 50-200 classified compounds with concentrations higher than 1 M. 18
All samples are calibrated adopting a retention index predicted on Retention Time (RT) markers detected by a chromatogram. "A sample component's retention index is a measure achieved by interpreting the sample component's adapted retention volume (time) or retention factor to the tailored retention amounts of two standards eluted just before and after the sample's peak." The implementation of a RI in GC/MS analyses had been established, although not necessarily in LC; nonetheless, the same principles that allow its use in GC/MS also allow its use in LC/MS analysis. All those are isotopically labelled metabolites designated through specific elution behaviour, especially stability and retention time. Such retention markers are assigned a constant RI score that really never alters. As a result, although retention time varies owing to systematic inaccuracies, the RIs for these indicators will not fluctuate. As a result, each component is determined by its own elution relation to the adjoining retention markers.19
The results are compared to the chemical library available that is designed specifically for each approach. Three criteria are used to identify.
1. Index of retention with 75 RI units of the intended identification
2. The prototype progenitor mass is well within 0.4 m/z of the library and
3. The forward and reverse MS/MS scores.
The ions in the experimental and collection spectra are contrasted to determine the MS/MS scores.
An ideal forward result (100%) indicates that each of the ions contained in the trial spectrum are detected by the library when in predetermined proportions. Significant differences in ion ratios, as well as additional experimental inconsistencies in ions not found in the library, reduce the forward score. The forward score is a reliable predictor of the reported compound’s quality. Co-elution but yet another molecule of the equal mass introduces ions into trial spectrum, which affects the forward score.20
An ideal reverse result (100%) implies that each ion in the library is visible in the trial spectrum within the appropriate ratios. Significant ion ratio aberrations or ions available within the library that are not included through the trial spectrum will result in a negative reverse result.
· Detections are automatically authorized since all of the aforementioned parameters are fulfilled and the MS/MS forward and reverse results are more than 80%
· Complexes that satisfied the aforementioned requirements, but has poor MS/MS values (less than 35%) are immediately eliminated.
· Intermediate MS/MS forward and reverse values ranging from 36-79 are flagged for manual evaluation.20
Unless a MS/MS spectra is not acquired for a certain ion, the attribution is predicted only on retention and parent masses and it is assigned the status of review, indicating that it would be reviewed by an analyst.
A prerequisite for the clinical use of biomarker is elucidation of the specific indication, standardization of analytical methods, characterization of analytical features, incremental yield of different markers for given clinical indications.21 Numerous metabolites linked to oxidative stress, tissue breakdown, inflammation, and bacterial metabolism were discovered to be considerably increased by the condition.22
Table 1:
|
SL. No |
Observation |
Inference |
|
1. |
Elevation of mono and oligosaccharides like α-amylase, the main glycoside |
Increase in periodontal population |
|
2. |
Of the 20 amino acids necessary for protein synthesis, 1 cysteine is increased. |
In some microorganisms, dipeptides may be used by the cells instead of being further reduced to amino acid form. |
|
3. |
Increase in lysolipids, monoacylglycerol and fatty acids |
Upregulation of lipase activity in periodontal population probably due to increased expression, pH and activated inflammatory response |
7.2 Inflammatory response and host-bacteria interaction 24
Table 2:
|
SL. No |
Observation |
Inference |
|
1. |
Increase in aromatic amino acid metabolites |
It suggests a more active bacterial environment |
|
2. |
augmentation of carnitine (sole source of carbon and nitrogen by bacteria) |
argues in favour of oral bacteria using carnitine for metabolism |
|
Sl. No |
Observation |
Inference |
|
1. |
Purine degradation is regulated. Hypoxanthine Xanthine Uric acid + O2 -+ H2 O2 |
Enhanced inflammatory response leads to oxidative stress, urea is acknowledged to function in redox balance as a radical scavenger. |
|
2. |
An increase in oxidized glutathione and cysteine-glutathione disulfide |
Glutathione is essential in cellular defense against free radicals and xenobiotics. |
7.4 Lipid and Sphingolipid metabolism 26
Table 4
|
Sl. No |
Observation |
Inference |
|
1. |
Elevation of sphingomyelin, ceramides, glycosphingolipids |
lipid signalling molecules such as 12-HETE Increase in molecules that participate in intracellular signalling cascades |
Translational metabolomics has indeed demonstrated incredible promise in the realm of diagnostic medicine, serving as both an indicator and a predictor of disease activity. However, it is still a developing approach with its own set of drawbacks. Bridging these gaps and conducting active research to make it viable for adoption into clinical periodontal practice might be a significant resource that should be pursued, especially given the continual evolution of disease causation and progression
9. REERENCES:
1. Theilade E. The non-specific theory in microbial etiology of inflammatory periodontal diseases. Journal of Clinical Periodontology. 1986; 13(10): 905-11. doi: 10.1111/j.1600-051x.1986.tb01425.x.
2. Berezow AB, Darveau RP. Microbial shift and periodontitis. Periodontology 2000. 2011; 55(1): 36-47. doi: 10.1111/j.1600-0757.2010.00350.x.
3. Haffajee AD et al. Microbial complexes in supragingival plaque. Oral Microbiology and Immunology Journal. 2008; 23(3): 196-205. doi: 10.1111/j.1399-302X.2007.00411.x.
4. Knight ET, Murray Thomson W. A public health perspective on personalized periodontics. Periodontology 2000. 2018; 78(1): 195-200. doi: 10.1111/prd.12228.
5. Manjula B, Shivalinge Gowda KP. Metabolomics - An Exciting New Field within the “OMICS” Sciences. Research Journal of Pharmacology and Pharmacodynamics. 2010; 2(6): 363-369.
6. B. Srivastava et al. Method Development and Validation of Simultaneous Estimation of Emtricitabine and Tenofovir Alafenamide in Bulk and tablet Dosage form using LC-MS/MS. Research Journal of Pharmacy and Technology. 2021; 14(5):2493-6. doi: 10.52711/0974-360X.2021.00439
7. Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation. 2012 ;126(9): 1110-20. doi: 10.1161/CIRCULATIONAHA.111.060368.
8. Sathammai Priya et al. Gas chromatography and mass spectroscopy analysis of phytoactive components on the seed extract of Caesalpinia bonducella. Research Journal of Pharmacy and Technology. 2019; 12(10): 4628-4634. doi: 10.5958/0974-360X.2019.00796.0
9. Zhan X. Metabolomics - Methodology and Applications in Medical Sciences and Life Sciences. London, Intech Open; 2021. Available from: http://dx.doi.org/10.5772/intechopen.90987
10. Rajasekaran R., Suresh P. K. Physical and Chemical methods of extraction of Bioactive Molecules from Lepidium sativum Linn. and Antioxidant Activity-based screening and selection of extracts-Probable Phytochemical, Chromatography and mass spectroscopy analysis-based correlates. Research Journal of Pharmacy and Technology. 2021; 14(6): 3082-2. doi: 10.52711/0974-360X.2021.00539
11. Meyyanathan. S. N, Babu. B, Kalaivani. M. Development and Validation of An Analytical Liquid Chromatography-Tandem Mass Spectroscopy Method for the Estimation Febuxostat in Pharmaceutical Formulation. Research Journal of Pharmacy and Technology. 2019; 12(5): 2137-2140. doi: 10.5958/0974-360X.2019.00354.8
12. Dubey R.D et al. Mass Spectroscopy: A Versatile Analytical Technique. Research Journal of Science and Technology. 2011; 3(2): 55-64.
13. Lundy FT et al. Quantitative analysis of MRP-8 in gingival crevicular fluid in periodontal health and disease using microbore HPLC. Journal of Clinical Periodontology. 2001; 28(12): 1172-7. doi: 10.1034/j.1600-051x.2001.281213.x.
14. Xu Y et al. Evaluation of accurate mass and relative isotopic abundance measurements in the LTQ-orbitrap mass spectrometer for further metabolomics database building. Journal of Analytical Chemistry 2010; 82(13): 5490-501. doi: 10.1021/ac100271j.
15. A. Anka Rao et al. Rapid Quantitative Estimation of Glipizide and Sitagliptin in Rat Plasma by Liquid Chromatography and Mass spectroscopy (LC-MS). Research Journal of Pharmacy and Technology. 2022; 15(4): 1675-9. doi: 10.52711/0974-360X.2022.00280
16. Pankaj et al. LC–Tof-Ms an Influential Hyphenated Technique and its Application. Asian Journal of Pharmaceutical Analysis. 2023; 13(1): 35-1. doi: 10.52711/2231-5675.2023.00006
17. Patil Ankita Sanjeev, Gaurav Mahesh Doshi. Chemical Constituents from Polyalthia longifolia seeds extract by Gas Chromatography-Mass Spectroscopy (GC-MS) Studies. Research Journal of Pharmacy and Technology 2018; 11(6): 2489-2492. doi: 10.5958/0974-360X.2018.00459.6
18. Aimetti, M., Cacciatore, S., Graziano, A. et al. Metabonomic analysis of saliva reveals generalized chronic periodontitis signature. Metabolomics. 2012; 8: 465–474. https://doi.org/10.1007/s11306-011-0331-2
19. Kind T et al. Fiehn Lib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Journal of Analytical Chemistry. 2009;81(24):10038-48. doi: 10.1021/ac9019522
20. Samaraweera MA et al. Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics. Journal of Analytical Chemistry. 2018 Nov 6; 90(21): 12752-12760. doi: 10.1021/acs.analchem.8b03118.
21. Pradeep Sahu et al. Biomarkers: An Emerging Tool for Diagnosis of a Disease and Drug Development. Asian Journal of Research and Pharmaceutical Sciences. 2011; 1(1): 9-16.
22. Kellogg JJ, Kvalheim OM, Cech NB. Composite score analysis for unsupervised comparison and network visualization of metabolomics data. Journal of Analytica Chemica Acta. 2020; 1095: 38-47. doi: 10.1016/j.aca.2019.10.029.
23. Sengupta A, Uppoor A, Joshi MB. Metabolomics: Paving the path for personalized periodontics - A literature review. Journal of Indian Society of Periodontology. 2022; 26(2): 98-103. doi: 10.4103/jisp.jisp_267_21.
24. Rai B et al. Biomarkers of periodontitis in oral fluids. Journal of Oral Sciences. 2008; 50(1): 53-6. doi: 10.2334/josnusd.50.53.
25. Liu J et al. Metabolomics of oxidative stress in recent studies of endogenous and exogenously administered intermediate metabolites. International Journal of Molecular Sciences. 2011; 12(10): 6469-501. doi: 10.3390/ijms12106469.
26. Yang PL. Metabolomics and Lipidomics: Yet More Ways Your Health Is Influenced by Fat. Viral Pathogenesis. 2016: 181–98. doi: 10.1016/B978-0-12-800964-2.00014-8.
Received on 05.12.2022 Modified on 07.06.2023
Accepted on 01.11.2023 © RJPT All right reserved
Research J. Pharm. and Tech 2023; 16(11):5439-5443.
DOI: 10.52711/0974-360X.2023.00881