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Omics Sciences for Salivary Diagnostics—2nd Edition
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Omics Sciences for Salivary Diagnostics—2nd Edition

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 1517

Special Issue Editors

Dr. Barbara Manconi

E-Mail Website
Guest Editor
Dipartimento di Scienze della Vita e dell’Ambiente, Università di Cagliari, Cagliari, Italy
Interests: proteomics; mass spectrometry; biomarker discovery; PTMs characterization; saliva
Special Issues, Collections and Topics in MDPI journals
Dr. Tiziana Cabras

E-Mail Website
Guest Editor
Biomedical Section–Proteomics Unit, Department of Life and Environmental Sciences, Cagliari State University, Cagliari, Italy
Interests: proteomics; biomarker discovery; mass spectrometry; post-translational modifications; salivary proteomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, saliva has become a biological matrix with huge potential beyond its applications in oral health. It is an incredibly complex biofluid which plays a critical role in normal human physiology and pathology. There has been long-standing interest in saliva for its uses in non-invasive biomarker discovery, human health monitoring, human microbiome analysis, and (more recently) the disease surveillance of the SARS-CoV-2 pandemic. Saliva omics is a field with broad profiling that includes, but is not limited to, proteomics and metabolomics, transcriptomics, extracellular vesicle profiling, miRNAs, salivary microbiomes, and meta-proteomics.

This Special Issue plans to offer an overview of recent advances in omics technologies in diverse areas of salivary biomedical research, with a special emphasis on proteomics-based biomarker discovery. Since the analysis of proteoforms, as biochemical indicators of health or disease, has a longstanding history in biomedical research, descriptions of post-translational modifications of salivary proteins are also welcome.

For this Special Issue, we invite you and your colleagues to submit an original research article or comprehensive review article relating to this field.

Dr. Barbara Manconi
Dr. Tiziana Cabras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • saliva
  • proteomics
  • metabolomics
  • metaproteomics
  • microbiomics
  • transcriptomics
  • mass spectrometry
  • biomarkers
  • PTMs

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Related Special Issue

Published Papers (2 papers)

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Research

11 pages, 1379 KiB  
Communication
Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta
by Felipe Morando Avelar, Célia Regina Moreira Lanza, Sttephany Silva Bernardino, Marcelo Augusto Garcia-Junior, Mario Machado Martins, Murillo Guimarães Carneiro, Vasco Ariston Carvalho de Azevedo and Robinson Sabino-Silva
Int. J. Mol. Sci. 2024, 25(17), 9464; https://doi.org/10.3390/ijms25179464 - 30 Aug 2024
Viewed by 524
Abstract
Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. [...] Read more.
Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm−1, 1013 cm−1, 1002 cm−1, 1004 cm−1, and 1011 cm−1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool. Full article
(This article belongs to the Special Issue Omics Sciences for Salivary Diagnostics—2nd Edition)
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14 pages, 1202 KiB  
Article
Comparison of Peptidomes Extracted from Healthy Tissue and Tumor Tissue of the Parotid Glands and Saliva Samples
by Michał Puchalski, Dmitry Tretiakow, Andrzej Skorek, Konrad Szydłowski, Dominik Stodulski, Bogusław Mikaszewski, Amadeusz Odroniec, Natalia Musiał, Marcel Thiel, Paulina Czaplewska and Stanisław Ołdziej
Int. J. Mol. Sci. 2024, 25(16), 8799; https://doi.org/10.3390/ijms25168799 - 13 Aug 2024
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Abstract
Salivary gland tumors are highly variable in clinical presentation and histology. The World Health Organization (WHO) classifies 22 types of malignant and 11 types of benign tumors of the salivary glands. Diagnosis of salivary gland tumors is based on imaging (ultrasound, magnetic resonance [...] Read more.
Salivary gland tumors are highly variable in clinical presentation and histology. The World Health Organization (WHO) classifies 22 types of malignant and 11 types of benign tumors of the salivary glands. Diagnosis of salivary gland tumors is based on imaging (ultrasound, magnetic resonance imaging) and fine-needle aspiration biopsy, but the final diagnosis is based on histopathological examination of the removed tumor tissue. In this pilot study, we are testing a new approach to identifying peptide biomarkers in saliva that can be used to diagnose salivary gland tumors. The research material for the peptidomic studies was extracts from washings of neoplastic tissues and healthy tissues (control samples). At the same time, saliva samples from patients and healthy individuals were analyzed. The comparison of the peptidome composition of tissue extracts and saliva samples may allow the identification of potential peptide markers of salivary gland tumors in patients’ saliva. The peptidome compositions extracted from 18 tumor and 18 healthy tissue samples, patients’ saliva samples (11 samples), and healthy saliva samples (8 samples) were analyzed by LC-MS tandem mass spectrometry. A group of 109 peptides was identified that were present only in the tumor tissue extracts and in the patients’ saliva samples. Some of the identified peptides were derived from proteins previously suggested as potential biomarkers of salivary gland tumors (ANXA1, BPIFA2, FGB, GAPDH, HSPB1, IGHG1, VIM) or tumors of other tissues or organs (SERPINA1, APOA2, CSTB, GSTP1, S100A8, S100A9, TPI1). Unfortunately, none of the identified peptides were present in all samples analyzed. This may be due to the high heterogeneity of this type of cancer. The surprising result was that extracts from tumor tissue did not contain peptides derived from salivary gland-specific proteins (STATH, SMR3B, HTN1, HTN3). These results could suggest that the developing tumor suppresses the production of proteins that are essential components of saliva. Full article
(This article belongs to the Special Issue Omics Sciences for Salivary Diagnostics—2nd Edition)
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