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- An analysis of protein patterns present in the saliva of diabetic patients using pairwise relationship and hierarchical clusteringPublication . Soares, Airton; Esteves, Eduardo; Rosa, Nuno; Esteves, Ana Cristina; Lins, Anthony; Bastos-Filho, Carmelo J. A.Molecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. The data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals’ health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.
- A protein profiling strategy for periodontal disease applications: the Perio-SalivaPRINTPublication . Rosa, Nuno; Esteves, Eduardo; Esteves, Ana Cristina; Fernandes, Gustavo; Correia, Maria; Siqueira, Walter L.; Barros, MarleneObjectives: It is known that several clinical situations have characteristic molecular deregulations. Some molecular data underlying these deregulations can be found in saliva and have been annotated in databases (SalivaTecDB). Strategies are needed to identify the phenotypes characteristic of these deregulations. Our group has developed a strategy that allows the establishment of saliva protein profiles reflecting different conditions (health and disease). These profiles can be integrated to clinical data (SalivaPRINT Toolkit). The present work aims to identify the Periodontal Diseases (PD)-specific protein profiles. Methods: Unstimulated whole saliva was collected from a group of healthy subjects and a group of PD patients (with gingivitis, periodontitis or periimplantitis). Salivary proteins were separated by the Experion™ automated capillary electrophoresis. The protein profiles of each condition were integrated with the corresponding protein data retrieved from our in-house database (SalivaTecDB). Results: The strategy used enabled the determination of a total protein profile from saliva characteristic of each PDs -the Perio-SalivaPrint. The use of the SalivaPrint Toolkit allowed the identification of molecular weight ranges altered in PD. Using SalivaTecDB we were able to suggest proteins potentially involved in the underlying dysregulated mechanisms of the disease. Conclusions: This approach enabled the determination of a Perio-SalivaPrint – protein profiles specific for gingivitis, periodontitis or periimplantitis - that could empower the use of saliva as a simple and less expensive diagnostic and monitoring fluid. The strategy presented could be an important tool for future applications in the early diagnostic/ screening of Periodontal Disease patients with applications in chairside monitoring.