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- SalivaPRINT Toolkit – protein profile evaluation and phenotype stratificationPublication . Cruz, Igor; Esteves, Eduardo; Fernandes, Mónica; Rosa, Nuno; Correia, Maria José; Arrais, Joel P.; Barros, MarleneThe value of the molecular information obtained from saliva is dependent on the use of in vitro and in silico techniques. The main proteins of saliva when separated by capillary electrophoresis enable the establishment of individual profiles with characteristic patterns reflecting each individual phenotype. Different physiological or pathological conditions may be identified by specific protein profiles. The association of each profile to the particular protein composition provides clues as to which biological processes are compromised in each situation. Patient stratification according to different phenotypes often within a particular disease spectrum is especially important for the management of individuals carrying multiple diseases and requiring personalized interventions. In this work we present the SalivaPRINT Toolkit, which enables the analysis of protein profile patterns and patient phenotyping. Additionally, the SalivaPRINT Toolkit allows the identification of molecular weight ranges altered in a particular condition and therefore potentially involved in the underlying dysregulated mechanisms. This tutorial introduces the use of the SalivaPRINT Toolkit command line interface (https://github.com/salivatec/SalivaPRINT) as an independent tool for electrophoretic protein profile evaluation. It provides a detailed overview of its functionalities, illustrated by the application to the analysis of profiles obtained from a healthy population versus a population affected with inflammatory conditions. Biological significance We present SalivaPRINT, which serves as a patient characterization tool to identify molecular weights related with particular conditions and, from there, find proteins, which may be involved in the underlying dysregulated cellular mechanisms. The proposed analysis strategy has the potential to boost personalized diagnosis. To our knowledge this is the first independent tool for electrophoretic protein profile evaluation and is crucial when a large number of complex electrophoretic profiles needs to be compared and classified.
- The landscape of protein biomarkers proposed for periodontal disease: markers with functional meaningPublication . Rosa, Nuno; Correia, Maria José; Arrais, Joel P.; Costa, Nuno; Oliveira, José Luís; Barros, MarlenePeriodontal disease (PD) is characterized by a deregulated inflammatory response which fails to resolve, activating bone resorption. The identification of the proteomes associated with PD has fuelled biomarker proposals; nevertheless, many questions remain. Biomarker selection should favour molecules representing an event which occurs throughout the disease progress. The analysis of proteome results and the information available for each protein, including its functional role, was accomplished using the OralOme database. The integrated analysis of this information ascertains if the suggested proteins reflect the cell and/or molecular mechanisms underlying the different forms of periodontal disease. The evaluation of the proteins present/absent or with very different concentrations in the proteome of each disease state was used for the identification of the mechanisms shared by different PD variants or specific to such state. The information presented is relevant for the adequate design of biomarker panels for PD. Furthermore, it will open new perspectives and help envisage future studies targeted to unveil the functional role of specific proteins and help clarify the deregulation process in the PD inflammatory response.
- CanisOme - the protein signatures of Canis lupus familiaris diseasesPublication . Fernandes, Mónica; Rosa, Nuno; Esteves, Eduardo; Correia, Maria José; Arrais, Joel; Ribeiro, Paulo; Vala, Helena; Barros, MarleneAlthough the applications of Proteomics in Human Biomedicine have been explored for some time now, in animal and veterinary research, the potential of this resource has just started to be explored, especially when companion animal health is considered. In the last years, knowledge on the Canis lupus familiaris proteome has been accumulating in the literature and a resource compiling all this information and critically reviewing it was lacking. This article presents such a resource for the first time. CanisOme is a database of all proteins identified in Canis lupus familiaris tissues, either in health or in disease, annotated with information on the proteins present on the sample and on the donors. This database reunites information on 549 proteins, associated with 63 dog diseases and 33 dog breeds. Examples of how this information may be used to produce new hypothesis on disease mechanisms is presented both through the functional analysis of the proteins quantified in canine cutaneous mast cell tumors and through the study of the interactome of C. lupus familiaris and Leishmania infantum. Therefore, the usefulness of CanisOme for researchers looking for protein biomarkers in dogs and interested in a comprehensive analysis of disease mechanisms is demonstrated. Biological significance: This paper presents CanisOme, a database of proteomic studies with relevant protein annotation, allowing the enlightenment of disease mechanisms and the discovery of novel disease biomarkers for C. lupus familiaris. This knowledge is important not only for the improvement of animal health but also for the use of dogs as models for human health studies.
- Computational prediction of the human-microbial oral interactomePublication . Coelho, Edgar D.; Arrais, Joel P.; Matos, Sérgio; Pereira, Carlos; Rosa, Nuno; Correia, Maria J.; Barros, Marlene; Oliveira, José L.Background: The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.Results: We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10-7), leading to a set of 46,579 PPIs to be further explored.Conclusions: We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint.