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 athttp://bioinformatics.ua.pt/software/oralint.

Biobank saliva sample quality depends on specific criteria applied to collection, processing, and storage. In spite of the growing interest in saliva as a diagnostic fluid, few biobanks currently store large collections of such samples. The development of a standard operating procedure (SOP) for saliva collection and quality control is fundamental for the establishment of a new saliva biobank, which stores samples to be made available to the saliva research community. Different collection methods were tested regarding total volume of protein obtained, protein content, and protein profiles, and the results were used to choose the best method for protein studies. Furthermore, the impact of the circadian variability and inter- and intraindividual differences, as well as the saliva sample stability at room temperature, were also evaluated. Considering our results, a sublingual cotton roll method for saliva collection proved to produce saliva with the best characteristics and should be applied in the morning, whenever possible. In addition, there is more variability in salivary proteins between individuals than in the same individual for a 5-month period. According to the electrophoretic protein profile, protein stability is guaranteed for 24 hours at room temperature and the protein degradation profile and protein identification were characterized. All this information was used to establish an SOP for saliva collection, processing, and storage in a biobank. We conclude that it is possible to collect saliva using an easy and inexpensive protocol, resulting in saliva samples for protein analysis with sufficient quality for biobanking purposes.

SARS-CoV-2 pandemic has forced frequent testing of populations. It is necessary to identify the most cost-effective strategies for the detection of COVID-19 outbreaks. Nasopharyngeal samples have been used for SARS-CoV-2 detection but require a healthcare professional to collect the sample and cause discomfort and pain to the individual. Saliva has been suggested as an appropriate fluid for the diagnosis of COVID-19. We have investigated the possibility of using pools of saliva samples to detect SARS-CoV-2 in symptomatic and asymptomatic patients. Two hundred and seventy-nine saliva samples were analyzed through RT-PCR of Envelope, Nucleocapsid and Open Reading Frame 1ab genes. Reproducibility assays showed an almost perfect agreement as well as high sensitivity (96.6%), specificity (96.8%), positive predicted value (96.6%), and negative predicted value (96.8%). The average Cycle Threshold of the genes detected was 29.7. No significant differences (p > 0.05) were detected when comparing the cycle threshold average of two consecutive reactions on the same positive saliva samples. Saliva samples have a higher median viral load (32.6) than in nasopharyngeal samples (28.9), although no significant differences were detected (p > 0.05). Saliva-pool samples allowed effective SARS-CoV-2 screening, with a higher sensibility (96.9%) on 10-sample pools than in 20-sample pools (87.5%). Regardless of pools size specificity was high (99.9%) and an almost perfect agreement was observed. Our strategy was successfully applied in population wide testing of more than 2000 individuals, showing that it is possible to use pooled saliva as diagnostic fluid for SARS-CoV-2 infection.