Fábio Trindade

Uncovering the molecular networks in periodontitis

Periodontitis is a complex immune‐inflammatory disease that results from a preestablished infection in gingiva, mainly due to Gram‐negative bacteria that colonize deeper in gingival sulcus and latter periodontal pocket. Host inflammatory and immune responses have both protective and destructive roles. Although cytokines, prostaglandins, and proteases struggle against microbial burden, these molecules promote connective tissue loss and alveolar bone resorption, leading to several histopathological changes, namely destruction of periodontal ligament, deepening of periodontal pocket, and bone loss, which can converge to attain tooth loss. Despite the efforts of genomics, transcriptomics, proteomics/peptidomics, and metabolomics, there is no available biomarker for periodontitis diagnosis, prognosis, and treatment evaluation, which could assist on the established clinical evaluation. Nevertheless, some genes, transcripts, proteins and metabolites have already shown a different expression in healthy subjects and in patients. Though, so far, ‘omics approaches only disclosed the host inflammatory response as a consequence of microbial invasion in periodontitis and the diagnosis in periodontitis still relies on clinical parameters, thus a molecular tool for assessing periodontitis lacks in current dental medicine paradigm. Saliva and gingival crevicular fluid have been attracting researchers due to their diagnostic potential, ease, and noninvasive nature of collection. Each one of these fluids has some advantages and disadvantages that are discussed in this review.

Cross-species comparison of mammalian saliva using an LC-MALDI based proteomic approach

Despite the importance of saliva in the regulation of oral cavity homeostasis, few studies have been conducted to quantitatively compare the saliva of different mammal species. Aiming to define a proteome signature of mammals’ saliva, an in‐depth SDS‐PAGE–LC coupled to MS/MS (GeLC–MS/MS) approach was used to characterize the saliva from primates (human), carnivores (dog), glires (rat and rabbit), and ungulates (sheep, cattle, horse). Despite the high variability in the number of distinct proteins identified per species, most protein families were shared by the mammals studied with the exception of cattle and horse. Alpha‐amylase is an example that seems to reflect the natural selection related to digestion efficacy and food recognition. Casein protein family was identified in all species but human, suggesting an alternative to statherin in the protection of hard tissues. Overall, data suggest that different proteins might assure a similar role in the regulation of oral cavity homeostasis, potentially explaining the specific mammals’ salivary proteome signature. Moreover, some protein families were identified for the first time in the saliva of some species, the presence of proline‐rich proteins in rabbits saliva being a good example.

Toward the definition of a peptidome signature and protease profile in chronic periodontitis

Chronic periodontitis (CP) is a complex immuno‐inflammatory disease that results from preestablished gingivitis. We investigated potential differences in salivary peptidome in health and CP.

endoProteoFASP: A novel FASP approach to profile salivary peptidome and disclose salivary proteases

The salivary peptidome, which can represent up to 20% of total secreted proteins in human saliva, is highly influenced by proteolytic events. However, the development of strategies to understand the dynamics underlying the generation of salivary peptides has been a challenging task. In order to disclose in more detail the proteolytic events taking place in saliva, we aimed to characterize salivary peptidome and predict salivary proteases by applying, for the first time, a filter-aided sample preparation (FASP) approach to saliva. Thus, as a proof-of-concept of this application, harvested saliva samples from healthy individuals were incubated in 30 kDa cut-off spin filters for 18 or 115 h, at 37 °C, to promote saliva autolysis and the attained peptidome was characterized and compared with the naturally occurring one. In ex vivo conditions, proline-rich proteins, P-B peptide, histatin 1 and statherin were found to be the most susceptible salivary proteins to proteolysis. Peptide fragments were mainly attributed to the activity of cathepsin L1 and K at 18 h, whereas at 115 h, the attained peptide fragments were attributed to the activity of cathepsins K and L1, and MEP1A. Overall, the described endoProteoFASP approach makes the most of saliva׳s own protease pool and avoids the use of synthetic peptides and exogenous proteases to understand the proteolytic events occurring in the oral fluid. Hence, it could be very helpful in future studies targeting the characterization of salivary proteases and peptidome from different pathophysiological conditions.

Human Antimicrobial Peptides in Bodily Fluids: Current Knowledge and Therapeutic Perspectives in the Postantibiotic Era

Antimicrobial peptides (AMPs) are an integral part of the innate immune defense mechanism of many organisms. Due to the alarming increase of resistance to antimicrobial therapeutics, a growing interest in alternative antimicrobial agents has led to the exploitation of AMPs, both synthetic and isolated from natural sources. Thus, many peptide‐based drugs have been the focus of increasing attention by many researchers not only in identifying novel AMPs, but in defining mechanisms of antimicrobial peptide activity as well. Herein, we review the available strategies for the identification of AMPs in human body fluids and their mechanism(s) of action. In addition, an overview of the distribution of AMPs across different human body fluids is provided, as well as its relation with microorganisms and infectious conditions.

Towards the standardization of stem cell therapy studies for ischemic heart diseases: Bridging the gap between animal models and the clinical setting

Today there is an increasing demand for heart transplantations for patients diagnosed with heart failure. Though, shortage of donors as well as the large number of ineligible patients hurdle such treatment option. This, in addition to the considerable number of transplant rejections, has driven the clinical research towards the field of regenerative medicine. Nonetheless, to date, several stem cell therapies tested in animal models fall by the wayside and when they meet the criteria to clinical trials, subjects often exhibit modest improvements. A main issue slowing down the admission of such therapies in the domain of human trials is the lack of protocol standardization between research groups, which hampers comparison between different approaches as well as the lack of thought regarding the clinical translation. In this sense, given the large amount of reports on stem cell therapy studies in animal models reported in the last 3years, we sought to evaluate their advantages and limitations towards the clinical setting and provide some suggestions for the forthcoming investigations. We expect, with this review, to start a new paradigm on regenerative medicine, by evoking the debate on how to plan novel stem cell therapy studies with animal models in order to achieve more consistent scientific production and accelerate the admission of stem cell therapies in the clinical setting.

JPEG Decoding in an Electronic Voting Machine

Since 1996 an electronic voting machine (EVM) has been in use in Brazilian elections. It is based on a PC motherboard, containing a small keypad and a LCD display which shows messages and candidates pictures. We are studying the introduction of programmable devices in the EVM in order to make it more flexible and to improve security issues. This paper presents part of this work, the implementation of a JPEG decoding algorithm in FPGA in the context of the EVM. It presents the decoding process and shows the first results obtained in the implementation of the IDCT (inverse discrete cosine transform) and Huffmann decoding.

Biofluid proteases profiling in diabetes mellitus.

The investigation of protease relevance in biologic systems beyond catabolism of proteins and peptides to amino acids has stimulated interest as to their role in the pathogenesis of several disorders including diabetes mellitus (DM). Evaluation of proteases and the assessment of their activity in biofluids are fundamental to elucidate these proteolytic systems in DM and its related complications. In contrast to traditional immunoassay or substrate based approaches that targeted specific proteases and their inhibitors, the field of degradomics has provided a comprehensive approach to study these enzymes. Although the degradome contains over 500 proteases, very few have been associated with DM and its micro- and macrovascular complications. In this paper, we review these proteases and their respective inhibitors with emphasis on DM. It is likely that future research will expand these initial studies and look to develop high throughput automated technologies to identify and characterize biofluid proteases of diagnostic and prognostic value in other pathologies.

Codon misreading tRNAs promote tumor growth in mice

ABSTRACT Deregulation of tRNAs, aminoacyl-tRNA synthetases and tRNA modifying enzymes are common in cancer, raising the hypothesis that protein synthesis efficiency and accuracy (mistranslation) are compromised in tumors. We show here that human colon tumors and xenograft tumors produced in mice by two epithelial cancer cell lines mistranslate 2- to 4-fold more frequently than normal tissue. To clarify if protein mistranslation plays a role in tumor biology, we expressed mutant Ser-tRNAs that misincorporate Ser-at-Ala (frequent error) and Ser-at-Leu (infrequent error) in NIH3T3 cells and investigated how they responded to the proteome instability generated by the amino acid misincorporations. There was high tolerance to both misreading tRNAs, but the Ser-to-Ala misreading tRNA was a more potent inducer of cell transformation, stimulated angiogenesis and produced faster growing tumors in mice than the Ser-to-Leu misincorporating tRNA. Upregulation of the Akt pathway and the UPR were also observed. Most surprisingly, the relative expression of both misreading tRNAs increased during tumor growth, suggesting that protein mistranslation is advantageous in cancer contexts. These data highlight new features of protein synthesis deregulation in tumor biology.

How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study

Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, we used a specific clinical problem - to discriminate pathophysiology and potential biomarkers between two similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) - to make a step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could find specific biological phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such as up-regulation of plasminogen activation. Moreover, through Cytoscape and DisGeNET we could pinpoint surrogate markers either for AVS (e.g. popeye domain containing protein 2 and 28S ribosomal protein S36, mitochondrial) or for CAD (e.g. ankyrin repeat and SOCS box protein 7) which deserve future validation. Data recycling and integration as well as research orientation are among the main advantages of resorting to bioinformatics analysis, hence these tutorials can be of great convenience for proteomics investigators. BIOLOGICAL SIGNIFICANCE As we saw for aortic valve stenosis and coronary artery disease, it can be of great relevance to perform preliminary bioinformatics analysis with already published proteomics data. It not only saves us time in the lab (avoiding work duplication) as it points out new hypothesis to explain the phenotypical presentation of the diseases as well as new surrogate markers with clinical relevance, deserving future scrutiny. These essential steps can be easily overcome if one follows the steps proposed in our tutorial for STRING, DisGeNET, Cytoscape and ClueGO utilization.