Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies.

Background: Zika virus (ZIKV) has recently become a major matter of concern since its association with microcephaly cases in newborns was determined. From then on, ZIKV has been untiringly studied, and several scientific data have shown the virus' tropism to neural cells. Previous studies have proposed that ZIKV induced glioblastoma cells death, suggesting that ZIKV and ZIKV-associated molecules might induce intracellular biochemical alterations, and thereby be alternatives for neural cancer management. In this sense, the present contribution presents a prospective approach for glioblastomas management: producing a ZIKV-based therapy that stimulates glioblastoma control. We developed an attenuated ZIKV prototype based on bacterial outer membrane vesicles (OMV). The attenuated ZIKV was applied on glioblastoma cells, where cytopathic and cytostatic effects were evaluated. Glioblastoma cells, with and without treatment, were submitted to mass spectrometry analysis. Results: Microscopic analysis showed cytopathic effects induced by ZIKV prototype on glioblastoma cells after 24 and 48 h post-treatment. DNA fragmentation assay and TNF-alpha expression were indicative that ZVp induced cell damage and death. The metabolomics investigation elected 5 different biomarkers that might be associated with cell cytopathic effects, highlighting intracellular biochemical modifications induced by the attenuated ZIKV. The remarkable evidence of cell death in glioblastoma stimulated further studies that rendered a preliminary screening with other tumor cell lineages, though anti-proliferative activity test. Among the 11 tumors evaluated, prostate and ovarian tumors were the most affected by ZIKV prototype, and other 6 cell lines also presented cytostatic effects. Conclusions: Results ultimately demonstrated that this prototype not only emerges as a potential alternative for glioblastoma management, but may also be an important tool against other important tumors.

Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients’ blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients’ condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.

The recent outbreak of Zika virus (ZIKV) infection associated with microcephaly cases has elicited much research on the mechanisms involved in ZIKV-host cell interactions. It has been described that Zika virus impairs cell growth, raising a hypothesis about its oncolytic potential against cancer cells. ZIKV tumor cell growth inhibition was later confirmed for glioblastoma. It was also demonstrated that an inactivated ZIKV prototype (ZVp) based on bacterial outer membrane vesicles has antiproliferative activity upon other cancer cell lines, such as PC-3 prostate cancer cell. This study aims at understanding the pathways that might be involved with the antiproliferative effect of Zika virus against prostate cancer cells. A metabolomic approach based on high-resolution mass spectrometry analysis led to the identification of 21 statistically relevant markers of PC-3 cells treated with ZVp. The markers were associated with metabolic alterations that trigger lipid remodeling, endoplasmic reticulum stress, inflammatory mediators, as well as disrupted porphyrin and folate metabolism. These findings highlight molecular signatures of ZVp-induced response that may be involved on cellular pathways triggered by its antiproliferative effect. To our knowledge, this is the first reported metabolomic assessment of ZIKV effect on prostate cancer cells, a promising topic for further research.

Os micro-organismos endofíticos são fungos e bactérias encontrados no interior de tecidos e órgãos saudáveis das plantas e que não causam danos aparentes aos hospedeiros durante o ciclo vital in vivo. Os fungos endofíticos são capazes de produzir substâncias de interesse biotecnológico, sendo que algumas enzimas possuem aplicações em processos industriais. A espécie Annona crassiflora, conhecida popularmente como marolo ou pinha do cerrado, é uma árvore nativa e exclusiva do cerrado brasileiro, amplamente difundido em todo o bioma. É encontrada e abrangem os estados de São Paulo, Minas Gerais, Bahia, Mato Grosso do Sul, Mato Grosso e Tocantins. Nesse contexto, o presente trabalho avaliou o potencial biocatalítico de enzimas produzidas por fungos endofíticos isolados de Annona crassiflora aplicadas em reações de biotransformação.

Infertility is a worldwide concern, affecting one in six couples throughout their reproductive period. Therefore, enhancing the clinical tools available to identify the causes of infertility may save time, money, and emotional distress for the involved parties. This study aims to annotate potential biomarkers in follicular fluid that are negatively affecting pregnancy outcomes in women suffering infertility-related diseases such as endometriosis, tuboperitoneal factor, uterine factor, and unexplained infertility, using a metabolomics approach through high-resolution mass spectrometry. Follicular fluid samples collected from women who have the abovementioned diseases and managed to become pregnant after in vitro fertilization procedures [control group (CT)] were metabolically compared with those from women who suffer from the same diseases and could not get pregnant after the same treatment [infertile group (IF)]. Mass spectrometry analysis indicated 10 statistically relevant differential metabolites in the IF group, including phosphatidic acids, phosphatidylethanolamines, phosphatidylcholines, phosphatidylinositol, glucosylceramides, and 1-hydroxyvitamin D3 3-d-glucopyranoside. These metabolites are associated with cell signaling, cell proliferation, inflammation, oncogenesis, and apoptosis, and linked to infertility problems. Our results indicate that understanding the IF’s metabolic profile may result in a faster and more assertive female infertility diagnosis, lowering the costs, and increasing the probability of a positive pregnancy outcome.

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