Pathophysiology of chikungunya virus infection associated with fatal outcomes

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes acute, subacute, and chronic human arthritogenic diseases and, in rare instances, can lead to neurological complications and death. Here, we combined epidemiological, virological, histopathological, cytokine, molecular dynamics, metabolomic, proteomic, and genomic analyses to investigate viral and host factors that contribute to chikungunya-associated (CHIK) death. Our results indicate that CHIK deaths are associated with multi-organ infection, central nervous system damage, and elevated serum levels of pro-inflammatory cytokines and chemokines compared with survivors. The histopathologic, metabolite, and proteomic signatures of CHIK deaths reveal hemodynamic disorders and dysregulated immune responses. The CHIKV East-Central-South-African lineage infecting our study population causes both fatal and survival cases. Additionally, CHIKV infection impairs the integrity of the blood-brain barrier, as evidenced by an increase in permeability and altered tight junction protein expression. Overall, our findings improve the understanding of CHIK pathophysiology and the causes of fatal infections.

Antioxidant, antimicrobial and healing properties of an extract from coffee pulp for the development of a phytocosmetic

Consumer demand for natural, chemical-free products has grown. Food industry residues, like coffee pulp, rich in caffeine, chlorogenic acid and phenolic compounds, offer potential for pharmaceutical and cosmetic applications due to their antioxidant, anti-inflammatory, and antibacterial properties. Therefore, the objective of this work was to develop a phytocosmetic only with natural products containing coffee pulp extract as active pharmaceutical ingredient with antioxidant, antimicrobial and healing activity. Eight samples from Coffea arabica and Coffea canephora Pierre were analyzed for caffeine, chlorogenic acid, phenolic compounds, tannins, flavonoids, cytotoxicity, antibacterial activity, and healing potential. The Robusta IAC-extract had the greatest prominence with 192.92 μg/mL of chlorogenic acid, 58.98 ± 2.88 mg GAE/g sample in the FRAP test, 79.53 ± 5.61 mg GAE/g sample in the test of total phenolics, was not cytotoxic, and MIC 3 mg/mL against Staphylococcus aureus. This extract was incorporated into a stable formulation and preferred by 88% of volunteers. At last, a scratch assay exhibited the formulation promoted cell migration after 24 h, therefore, increased scratch retraction. In this way, it was possible to develop a phytocosmetic with the coffee pulp that showed desirable antioxidant, antimicrobial and healing properties.

Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment

As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions. KEY MESSAGES: Skin imprints complement plasma in disease metabolomics. The annotated markers have a role in immunomodulation and metabolic diseases. Skin imprints outperformed plasma samples at assessing disease severity. Skin imprints have potential as non-invasive sampling strategy for COVID-19.

Metabolomics in chronic hepatitis C: Decoding fibrosis grading and underlying pathways

BACKGROUND

Chronic Hepatitis C (CHC) affects 71 million people globally and leads to liver issues such as fibrosis, cirrhosis, cancer, and death. A better understanding and prognosis of liver involvement are vital to reduce morbidity and mortality. The accurate identification of the fibrosis stage is crucial for making treatment decisions and predicting outcomes. Tests used to grade fibrosis include histological analysis and imaging but have limitations. Blood markers such as molecular biomarkers can offer valuable insights into fibrosis.

AIM

To identify potential biomarkers that might stratify these lesions and add information about the molecular mechanisms involved in the disease.

METHODS

Plasma samples were collected from 46 patients with hepatitis C and classified into fibrosis grades F1 (n = 13), F2 (n = 12), F3 (n = 6), and F4 (n = 15). To ensure that the identified biomarkers were exclusive to liver lesions (CHC fibrosis), healthy volunteer participants (n = 50) were also included. An untargeted metabolomic technique was used to analyze the plasma metabolites using mass spectrometry and database verification. Statistical analyses were performed to identify differential biomarkers among groups.

RESULTS

Six differential metabolites were identified in each grade of fibrosis. This six-metabolite profile was able to establish a clustering tendency in patients with the same grade of fibrosis; thus, they showed greater efficiency in discriminating grades.

CONCLUSION

This study suggests that some of the observed biomarkers, once validated, have the potential to be applied as prognostic biomarkers. Furthermore, it suggests that liquid biopsy analyses of plasma metabolites are a good source of molecular biomarkers capable of stratifying patients with CHC according to fibrosis grade.

Metabolomics Approach Reveals Important Glioblastoma Plasma Biomarkers for Tumor Biology

Glioblastoma (GB) is the most aggressive and frequent primary malignant tumor of the central nervous system and is associated with poor overall survival even after treatment. To better understand tumor biochemical alterations and broaden the potential targets of GB, this study aimed to evaluate differential plasma biomarkers between GB patients and healthy individuals using metabolomics analysis. Plasma samples from both groups were analyzed via untargeted metabolomics using direct injection with an electrospray ionization source and an LTQ mass spectrometer. GB biomarkers were selected via Partial Least Squares Discriminant and Fold-Change analyses and were identified using tandem mass spectrometry with in silico fragmentation, consultation of metabolomics databases, and a literature search. Seven GB biomarkers were identified, some of which were unprecedented biomarkers for GB, including arginylproline (m/z 294), 5-hydroxymethyluracil (m/z 143), and N-acylphosphatidylethanolamine (m/z 982). Notably, four other metabolites were identified. The roles of all seven metabolites in epigenetic modulation, energy metabolism, protein catabolism or folding processes, and signaling pathways that activate cell proliferation and invasion were elucidated. Overall, the findings of this study highlight new molecular targets to guide future investigations on GB. These molecular targets can also be further evaluated to derive their potential as biomedical analytical tools for peripheral blood samples.

Exogenous succinate impacts mouse brown adipose tissue mitochondrial proteome and potentiates body mass reduction induced by liraglutide

Obesity is one of the leading noncommunicable diseases in the world. Despite intense efforts to develop strategies to prevent and treat obesity, its prevalence continues to rise worldwide. A recent study has shown that the tricarboxylic acid intermediate succinate increases body energy expenditure by promoting brown adipose tissue thermogenesis through the activation of uncoupling protein-1; this has generated interest surrounding its potential usefulness as an approach to treat obesity. It is currently unknown how succinate impacts brown adipose tissue protein expression, and how exogenous succinate impacts body mass reduction promoted by a drug approved to treat human obesity, the glucagon-like-1 receptor agonist, liraglutide. In the first part of this study, we used bottom-up shotgun proteomics to determine the acute impact of exogenous succinate on the brown adipose tissue. We show that succinate rapidly affects the expression of 177 brown adipose tissue proteins, which are mostly associated with mitochondrial structure and function. In the second part of this study, we performed a short-term preclinical pharmacological intervention, treating diet-induced obese mice with a combination of exogenous succinate and liraglutide. We show that the combination was more efficient than liraglutide alone in promoting body mass reduction, food energy efficiency reduction, food intake reduction, and an increase in body temperature. Using serum metabolomics analysis, we showed that succinate, but not liraglutide, promoted a significant increase in the blood levels of several medium and long-chain fatty acids. In conclusion, exogenous succinate promotes rapid changes in brown adipose tissue mitochondrial proteins, and when used in association with liraglutide, increases body mass reduction.NEW & NOTEWORTHY Exogenous succinate induces major changes in brown adipose tissue protein expression affecting particularly mitochondrial respiration and structural proteins. When given exogenously in drinking water, succinate mitigates body mass gain in a rodent model of diet-induced obesity; in addition, when given in association with the glucagon-like peptide-1 receptor agonist, liraglutide, succinate increases body mass reduction promoted by liraglutide alone.

Unraveling the Metabolic Alterations Induced by Zika Infection in Prostate Epithelial (PNT1a) and Adenocarcinoma (PC-3) Cell Lines

The outbreak of Zika virus infection in 2016 led to the identification of its presence in several types of biofluids, including semen. Later discoveries associated Zika infection with sexual transmission and persistent replication in cells of the male reproductive tract. Prostate epithelial and carcinoma cells are favorable to virus replication, with studies pointing to transcriptomics alterations of immune and inflammation genes upon persistence. However, metabolome alterations promoted by the Zika virus in prostate cells are unknown. Given its chronic effects and oncolytic potential, we aim to investigate the metabolic alterations induced by the Zika virus in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cells using an untargeted metabolomics approach and high-resolution mass spectrometry. PNT1a cells were viable up to 15 days post ZIKV infection, in contrast to its antiproliferative effect in the PC-3 cell lineage. Remarkable alterations in the PNT1a cell metabolism were observed upon infection, especially regarding glycerolipids, fatty acids, and acylcarnitines, which could be related to viral cellular resource exploitation, in addition to the over-time increase in oxidative stress metabolites associated with carcinogenesis. The upregulation of FA20:5 at 5 dpi in PC-3 cells corroborates the antiproliferative effect observed since this metabolite was previously reported to induce PC-3 cell death. Overall, Zika virus promotes extensive lipid alterations on both PNT1a and PC-3 cells, promoting different outcomes based on the cellular metabolic state.

Differential Plasma Metabolites between High- and Low-Grade Meningioma Cases

Meningiomas (MGMs) are currently classified into grades I, II, and III. High-grade tumors are correlated with decreased survival rates and increased recurrence rates. The current grading classification is based on histological criteria and determined only after surgical tumor sampling. This study aimed to identify plasma metabolic alterations in meningiomas of different grades, which would aid surgeons in predefining the ideal surgical strategy. Plasma samples were collected from 51 patients with meningioma and classified into low-grade (LG) (grade I; n = 43), and high-grade (HG) samples (grade II, n = 5; grade III, n = 3). An untargeted metabolomic approach was used to analyze plasma metabolites. Statistical analyses were performed to select differential biomarkers among HG and LG groups. Metabolites were identified using tandem mass spectrometry along with database verification. Five and four differential biomarkers were identified for HG and LG meningiomas, respectively. To evaluate the potential of HG MGM metabolites to differentiate between HG and LG tumors, a receiving operating characteristic curve was constructed, which revealed an area under the curve of 95.7%. This indicates that the five HG MGM metabolites represent metabolic alterations that can differentiate between LG and HG meningiomas. These metabolites may indicate tumor grade even before the appearance of histological features.

Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth

Imaging mass spectrometry (MS) is becoming increasingly applied for single-cell analyses. Multiple methods for imaging MS-based single-cell metabolomics were proposed, including our recent method SpaceM. An important step in imaging MS-based single-cell metabolomics is the assignment of MS intensities from individual pixels to single cells. In this process, referred to as pixel-cell deconvolution, the MS intensities of regions sampled by the imaging MS laser are assigned to the segmented single cells. The complexity of the contributions from multiple cells and the background, as well as lack of full understanding of how input from molecularly-heterogeneous areas translates into mass spectrometry intensities make the cell-pixel deconvolution a challenging problem. Here, we propose a novel approach to evaluate pixel-cell deconvolution methods by using a molecule detectable both by mass spectrometry and fluorescent microscopy, namely fluorescein diacetate (FDA). FDA is a cell-permeable small molecule that becomes fluorescent after internalisation in the cell and subsequent cleavage of the acetate groups. Intracellular fluorescein can be easily imaged using fluorescence microscopy. Additionally, it is detectable by matrix-assisted laser desorption/ionisation (MALDI) imaging MS. The key idea of our approach is to use the fluorescent levels of fluorescein as the ground truth to evaluate the impact of using various pixel-cell deconvolution methods onto single-cell fluorescein intensities obtained by the SpaceM method. Following this approach, we evaluated multiple pixel-cell deconvolution methods, the 'weighted average' method originally proposed in the SpaceM method as well as the novel 'linear inverse modelling' method. Despite the potential of the latter method in resolving contributions from individual cells, this method was outperformed by the weighted average approach. Using the ground truth approach, we demonstrate the extent of the ion suppression effect which considerably worsens the pixel-cell deconvolution quality. For compensating the ion suppression individually for each analyte, we propose a novel data-driven approach. We show that compensating the ion suppression effect in a single-cell metabolomics dataset of co-cultured HeLa and NIH3T3 cells considerably improved the separation between both cell types. Finally, using the same ground truth, we evaluate the impact of drop-outs in the measurements and discuss the optimal filtering parameters of SpaceM processing steps before pixel-cell deconvolution.

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.

Molecular signatures associated with diuron exposure on rat urothelial mitochondria

Diuron, 3- (3,4-dichlorophenyl)-1,1-dimethylurea, is a worldwide used herbicide whose biotransformation gives rise to the metabolites, 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU) and 3,4-dichloroaniline (DCA). Previous studies indicate that diuron and/or its metabolites are toxic to the bladder urothelium of the Wistar rats where, under certain conditions of exposure, they may induce successively urothelial cell degeneration, necrosis, hyperplasia and eventually tumors. The hypothesis was raised that the molecular initiating event (MIE) of this Adverse Outcome Pathway (AOP) is the mitochondrial toxicity of those compounds. Therefore, this study aimed to investigate in vitro the metabolic alterations resulting from urothelial mitochondria isolated from male Wistar rats exposure to diuron, DCPMU and DCA at 10 and 100 µM. A non-targeted metabolomic analysis using mass spectrometry showed discriminative clustering among groups and alterations in the intensity abundance of membrane-associated molecules phosphatidylcholine (PC), phosphatidylinositol (PI) and phosphatidylserine (PS), in addition to methylhexanoyl-CoA and, particularly for diuron 100 µM, dehydro-L-gulonate, all of them involved in critical mitochondrial metabolism. Collectively, these data indicate the mitochondrial dysfunction as a MIE that triggers cellular damage and death observed in previous studies.

Metabolic alterations in Strongyloidiasis stool samples unveil potential biomarkers of infection

Strongyloidiasis, a parasitosis caused by Strongyloides stercoralis in humans, is a very prevalent infection in tropical or subtropical areas. Gaps on public health strategies corroborates to the high global incidence of strongyloidiasis especially due to challenges involved on its diagnosis. Based on the lack of a gold-standard diagnostic tool, we aimed to present a metabolomic study for the assessment of stool metabolic alterations. Stool samples were collected from 25 patients segregated into positive for strongyloidiasis (n = 10) and negative control (n = 15) and prepared for direct injection high-resolution mass spectrometry analysis. Using metabolomics workflow, 18 metabolites were annotated increased or decreased in strongyloidiasis condition, from which a group of 5 biomarkers comprising caprylic acid, mannitol, glucose, lysophosphatidylinositol and hydroxy-dodecanoic acid demonstrated accuracy over 89% to be explored as potential markers. The observed metabolic alteration in stool samples indicates involvement of microbiota remodeling, parasite constitution, and host response during S. stercoralis infection.

Metabolic shift of chronic myeloid leukemia patients under imatinib-pioglitazone regimen and discontinuation

The Estudo de Descontinuação de Imatinibe após Pioglitazona (EDI-PIO) is a single-center, longitudinal, prospective, phase 2, non-randomized, open, clinical trial (NCT02852486, August 2, 2016 retrospectively registered) for the discontinuation of imatinib after concomitant use of pioglitazone, being the first of its kind in a Brazilian population with chronic myeloid leukemia. Due to remaining of leukemic quiescent cells that are not affected by tyrosine kinase inhibitors, it has been suggested the use of pioglitazone, a PPARγ agonist, together with imatinib as a strategy for the maintenance of deep molecular response. The clinical benefit to this association is still controversial, and the metabolic alteration along this process remains unclear. Therefore, we applied a metabolomic protocol using high-resolution mass spectrometry to profile plasmatic metabolic response of a prospective cohort of ten individuals under discontinuation of imatinib and pioglitazone protocol. By comparing patients under pioglitazone and imatinib treatment with imatinib monotherapy and discontinuation phase, we were able to annotate 41 and 36 metabolites, respectively. The metabolic alterations observed during imatinib-pioglitazone combined therapy are associated with an extensive lipid remodeling, with activation of β-oxidation pathway, in addition to the presence of markers that suggest mitochondrial dysfunction.

Evaluation of antioxidant capacity, fatty acid profile, and bioactive compounds from buritirana (Mauritiella armata Mart.) oil: A little-explored native Brazilian fruit

Buritirana (Mauritiella armata Mart.) is a fruit species native to the Amazon and Cerrado region, belonging to the Arecaceae family. It has high nutritional and functional potential, yet little explored. In this study, we evaluated for the first time the overall yield, behavior of total carotenoids in the extraction kinetics, fatty acid profile, bioactive compounds, and the antioxidant capacity of the oil from buritirana fractions obtained by supercritical CO₂. The highest extraction yield was found in the pulp and whole without seed at 60 °C (18.06 ± 0.40 and 14.55 ± 1.10 g 100 g^-1 of the freeze-dried sample (fdw), respectively), and in the peel at 40 °C (8.31 ± 0.73 g 100 g^-1 fdw). During the extraction kinetics, the pulp had the highest yields of oil (41.57%) and total carotenoids (8.34 mg g^-1) after 61 min at 40 °C. The antioxidant potential, fatty acid profile, and α-tocopherol content were dependent on both fraction and temperature, with oleic acid being the main fatty acid. The oil from the whole fraction without seed had the largest number (20) of identified phenolic compounds. The extraction at 60 °C reduced the relative intensity of most compounds in the whole without seed and pulp. Moreover, it increased the intensity of the compounds in the peel. These results suggest that buritirana is a good oil source with great bioactive potential to produce new products with functional claims.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

Chemical characterization of Eugenia stipitata: A native fruit from the Amazon rich in nutrients and source of bioactive compounds

Eugenia stipitata is a fruit native to the Brazilian Amazonian region, belonging to the Myrtaceae family whose chemical composition has been little evidenced. In this study, we evaluated for the first time the nutritional composition, bioactive compounds and antioxidant properties of two fractions of this fruit. It was observed that the edible fraction had a higher content of minerals such as K, Ca and Mg (827.66 ± 14.51; 107.16 ± 1.54; and 75.65 ± 1.28 mg 100 g^-1 dw, respectively), sucrose (38.01 ± 2.94 mg g^-1 dw), fructose (17.58 ± 0.80 mg g^-1 dw), and maltotetraose (1.63 ± 0.09 mg g^-1 dw). In this same fraction, about 30 volatile compounds were found, mainly biciclo(3.2.1)octan-3-one, 6 (2-hydroxyethyl)-, endo-; butanoic acid, 2-methyl-, hexyl ester and p-ocimene. In turn, the seed had the highest number of compounds identified by ESI-LTQ-MS/MS (including vanillic acid, gallic acid hexoside, catechin hexoside, luteolin hexoside, among others), higher content of phenolics (142.43 ± 0.82 mg GAE g^-1 dw), flavonoids (43.73 ± 0.23 mg CE g^-1 dw), and antioxidant capacity (139.59 ± 2.47; 447.94 ± 2.70; and 100.07 ± 10.50 µM TE g^-1 dw for DPPH, ABTS, and ORAC, respectively). These results suggest that Eugenia stipitata has excellent nutritional value and great functional potential, and may contribute to a greater commercial exploitation of this fruit, not only in food, but also in the pharmaceutical and cosmetic industries.

Influence of high-intensity ultrasound on color, chemical composition and antioxidant properties of araçá-boi pulp

In this study, we evaluated the influence of the ultrasound application on five levels of energy density (1000; 3000; 5000 and 7000 J g^-1) compared to two pasteurization techniques (70 °C/5 min and 94 °C/0.5 min) on color parameters, polyphenoloxidase activity, chemical composition, and antioxidant properties of araçá-boi pulp. Ultrasound caused changes in the parameters brightness/darkness, hue angle, and total color difference, but did not change chroma, yellowness/blueness, color index, and yellow index. Moreover, this technique was efficient for inactivating polyphenoloxidase. Ultrasound at 7000 J g^-1 was responsible for an increase in soluble solids (16%), vitamin C (46.5%), phenolics (15.65%), flavonoids (50%) and antioxidant capacity in relation to untreated pulp, while ultrasound at 5000 J g^-1 increased the relative intensity of compounds of biological interest. Thus, ultrasound can be considered as a promising technique to maintain the shelf life, without drastically affecting the nutritional and functional qualities of this fruit.

Does leukotriene F4 play a major role in the infection mechanism of Candida sp.?

Candidiasis is the most common fungal infection affecting hospitalized patients, especially immunocompromised and critical patients. Limitations regarding the assertive diagnosis of both Candidemia and Candidiasis not only impairs the introduction of effective treatments but also lays a heavy financial burden over the health system. Furthermore, it is still challenging to ascertain whether diagnostic methods are accurate and whether treatment is effective for patients with Candidemia. These constraints come from the uncertainty of the pathophysiological mechanism by which the pathogen establishes the opportunistic infection. Additionally, it is the reason why some patients present positive blood culture results, and others do not, and why it is very difficult during clinical routines to prove Candidemia or invasive candidiasis. Taking into account the current situation, this contribution proposes two markers that may help to understand the mechanisms of infection by the pathogen: Leukotriene F4 and 5,6-dihydroxy-eicosatetraenoic. These two lipids putatively modulate the host's immune response, and the initial data presented in this contribution suggest that these lipids allow the opportunistic infection to be installed. The study was carried out using an omics-based platform using direct-infusion high-resolution mass spectrometry and allied with bioinformatics tools to provide accurate and reliable results for biomarker candidates screening.

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

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.

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.

Inflammation markers in the saliva of infants born from Zika-infected mothers: exploring potential mechanisms of microcephaly during fetal development

Zika virus (ZIKV) has emerged as one of the most medically relevant viral infections of the past decades; the devastating effects of this virus over the developing brain are a major matter of concern during pregnancy. Although the connection with congenital malformations are well documented, the mechanisms by which ZIKV reach the central nervous system (CNS) and the causes of impaired cortical growth in affected fetuses need to be better addressed. We performed a non-invasive, metabolomics-based screening of saliva from infants with congenital Zika syndrome (CZS), born from mothers that were infected with ZIKV during pregnancy. We were able to identify three biomarkers that suggest that this population suffered from an important inflammatory process; with the detection of mediators associated with glial activation, we propose that microcephaly is a product of immune response to the virus, as well as excitotoxicity mechanisms, which remain ongoing even after birth.

The role of lipids in the inception, maintenance and complications of dengue virus infection

Dengue fever is a viral condition that has become a recurrent issue for public health in tropical countries, common endemic areas. Although viral structure and composition have been widely studied, the infection phenotype in terms of small molecules remains poorly established. This contribution providing a comprehensive overview of the metabolic implications of the virus-host interaction using a lipidomic-based approach through direct-infusion high-resolution mass spectrometry. Our results provide further evidence that lipids are part of both the immune response upon Dengue virus infection and viral infection maintenance mechanism in the organism. Furthermore, the species described herein provide evidence that such lipids may be part of the mechanism that leads to blood-related complications such as hemorrhagic fever, the severe form of the disease.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

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.

Serum Metabolic Alterations upon Zika Infection

Zika virus (ZIKV) infection has recently emerged as a major concern worldwide due to its strong association with nervous system malformation (microcephaly) of fetuses in pregnant women infected by the virus. Signs and symptoms of ZIKV infection are often mistaken with other common viral infections. Since transmission may occur through biological fluids exchange and coitus, in addition to mosquito bite, this condition is an important infectious disease. Thus, understanding the mechanism of viral infection has become an important research focus, as well as providing potential targets for assertive clinical diagnosis and quality screening for hemoderivatives. Within this context, the present work analyzed blood plasma from 79 subjects, divided as a control group and a ZIKV-infected group. Samples underwent direct-infusion mass spectrometry and statistical analysis, where eight markers related to the pathophysiological process of ZIKV infection were elected and characterized. Among these, Angiotensin (1-7) and Angiotensin I were upregulated under infection, showing an attempt to induce autophagy of the infected cells. However, this finding is concerning about hypertensive individuals under treatment with inhibitors of the Renin-Angiotensin System (RAS), which could reduce this response against the virus and exacerbate the symptoms of the infection. Moreover, one of the most abundant glycosphingolipids in the nervous tissue, Ganglioside GM2, was also elected in the present study as an infection biomarker. Considered an important pathogen receptor at membrane's outer layer, this finding represents the importance of gangliosides for ZIKV infection and its association with brain tropism. Furthermore, a series of phosphatidylinositols were also identified as biomarkers, implying a significant role of the PI3K-AKT-mTOR Pathway in this mechanism. Finally, these pathways may also be understood as potential targets to be considered in pharmacological intervention studies on ZIKV infection management.

Skin Biomarkers for Cystic Fibrosis: A Potential Non-Invasive Approach for Patient Screening

BACKGROUND

Cystic fibrosis (CF) is a disabling genetic disease with an increased prevalence in European heritage populations. Currently, the most used technique for collection of CF samples and diagnosis is provided through uncomfortable tests, with uncertain results, mostly based on chloride concentration in sweat. Since CF mutation induces many metabolic changes in patients, exploring these alterations might be an alternative to visualize potential biomarkers that could be used as interesting tools for further diagnostic upgrade, prioritizing simplicity, low cost, and quickness.

METHODS

This contribution describes an accurate strategy to provide potential biomarkers related to CF, which may be understood as a potential tool for new diagnostic approaches and/or for monitoring disease evolution. Therefore, the present proposal consists of using skin imprints on silica plates as a way of sample collection, followed by direct-infusion high-resolution mass spectrometry and multivariate data analysis, intending to identify metabolic changes in skin composition of CF patients.

RESULTS

Metabolomics analysis allowed identifying chemical markers that can be traced back to CF in patients' skin imprints, differently from control subjects. Seven chemical markers from several molecular classes were elected, represented by bile acids, a glutaric acid derivative, thyrotropin-releasing hormone, an inflammatory mediator, a phosphatidic acid, and diacylglycerol isomers, all reflecting metabolic disturbances that occur due to of CF.

CONCLUSION

The comfortable method of sample collection combined with the identified set of biomarkers represent potential tools that open the range of possibilities to manage CF and follow the disease evolution. This exploratory approach points to new perspectives about the development of diagnostic assay using biomarkers and the management CF.

In vitro evaluation of Sun Protection Factor and stability of commercial sunscreens using mass spectrometry

Sunlight exposure causes several types of injury to humans, especially on the skin; among the most common harmful effects due to ultraviolet (UV) exposure are erythema, pigmentation and lesions in DNA, which may lead to cancer. These long-term effects are minimized with the use of sunscreens, a class of cosmetic products that contains UV filters as the main component in the formulation; such molecules can absorb, reflect or diffuse UV rays, and can be used alone or as a combination to broaden the protection on different wavelengths. Currently, worldwide regulatory agencies define which ingredients and what quantities must be used in each country, and enforce companies to conduct tests that confirm the Sun Protection Factor (SPF) and the UVA (Ultraviolet A) factor. Standard SPF determination tests are currently conducted in vivo, using human subjects. In an industrial mindset, apart from economic and ethical reasons, the introduction of an in vitro method emerges as an interesting alternative by reducing risks associated to UV exposure on tests, as well as providing assertive analytical results. The present work aims to describe a novel methodology for SPF determination directly from sunscreen formulations using the previously described cosmetomics platform and mass spectrometry as the analytical methods of choice.