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Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1 H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

Sea salt is a handmade food product that is obtained by evaporation of seawater in saltpans. During the crystallisation process, organic compounds from surroundings can be incorporated into sea salt crystals. The aim of this study is to search for potential volatile markers of sea salt. Thus, sea salts from seven north-east Atlantic Ocean locations (France, Portugal, Continental Spain, Canary Islands, and Cape Verde) were analysed by headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. A total of 165 compounds were detected, ranging from 32 to 71 compounds per salt. The volatile composition revealed the variability and individuality of each salt, and a set of ten compounds were detected in all samples. From these, seven are carotenoid-derived compounds that can be associated with the typical natural surroundings of ocean hypersaline environment. These ten compounds are proposed as potential volatile markers of sea salt., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The associations of plasma metabolites with adverse cardiovascular (CV) outcomes are still underexplored and may be useful in CV risk stratification. We performed a systematic review and meta-analysis to establish correlations between blood metabolites and adverse CV outcomes in patients with heart failure (HF). Four cohorts were included, involving 83 metabolites and 37 metabolite ratios, measured in 1158 HF patients. Hazard ratios (HR) of 42 metabolites and 3 metabolite ratios, present in at least two studies, were combined through meta-analysis. Higher levels of histidine (HR 0.74, 95% CI [0.64; 0.86]) and tryptophan (HR 0.82 [0.71; 0.96]) seemed protective, whereas higher levels of symmetric dimethylarginine (SDMA) (HR 1.58 [1.30; 1.93]), N-methyl-1-histidine (HR 1.56 [1.27; 1.90]), SDMA/arginine (HR 1.38 [1.14; 1.68]), putrescine (HR 1.31 [1.06; 1.61]), methionine sulfoxide (HR 1.26 [1.03; 1.52]), and 5-hydroxylysine (HR 1.25 [1.05; 1.48]) were associated with a higher risk of CV events. Our findings corroborate important associations between metabolic imbalances and a higher risk of CV events in HF patients. However, the lack of standardization and data reporting hampered the comparison of a higher number of studies. In a future clinical scenario, metabolomics will greatly benefit from harmonizing sample handling, data analysis, reporting, and sharing. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) and the frequently coexisting aortic valve stenosis (AVS) are heart diseases accounting for most cardiac surgeries. These share many risk factors, such as age, diabetes, hypertension, or obesity, and similar pathogenesis, including endothelial disruption, lipid and immune cell infiltration, inflammation, fibrosis, and calcification. Unsuspected CAD and AVS are sometimes detected opportunistically through echocardiography, coronary angiography, and magnetic resonance. Routine biomarkers for early detection of either of these atherosclerotic-rooted conditions would be important to anticipate the diagnosis. With a noninvasive collection, urine is appealing for biomarker assessment. We conducted a shotgun proteomics exploratory analysis of urine from 12 CAD and/or AVS patients and 11 controls to identify putative candidates to differentiate these diseases from healthy subjects. Among the top 20 most dysregulated proteins, TIMP1, MMP2 and vWF stood out, being at least 2.5× increased in patients with CAD/AVS and holding a central position in a network of protein-protein interactions. Moreover, their assessment in an independent cohort (19 CAD/AVS and 10 controls) evidenced strong correlations between urinary TIMP1 and vWF levels and a common cardiovascular risk factor - HDL (r = 0.59, p < 0.05, and r = 0.64, p < 0.01, respectively). [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) is one of the most common cancers. Due to the limited and invasive approaches for PCa diagnosis, it is crucial to identify more accurate and non-invasive biomarkers for its detection. The aim of our study was to non-invasively uncover new protein targets for detecting PCa using a proteomics and proteogenomics approach. This work identified several dysregulated mutant protein isoforms in urine from PCa patients, some of them predicted to have a protective or an adverse role in these patients. These results are promising given urine's non-invasive nature and offers an auspicious opportunity for research and development of PCa biomarkers. To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer. [ABSTRACT FROM AUTHOR]

A novel approach based on headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC–ToFMS) was developed for the simultaneous screening of microbial and mite contamination level in cereals and coffee beans. The proposed approach emerges as a powerful tool for the rapid assessment of the microbial contamination level (ca. 70 min versus ca. 72 to 120 h for bacteria and fungi, respectively, using conventional plate counts), and mite contamination (ca. 70 min versus ca. 24 h). A full-factorial design was performed for optimization of the SPME experimental parameters. The methodology was applied to three types of rice (rough, brown, and white rice), oat, wheat, and green and roasted coffee beans. Simultaneously, microbiological analysis of the samples (total aerobic microorganisms, moulds, and yeasts) was performed by conventional plate counts. A set of 54 volatile markers was selected among all the compounds detected by GC×GC–ToFMS. Principal Component Analysis (PCA) was applied in order to establish a relationship between potential volatile markers and the level of microbial contamination. Methylbenzene, 3-octanone, 2-nonanone, 2-methyl-3-pentanol, 1-octen-3-ol, and 2-hexanone were associated to samples with higher microbial contamination level, especially in rough rice. Moreover, oat exhibited a high GC peak area of 2-hydroxy-6-methylbenzaldehyde, a sexual and alarm pheromone for adult mites, which in the other matrices appeared as a trace component. The number of mites detected in oat grains was correlated to the GC peak area of the pheromone. The HS-SPME/GC×GC–ToFMS methodology can be regarded as the basis for the development of a rapid and versatile method that can be applied in industry to the simultaneous assessment the level of microbiological contamination and for detection of mites in cereals grains and coffee beans. [ABSTRACT FROM AUTHOR]

• Rheumatoid arthritis patients have high risk of heart failure. Age, cardiovascular risk factors, and rheumatoid arthritis duration increase the heart failure risk. • Patients with rheumatoid arthritis and heart failure largely share similar mechanistic pathways with patients with heart failure without rheumatoid arthritis. These include a higher expression of biomarkers associated with congestion, inflammation, fibrosis and RAAS activation. • Treatments that prolong the event-free survival of heart failure patients without rheumatoid arthritis should also be formally tested in patients with concomitant heart failure and rheumatoid arthritis. Rheumatoid arthritis (RA) patients have high risk of heart failure (HF). Identifying the risk factors and mechanistic pathways associated with HF in patients with RA. Cohort study enrolling 355 RA patients. HF was defined according to the ESC criteria. 93 circulating protein-biomarkers (91CVDIIOlink®+troponin-T+c-reactive protein) were measured. Regression modeling (multivariate and multivariable) were built and network analyses were performed - based on the identified relevant protein biomarkers. 115 (32.4%) patients fulfilled the ESC criteria for HF, but only 24 (6.8%) had a prior HF diagnosis. Patients with HF were older (67 vs. 55yr), had a longer RA duration (10 vs. 14yr), had more frequently diabetes, hypertension, obesity, dyslipidemia, atrial fibrillation, and ischemic arterial disease. Several protein-biomarkers remained independently associated with HF, the top (FDR1%) were adrenomedullin, placenta-growth-factor, TNF-receptor-11A, and angiotensin-converting-enzyme-2. The networks underlying the expression of these biomarkers pointed towards congestion, apoptosis, inflammation, immune system signaling and RAAS activation as central determinants of HF in RA. Similar HF-associated biomarker-pathways were externally found in patients without RA. Having RA plus HF increased the risk of cardiovascular events compared to RA patients without RF; adjusted-HR (95%CI)=2.37 (1.07-5.30), p=0.034 Age, cardiovascular risk factors, and RA duration increase the HF odds in patients with RA. Few RA patients had a correct prior HF diagnosis, but the presence of HF increased the patients' risk. RA patients with HF largely share the mechanistic pathways of HF patients without RA. Randomized HF trials should include patients with RA. NCT03960515 Image, graphical abstract Legend: RAAS, renin-angiotensin aldosterone system. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) remain the biggest cause of deaths worldwide and a major socio-economic impact to society. In this work, we conducted an unbiased exploratory analysis of the large-scale lipidome in human plasma samples from patients with fatal and non-fatal CVD from large cohorts. The exploratory analysis included data from 10,349 individuals from 20 countries in Asia, Australasia, Europe and North America (ADVANCE cohort), and thus representative of the worldwide population. Through the analysis of hazard ratios (HR), we found 306 lipids relevant in CV Death and 294 lipids relevant in CV Events of which 262 lipids were common to fatal and non-fatal events followed over time (3, 5 and 8 years). Our exploratory analysis reveals that, over time, the plasma lipid signature found in non-fatal CVD events is similar to that preceding CVD death. Among the common lipid signature, we found that sphingolipids (HexCer, SM, Cer and other glycosphingolipids) and phospholipids (PC and PE) were strongly associated with CVD events outcome, while polyunsaturated plasmenyl PC and PE lipids were inversely associated with CV outcome. The restricted panel of specific lipids has the potential to improve CVD risk stratification and management, and significantly reduce the time involved in the analysis and data treatment in low-resolution MS instruments making plasma lipidomics a cost-efficient approach for clinical scenario. In our view, once standardized clinical, analytical and data reporting guidelines are implemented worldwide, lipid-based discriminators can be routinely applied in the CVD risk stratification and improve the performance of current clinical, biochemical and imaging diagnostic tools assisting the decision-making process particularly in patients with multiple co-morbidities. Image 1 • Clinical and biochemical markers are unable to stratify patients at intermediate risk. • We pursued an exploratory analysis of large lipidome in large sampling group datasets. • Our exploratory analysis revealed lipid signature of CV Events preceded CV Death. • Panel of 29 discriminant lipids could speed-up decision-making process. • CVD lipid-based marker validation rely on standardization of lipid pipeline protocols. [ABSTRACT FROM AUTHOR]

Abstract: This work describes a mid-infrared (MIR) metabolic profiling study of 2nd trimester amniotic fluid in relation to selected prenatal disorders, with results focusing on fetal malformations (FM), preterm delivery (PTD) and premature rupture of membranes (PROM), the latter two conditions occurring later in pregnancy. Partial least squares-discriminant analysis (PLS-DA) models were obtained for FM and pre-PTD subject groups, supported by Monte Carlo Cross Validation (MCCV), and identified specific MIR profile changes. For pre-PROM subjects, minor changes were noted. MIR interpretation was assisted by intra- (MIR/MIR) and inter- (MIR/NMR) domain statistical correlation analysis, the results unveiling possible biomarker MIR signatures for FM and pre-PTD subjects. Biofluid MIR metabolic profiling holds enticing possibilities as a low cost, easy to use, rapid method and the results presented have shown its sensitivity to clinically diagnosed conditions such as FM, and to the pre-clinical stages of PTD. Specific improvement needs are discussed, namely regarding sample numbers and experimental reproducibility. [Copyright &y& Elsevier]