Changes in human sweat metabolome conditioned by severity of obstructive sleep apnea and intermittent hypoxemia.

Obstructive sleep apnea (OSA) is a sleep disorder that has been associated with the incidence of other pathologies. Diagnosis is mainly based on the apnea-hypopnea index (AHI) obviating other repercussions such as intermittent hypoxemia, which has been found to be associated to cardiovascular complications. Blood-based samples and urine have been the most utilised biofluids in metabolomics studies related to OSA, while sweat could be an alternative due to its non-invasive and accessible sampling, its reduced complexity, and comparability with other biofluids. Therefore, this research aimed to evaluate metabolic overnight changes in sweat collected from patients with OSA classified according to the AHI and oxygen desaturation index (ODI), looking for potential cardiovascular repercussions. Pre- and post-sleeping sweat samples from all individuals (n = 61) were analysed by gas chromatography coupled to high-resolution mass spectrometry after appropriate sample preparation to detect as many metabolites as possible. Permanent significant alterations in the sweat were reported for pyruvate, serine, lactose, and hydroxybutyrate. The most relevant overnight metabolic alterations in sweat were reported for lactose, succinate, urea, and oxoproline, which presented significantly different effects on factors such as the AHI and ODI for OSA severity classification. Overall metabolic alterations mainly affected energy production-related processes, nitrogen metabolism, and oxidative stress. In conclusion, this research demonstrated the applicability of sweat for evaluation of OSA diagnosis and severity supported by the detected metabolic changes during sleep.

Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study.

OBJECTIVE: We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. METHODS: This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60-40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out. RESULTS: LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%. CONCLUSION: Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937.

MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis.

Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).

Determination of vitamin D3 conjugated metabolites: a complementary view on hydroxylated metabolites.

Experts typically define vitamin D deficiency levels by the determination of a circulating 25-hydroxyvitamin D3-calcifediol prohormone. A large part of the population is characterized by deficient vitamin D levels (calcifediol < 20 ng mL-1) despite individuals not being affected by any disorder. Cholecalciferol (vitamin D3) and/or calcifediol supplementation is a common practice for vitamin D-deficient individuals as recommended by international scientific societies and official agencies. In the last few years, several studies have reported the presence of conjugated vitamin D3 metabolites, mainly glucuronidation and sulfation derivatives, although simultaneous quantitative measurements involving phase I and II vitamin D metabolites have not been carried out. A quantitative method based on tandem mass spectrometry detection is proposed here for the combined determination of phase I and phase II vitamin D3 metabolites in human serum. As phase I and phase II metabolites are preferentially ionized in different modes, a switching polarity mode was adopted to determine both groups of compounds in serum at high sensitivity levels (pg mL-1). The validation of this proposal was successfully accomplished by following the Center for Drug Evaluation and Research (CDER) guidelines. Its applicability was tested in a cohort of volunteers with mostly deficient baseline levels. Considering the sulfated form of calcifediol, the sum of its concentrations showed sufficient baseline vitamin D levels in all individuals, suggesting that this could be a novel strategy for vitamin D deficiency definition. Therefore, phase II metabolites are proposed to be included when evaluating the vitamin D status since they provide more information about the overall status of the vitamin D endocrine system. Nevertheless, further studies are required to confirm the biological activity of these conjugated metabolites and the suitability of this strategy for the description of vitamin D deficiency.

Profiling analysis of phospholipid fatty acids in serum as a complement to the comprehensive fatty acids method.

Fatty acids (FAs) are mostly found in blood as triglycerides, phospholipids (PLs) and cholesteryl esters. Determination of FAs is typically carried out in serum or plasma by a comprehensive method (known as the classical FAMEs method since FAs are determined as Fatty Acids Methyl Esters), which is based on liquid-liquid extraction, derivatization by transesterification, and determination by gas chromatography (GC) coupled to a suited detection technique. However, this method does not favor the determination of FAs that are chemically conjugated in PLs due to kinetics impediment. For this reason, we have developed a selective method to determine the FAs profile of PLs in serum based on solid-phase extraction (SPE) for isolation of PLs and determination of the FAME derivatives by GC-mass spectrometry (GC-MS). The method was applied to serum samples collected from twenty-five individuals to compare the FAs profile versus that provided by the non-selective protocol based on liquid-liquid extraction of lipid families. Statistical analysis revealed compositional changes in the FAs profile with special emphasis on the content of saturated (SFAs) and monounsaturated FAs (MUFAs). Thus, SFAs passed from 34.0% with the classical method to 49.3% in PLs while MUFAs went from 24.4% to 11.4%. This study proves that the proposed method provides complementary results to the comprehensive method and, therefore, both methods can be combined to evaluate the effect of intervention diets and their connection to metabolic diseases.

Metabolomic profiling of human lung tumor tissues - nucleotide metabolism as a candidate for therapeutic interventions and biomarkers.

Although metabolomics has attracted considerable attention in the field of lung cancer (LC) detection and management, only a very limited number of works have applied it to tissues. As such, the aim of this study was the thorough analysis of metabolic profiles of relevant LC tissues, including the most important histological subtypes (adenocarcinoma and squamous cell lung carcinoma). Mass spectrometry-based metabolomics, along with genetic expression and histological analyses, were performed as part of this study, the widest to date, to identify metabolic alterations in tumors of the most relevant histological subtypes in lung. A total of 136 lung tissue samples were analyzed and 851 metabolites were identified through metabolomic analysis. Our data show the existence of a clear metabolic alteration not only between tumor vs. nonmalignant tissue in each patient, but also inherently intrinsic changes in both AC and SCC. Significant changes were observed in the most relevant biochemical pathways, and nucleotide metabolism showed an important number of metabolites with high predictive capability values. The present study provides a detailed analysis of the metabolomic changes taking place in relevant biochemical pathways of the most important histological subtypes of LC, which can be used as biomarkers and also to identify novel targets.

Multi-omic profiling to assess the effect of iron starvation in Streptococcus pneumoniae TIGR4.

We applied multi-omics approaches (transcriptomics, proteomics and metabolomics) to study the effect of iron starvation on the Gram-positive human pathogen Streptococcus pneumoniae to elucidate global changes in the bacterium in a condition similar to what can be found in the host during an infectious episode. We treated the reference strain TIGR4 with the iron chelator deferoxamine mesylate. DNA microarrays revealed changes in the expression of operons involved in multiple biological processes, with a prevalence of genes coding for ion binding proteins. We also studied the changes in protein abundance by 2-DE followed by MALDI-TOF/TOF analysis of total cell extracts and secretome fractions. The main proteomic changes were found in proteins related to the primary and amino sugar metabolism, especially in enzymes with divalent cations as cofactors. Finally, the metabolomic analysis of intracellular metabolites showed altered levels of amino sugars involved in the cell wall peptidoglycan metabolism. This work shows the utility of multi-perspective studies that can provide complementary results for the comprehension of how a given condition can influence global physiological changes in microorganisms.

MetaboQC: A tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls.

Nowadays most metabolomic studies involve the analysis of large sets of samples to find a representative metabolite pattern associated to the factor under study. During a sequence of analyses the instrument signals can be subjected to the influence of experimental variability sources. Implementation of quality control (QC) samples to check the contribution of experimental variability is the most common approach in metabolomics. This practice is based on the filtration of molecular entities experiencing a variation coefficient higher than that measured in the QC data set. Although other robust correction algorithms have been proposed, none of them has provided an easy-to-use and easy-to-install tool capable of correcting experimental variability sources. In this research an R-package -the MetaboQC- has been developed to correct intra-day and inter-days variability using QCs analyzed within a pre-set sequence of experiments. MetaboQC has been tested in two data sets to assess the correction effects by comparing the metabolites variability before and after application of the proposed tool. As a result, the number of entities in QCs significantly different between days was reduced from 86% to 19% in the negative ionization mode and from 100% to 13% in the positive ionization mode. Furthermore, principal component analysis allowed detecting the filtration of instrumental variability associated to the injection order.

Integrated proteomic and metabolomic analysis reveals that rhodomyrtone reduces the capsule in Streptococcus pneumoniae.

The emergence of antibiotic-resistant pathogenic bacteria is a healthcare problem worldwide. We evaluated the antimicrobial activity of rhodomyrtone, an acylphloroglucinol present in Rhodomyrtus tomentosa leaves, against the human Gram-positive pathogen Streptococcus pneumoniae. The compound exhibited pronounced anti-pneumococcal activity against a broad collection of clinical isolates. We studied the effects at the molecular level by integrated proteomic and metabolomic analysis. The results revealed alterations in enzymes and metabolites involved in several metabolic pathways including amino acid biosynthesis, nucleic acid biosynthesis, glucid, and lipid metabolism. Notably, the levels of two enzymes (glycosyltransferase and UTP-glucose-1-phosphate uridylyltransferase) and three metabolites (UDP-glucose, UDP-glucuronic acid and UDP-N-acetyl-D-galactosamine) participating in the synthesis of the pneumococcal capsule clearly diminished in the bacterial cells exposed to rhodomyrtone. Rhodomyrtone-treated pneumococci significantly possessed less amount of capsule, as measured by a colorimetric assay and visualized by electron microscopy. These findings reveal the utility of combining proteomic and metabolomic analyses to provide insight into phenotypic features of S. pneumoniae treated with this potential novel antibiotic. This can lead to an alternative antibiotic for the treatment of S. pneumoniae infections, because of the growing concern regarding antimicrobial resistance.

Untargeted analysis to monitor metabolic changes of garlic along heat treatment by LC-QTOF MS/MS.

Black garlic is increasing its popularity in cuisine around the world; however, scant information exists on the composition of this processed product. In this study, polar compounds in fresh garlic and in samples taken at different times during the heat treatment process to obtain black garlic have been characterized by liquid chromatography coupled to tandem mass spectrometry in high resolution mode. Ninety-five compounds (mainly amino acids and metabolites, organosulfur compounds, and saccharides and derivatives) were tentatively identified in all the analysed samples and classified as a function of the family they belong to. Statistical analysis of the results allowed establishing that the major changes in garlic occur during the first days of treatment, and they mainly affect to the three representative families. The main pathways involved in the synthesis of the compounds affected by heat treatment, and their evolution during the process were studied.

Confirmatory and quantitative analysis of fatty acid esters of hydroxy fatty acids in serum by solid phase extraction coupled to liquid chromatography tandem mass spectrometry.

A novel class of endogenous mammalian lipids endowed with antidiabetic and anti-inflammatory properties has been recently discovered. These are fatty acid esters of hydroxy fatty acids (FAHFAs) formed by condensation between a hydroxy fatty acid and a fatty acid. FAHFAs are present in human serum and tissues at low nanomolar concentrations. Therefore, high sensitivity and selectivity profiling analysis of these compounds in clinical samples is demanded. An automated qualitative and quantitative method based on on-line coupling between solid phase extraction and liquid chromatography-tandem mass spectrometry has been here developed for determination of FAHFAs in serum with the required sensitivity and selectivity. Matrix effects were evaluated by preparation of calibration models in serum and methanol. Recovery factors ranged between 73.8 and 100% in serum. The within-day variability ranged from 7.1 to 13.8%, and the between-days variability varied from 9.3 to 21.6%, which are quite acceptable values taking into account the low concentration levels at which the target analytes are found. The method has been applied to a cohort of human serum samples to estimate the concentrations profiles as a function of the glycaemic state and obesity. Statistical analysis revealed three FAHFAs with levels significantly different depending on the glycaemic state or the body mass index. This automated method could be implemented in high-throughput analysis with minimum user assistance.

Enhancing detection coverage in untargeted metabolomics analysis by solid-phase extraction on-line coupled to LC-MS/MS.

One of the main limitations of untargeted metabolomics analysis is the low detection coverage of analytical techniques such as NMR, LC-MS, or GC-MS. In this research, the detection coverage of an automated approach configured by the on-line coupling of SPE to LC-MS/MS was evaluated by combination of sorbents based on different retention mechanisms. The approach was applied to the analysis of human serum using three types of sorbents: alkyl bonded silica, polymeric resins, and mixed-mode ionic resins. The combination of four sorbents (C18, a modified polystyrene-divinylbenzene resin and two mixed-mode ionic resins) led to the best extraction results and, therefore, the best detection coverage, which is explained by their complementary retention mechanisms. However, some of the sorbents provide a high detection coverage by themselves, as is the case with C18, which can afford to retain almost 83% of all detected entities. Taking into account the complementarity between pairs of these sorbents (C18 and the polystyrene-divinylbenzene resin with the mixed-mode ionic resins), dual cartridge SPE-LC-MS/MS configurations were designed for serum analysis. These configurations allowed increasing the detection coverage up to 91% of the total number of molecular features detected with all sorbents tested. An additional benefit of the SPE-LC-MS/MS strategy was the improvement of sensitivity as compared to protein precipitation and fractionation with methanol and chloroform. Thus, an average preconcentration factor of 10-75 was obtained in the SPE-based approach versus the two-phase protocol for metabolites extraction.

Human sweat metabolomics for lung cancer screening.

Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites.

Determination of fatty acids and stable carbon isotopic ratio in subcutaneous fat to identify the feeding regime of Iberian pigs.

Discrimination among the types of feeding regimes for Iberian pigs is currently a highly demanded challenge by the Iberian pig sector. In the present research, discrimination among feeding regimes has been achieved by the combination of two analytical methods (based on FAMEs analysis by GC-FID and determination of δ(13)C by IRMS) previously used independently without success. In the present study, 80 samples of adipose tissue from Iberian pigs subjected to four different feedings were analyzed. The study of the variables more influenced by the feeding regime has allowed us to configure panels of markers with predictive power for the studied feedings by multivariate ROC analysis. The results provided values of specificity and sensitivity higher than 85% in most cases. The statistical combination of results from different analytical methods could be the key to develop models for the correct discrimination of Iberian pigs according to the feeding regime.

Enhanced detection and identification in metabolomics by use of LC-MS/MS untargeted analysis in combination with gas-phase fractionation.

Liquid chromatography coupled to tandem mass spectrometry is one of the most widely used analytical platforms for profiling analysis in metabolomics. One weakness of untargeted metabolomic analysis, however, is the difficulty of identifying metabolites. In fact, the process typically involves mass-based searching of LC-MS and LC-MS/MS data and requires using MS/MS data for unequivocal identification. Current strategies use LC-MS analysis in the scan mode prior to acquiring MS/MS information about targeted metabolites or the "auto MS/MS" mode to fragment automatically the most intense precursor ions. Therefore, in both cases additional injections are required to obtain MS/MS data after data treatment to identify significant compounds whose signals are not so intense. Because an additional procedure is needed to enhance the fraction of metabolites with MS/MS data, in this work, the effectiveness of utilizing different MS/MS parameters across an analytical batch or repetitions of the same sample by using exclusion or inclusion criteria to select precursor ions is assessed. The procedure, known as "gas-phase fractionation (GPF)", was used here for untargeted analysis of serum. The joint use of four methods with a different mass range for selection of precursor ions each provided useful MS/MS information for at least 80% of all molecular entities detected in the MS scan replicates. By contrast, the conventional "auto MS/MS" mode of data acquisition provided MS/MS data for only 48-57% of entities and was therefore less effective toward identifying metabolites. The additional use of GPF improved the detection and annotation of metabolite families such as phospholipids, amino acids, bile acids, carnitines, and fatty acids and their derivatives.

High-resolution mass spectrometry to evaluate the influence of cross-breeding segregating populations on the phenolic profile of virgin olive oils.

BACKGROUND: The growing demand for high-quality virgin olive oils (VOOs) has increased the interest in olive breeding programs. Cross-breeding is considered, within these programs, the best strategy to generate new cultivars as an attempt to improve the present cultivars. In this research, the phenolic profile of VOOs from target crosses (Arbequina × Arbosana, Picual × Koroneiki and Sikitita × Arbosana) and their corresponding genitors (Arbequina, Arbosana, Koroneiki, Picual and Sikitita) has been evaluated using a targeted metabolomics approach. RESULTS: The phenolic profiles were obtained by liquid chromatographic-hybrid quadrupole time-of-flight mass spectrometric targeted analysis of 37 phenols or compounds involved in the main pathways for their biosynthesis. Statistical multivariate analysis by principal component analysis was applied to study the influence of genotype on phenol composition. Phenolic compounds with the highest contribution to explain the observed variability associated to genotype were identified through fold change algorithms (cut-off > 2.0) and t-test analysis. CONCLUSION: A total of nine phenols (viz. quercetin, ligstroside aglycon (p-HPEA-EA), demethyl oleuropein aglycon, oleuropein aglycon (3,4-DHPEA-EA), hydroxypinoresinol, hydroxytyrosol and phenolic acids such as p-coumaric acid, ferulic acid and protocatechuic acid) contributed to explain the observed variability with 99% confidence (P<0.01).

Global metabolomic profiling of human serum from obese individuals by liquid chromatography-time-of-flight/mass spectrometry to evaluate the intake of breakfasts prepared with heated edible oils.

The metabolic profile of human serum after intake of breakfasts prepared with different heated vegetable oils has been studied. Four oils (olive and sunflower oils, pure and enriched with natural and artificial oxidation inhibitors) were subjected to a simulated heated process prior to breakfast preparation. A metabolomics global profiling approach performed on post-basal serum samples revealed statistical differences among individuals based on breakfast intake, and identified compounds responsible for such differences. Serum samples obtained in basal state (control samples) and 2 and 4h after programmed intakes were analyzed by LC-TOF/MS. The resulting fingerprints were compared and differences between basal and post-basal states evaluated, observing that the intake of different breakfasts altered the metabolic signature of serum. Analysis models based on PLS algorithms were developed to discriminate individuals in post-basal state for each intervention breakfast. Then, Volcano tests enabled to detect significant molecular entities explaining the variability associated to each breakfast. It is worth emphasizing the importance of fatty acids, their derivatives and phospholipids for tentative identification.

Analytical platform for verification and quantitation of target peptides in human serum: application to cathelicidin.

A selective and sensitive, fully automated platform for verification and quantitative determination of target peptides in biofluids is proposed and then validated by development of a method for analysis of cathelicidin in human serum. The method is based on the on-line coupling of solid-phase extraction (SPE) and tandem mass spectrometry with direct infusion. Mass spectrometry analysis was carried out by multiple reaction monitoring using three transitions (one for quantitative analysis and two for qualitative analysis), all them confirmed by in silico fragmentation of the target peptide. Samples were prepared in the SPE workstation on a polymeric divinylbenzene resin by preconcentration, deproteinization, and cleanup, removing salts and interferences after direct injection of human serum. The analytical process required 12 min. The limits of detection and quantitation were 2.5 and 8.25 µg/L, respectively (0.20 and 0.66 pg on column). Repeatability and within-laboratory reproducibility were 2.4% and 2.7%, respectively. A dual-cartridge configuration was used to test recovery of cathelicidin in serum, resulting in 80%. Because quantitative retention in the cartridge was assessed, determination of cathelicidin was validated without using synthetic peptides labeled with stable isotopes. The hyphenated system allows full automation, thereby improving reproducibility and accuracy, as demanded by clinical analysis.