Direct-infusion High-resolution Mass Research Articles

Exploiting Hydroxamic Acids as Organocatalysts for the Epoxidation of Alkenes with Hydrogen Peroxide as the Oxidant

AbstractThe epoxidation of alkenes has altered and reshaped the way scientists think in modern organic synthesis. Herein, a novel, sustainable and eco‐friendly protocol for the epoxidation of alkenes is reported, utilizing hydrogen peroxide as the green oxidant and, for the first time, hydroxamic acid derivatives as the organocatalyst. The presented methodology is very effective, leading to good to high yields for the oxidation of a variety of olefins, while various functional groups are well tolerated. Mechanistic studies utilizing direct infusion high resolution mass spectrometry (DI‐HRMS) provide strong evidence for the mechanism of the organocatalytic oxidation.

A one-year pilot study comparing direct-infusion high resolution mass spectrometry based untargeted metabolomics to targeted diagnostic screening for inherited metabolic diseases.

Background: Early diagnosis of inherited metabolic diseases (IMDs) is important because treatment may lead to reduced mortality and improved prognosis. Due to their diversity, it is a challenge to diagnose IMDs in time, effecting an emerging need for a comprehensive test to acquire an overview of metabolite status. Untargeted metabolomics has proven its clinical potential in diagnosing IMDs, but is not yet widely used in genetic metabolic laboratories. Methods: We assessed the potential role of plasma untargeted metabolomics in a clinical diagnostic setting by using direct infusion high resolution mass spectrometry (DI-HRMS) in parallel with traditional targeted metabolite assays. We compared quantitative data and qualitative performance of targeted versus untargeted metabolomics in patients suspected of an IMD (n = 793 samples) referred to our laboratory for 1year. To compare results of both approaches, the untargeted data was limited to polar metabolites that were analyzed in targeted plasma assays. These include amino acid, (acyl)carnitine and creatine metabolites and are suitable for diagnosing IMDs across many of the disease groups described in the international classification of inherited metabolic disorders (ICIMD). Results: For the majority of metabolites, the concentrations as measured in targeted assays correlated strongly with the semi quantitative Z-scores determined with DI-HRMS. For 64/793 patients, targeted assays showed an abnormal metabolite profile possibly indicative of an IMD. In 55 of these patients, similar aberrations were found with DI-HRMS. The remaining 9 patients showed only marginally increased or decreased metabolite concentrations that, in retrospect, were most likely to be clinically irrelevant. Illustrating its potential, DI-HRMS detected additional patients with aberrant metabolites that were indicative of an IMD not detected by targeted plasma analysis, such as purine and pyrimidine disorders and a carnitine synthesis disorder. Conclusion: This one-year pilot study showed that DI-HRMS untargeted metabolomics can be used as a first-tier approach replacing targeted assays of amino acid, acylcarnitine and creatine metabolites with ample opportunities to expand. Using DI-HRMS untargeted metabolomics as a first-tier will open up possibilities to look for new biomarkers.

Optimization of azithromycin degradation: Integrating ANN-PSO modeling, intermediates analysis, identification, and microbiological assessment

BackgroundThis study aims to identify the by-products generated during the degradation process of azithromycin (AZM), a prevalent antibiotic during the COVID-19 pandemic, via a novel chlorine/ferrous-based advanced oxidation process. MethodsThe optimal conditions for AZM degradation were determined using a neural network coupled with particle swarm optimization modeling. The by-products generated during the process were identified using QuEChERS based on dispersive solid-phase extraction and direct-infusion high-resolution mass spectrometry analysis techniques. The efficacy of the proposed method was validated through microbiological assays conducted pre- and post-degradation of AZM. Significant findingsThe optimal values determined were [NaClO] = 200 μM, [AZM] = 35.37 mg L–1, [FeSO4] = 3.5 mg L–1, and a pH 2.51, providing a predicted yield of 99.9%. Five photoproducts, Azit-DP1 through Azit-DP5, were successfully identified with respective m/z values of 357.11533, 159.06552, 146.08127, 100.07701, and 83.04969. A proposed mechanism for the degradation pathway was presented. The biotests results confirmed that as the quantity of AZM in the medium decreases due to degradation, its antimicrobial effectiveness diminishes accordingly, eventually disappearing completely after 4 h of the degradation process. The results of the performed biotests confirmed the effectiveness of the proposed low-cost system in removing azithromycin and its by-products from the aqueous medium.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry.

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Sunlight‐ or UVA‐Light‐Mediated Synthesis of Hydroxamic Acids from Carboxylic Acids

AbstractThe development of light‐mediated methods for synthetic applications is increasingly attracting high interest. We present herein a new photochemical protocol for the synthesis of hydroxamic acids, which constitute an important class of medicinal agents, mainly due to their anticancer properties. The method is mediated by UVA‐light or sunlight and its key point is the generation of a charge transfer complex by the interaction of 4‐dimethylaminopyridine with a halomethane. Various carboxylic acids were directly coupled with O‐protected hydroxylamines, upon irradiation with either LED 370 nm or solar light. A detailed study of the mechanism was carried out by the employment of direct infusion–high resolution mass spectrometry (DI‐HRMS), providing experimental evidence for the formation of various activated species, which may lead to the desired product. The light‐mediated protocol was applied in the synthesis of the drugs Vorinostat and Bufexamac.

Untargeted Metabolomics Identifies Potential Hypertrophic Cardiomyopathy Biomarkers in Carriers of MYBPC3 Founder Variants.

Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease, commonly caused by pathogenic MYBPC3 variants, and a significant cause of sudden cardiac death. Severity is highly variable, with incomplete penetrance among genotype-positive family members. Previous studies demonstrated metabolic changes in HCM. We aimed to identify metabolite profiles associated with disease severity in carriers of MYBPC3 founder variants using direct-infusion high-resolution mass spectrometry in plasma of 30 carriers with a severe phenotype (maximum wall thickness ≥20 mm, septal reduction therapy, congestive heart failure, left ventricular ejection fraction <50%, or malignant ventricular arrhythmia) and 30 age- and sex-matched carriers with no or a mild phenotype. Of the top 25 mass spectrometry peaks selected by sparse partial least squares discriminant analysis, XGBoost gradient boosted trees, and Lasso logistic regression (42 total), 36 associated with severe HCM at a p < 0.05, 20 at p < 0.01, and 3 at p < 0.001. These peaks could be clustered to several metabolic pathways, including acylcarnitine, histidine, lysine, purine and steroid hormone metabolism, and proteolysis. In conclusion, this exploratory case-control study identified metabolites associated with severe phenotypes in MYBPC3 founder variant carriers. Future studies should assess whether these biomarkers contribute to HCM pathogenesis and evaluate their contribution to risk stratification.

Untargeted Metabolomics on Dried Blood Spots of Patients with Sickle Cell Disease Treated with the Pyruvate Kinase Activator Mitapivat

Background Sickle cell disease (SCD) is one of the most common inherited red blood cell (RBC) disorders. The disease is characterized by polymerization of sickle hemoglobin (HbS) upon deoxygenation resulting in, among other things, chronic life-threatening complications. The pathophysiology of SCD is multifactorial and highly complex. Novel anti-sickling agents under development for SCD include oral, small molecule pyruvate kinase activators such as mitapivat (AG-348). Mitapivat could reduce HbS polymerization by decreasing the level of the glycolytic intermediate metabolite 2,3-diphosphoglycerate (2,3-DPG), thereby increasing the oxygen affinity of hemoglobin, and the level of adenosine triphosphate (ATP) to increase cellular energy. We investigated, as an exploratory outcome, changes in untargeted metabolomics on dried blood spots (DBS) of patients with SCD treated with mitapivat in the 8-week Dose Finding Period of an investigator-initiated, open label, phase 2 study (EudraCT 2019-003438-18, ESTIMATE study). Methods Untargeted metabolomics was performed by direct-infusion high-resolution mass spectrometry on DBS of patients ≥16 years with SCD (HbSS, HbS/β0 or HbS/β+-thalassemia) collected at baseline and at completion of the 8-week Dose Finding Period. In this period, patients received initial dosing of 20 mg mitapivat twice daily with a maximum of two sequential dose escalations to 50 mg and 100 mg twice daily every two weeks depending on safety. DBS samples obtained from healthy adults were used to calculate Z-scores. Data analyses were conducted in MetaboAnalyst (v5.0) without data filtering or normalization. Distinct metabolic profiles between baseline samples of patients with SCD and healthy controls were identified by multivariate principal component analysis and partial least square discriminant analysis, as well as independent two-sample t-tests with equal group variance. False Discovery Rate (FDR) adjusted p-values were used to correct for multiple testing. A significance level of 0.05 was applied. Distinctive increased or decreased metabolites were also analyzed at treatment week 8 and compared to baseline in the cohort of patients with SCD. Additional analyses were performed in Graphpad Prism (v9.3.0) including paired two-sample t-tests for individual Z-scores of features. Results In total, 1907 unique metabolite features were identified in DBS of 9 patients with SCD (n=7 HbSS, n=1 HbS/β0, n=1 HbS/β+-thalassemia; median age 22 years (range 16-59 years); 6/9 (67%) female) and 29 healthy controls (median age 38 years (range 25-65 years); 21/29 (72%) female). One HbSS patient and one healthy control were determined as outliers and excluded from final analysis. A total of 55 metabolites were significantly different between patient samples at baseline and healthy control samples. The top 35 are shown in a heatmap (Figure 1) and include increased acyl carnitines, (derivatives of) polyamines, purines and pyrimidines, and decreased carbohydrates and benzenoids. Patient samples showed an increase in 8/55 metabolites, of which 5 towards normalization, after 8-week treatment with mitapivat (unadjusted p-value <0.05), and no significant decreases (Table 1). In patients a significant increase was seen in 2/1907 unique metabolite features after 8 weeks of treatment with mitapivat compared to baseline: butenylcarnitine, which is an acyl carnitine, and inosinic acid, or inosine-5-monophosphate (5'-IMP), which is a purine nucleotide (FDR-adjusted p-value <0.05; data not shown). Conclusion Patients with SCD showed a distinct metabolic profile compared to healthy controls. The distinctions were determined by metabolites produced by RBC-related pathways including the glycolytic and purinergic signaling pathway that mediate hypoxic metabolic reprogramming, carnitine derivatives, involved in turnover and repair of the RBC membrane, and polyamines, that probably stabilize the RBC membrane (as reported from other hereditary hemolytic anemias). Moreover, after 8 weeks of treatment with mitapivat showed, again, changes in an acyl carnitine and purine nucleotide compared to baseline. We conclude that the distinct metabolic profile from healthy controls is a promising starting point to further unravel the underlying pathophysiology of SCD and to characterize the endogenous and exogenous effects of pyruvate kinase activators and other novel therapies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

P-023: 2,3-DIPHOSPHOGLYCERATE DETECTION VIA DIRECT INFUSION HIGH RESOLUTION MASS SPECTROMETRY CORRELATES WITH QUANTITATIVE DETECTION IN BLOOD OF PATIENTS WITH SICKLE CELL DISEASE

Purpose: Sickle cell disease (SCD) is a hereditary and chronic life-threatening disorder, characterized by haemolytic anaemia. Increased 2,3-diphosphoglycerate (2,3-DPG) concentrations, along with decreased oxygen affinity of hemoglobin, may be related to the variability of clinical outcomes in SCD. Furthermore, genomic health data holds promise to improve the prediction of disease severity in SCD. Based on the integration of genomics, metabolomics and clinical data from 1000 SCD patients, to be included in 2022, GenoMED4all aims to develop Artificial Intelligence (AI) based deep learning algorithms to improve the prediction of disease severity and phenotype in SCD. This study aims to correlate non-quantitative metabolomics data obtained from dried blood spots (DBS), one of the inputs for GenoMED4all, to quantitative measurement of 2,3-DPG. Aiming to improve the potential of non-quantitative metabolomics from DBS to asses 2,3-DPG. Materials and methods: In snap frozen blood samples from 37 SCD patients and 29 healthy controls, 2,3-DPG was quantified by liquid chromatography mass spectrometry. 2,3-DPG was also detected in DBS from the same subjects by direct infusion high resolution mass spectrometry (DIHRMS). The oxygen tension at 50% Hb saturation (p50) was determined using a Hemox Analyzer (TCS). Statistical analysis were performed by Spearman’s correlation coefficients (SPSS v26.0.0.1) and Mann Whitney testing (GraphPad Prism v9.3.0). Results: After correcting for Hb, 2,3-DPG concentrations were higher in SCD patients than in controls (p<0.001) and Z-scores for 2,3-DPG, as assessed by DIHRMS, were similar in patients and controls. The Z-scores positively correlated with 2,3-DPG concentrations (Fig1A, 0.353, p=0.004). Because of the anaemia in SCD, red blood cells (RBCs) and plasma make up lower and higher volumes in the blood, respectively, compared to healthy controls. This affects the ratio of RBC and plasma metabolites on the DBS. DIHRMS detects a wide range of RBC and plasma metabolites, whereas the quantitative measurement is restricted to measuring 2,3-DPG of RBCs. To correct for those differences between methods, we applied a correction factor to the DIHRMS data using (1/Ht)*(1-Ht/1), correcting for the RBC volume (1/Ht) and the plasma volume (1-Ht/1). This resulted in a trend towards higher Z-scores in patients than controls (p=0.0597). Moreover, the positive correlation with 2,3-DPG concentrations increased to 0.526 (Fig1B, p<0.001). As 2,3-DPG affects the oxygen affinity of Hb, all measurements were correlated to p50. Expectedly, 2,3-DPG concentrations positively correlated with p50 (0.842, p<0.001). After applying the correction factor to the DIHRMS data, p50 correlations increased from 0.361 (p=0.003) to 0.529 (p<0.001). Conclusion: Strongest correlation between quantitative and non-quantitative methods for 2,3-DPG detection were observed after correcting for both RBC and plasma volumes in non-quantitative metabolomics. After correction, DIHRMS can be used to assess 2,3-DPG concentrations in DBS. This translation of non-quantitative to quantitative metabolomics for 2,3-DPG and potentially other RBC metabolites adds significant value to the use of DIHRMS, as DIHRMS is far more efficient in obtaining extensive information about the total metabolome than quantitative methods. The large population size and the AI based deep learning algorithms of GenoMED4all will enable to evaluate the potential of non-quantitative metabolomics in SCD. Funding: Horizon2020, GenoMED4all(https://genomed4all.eu/), Agios Pharmaceuticals Inc., ERN-EuroBloodNetFig 1. correlations between non-quantitative (Z-score) and quantitative 2,3-DPG detection A) non-quantitative and quantitative 2,3-DPG detection B) non-quantitative detection corrected for RBC and plasma volume and quantitative 2,3-DPG detection The authors do not declare any conflict of interest

P1500: 2,3-DIPHOSPHOGLYCERATE DETECTION VIA DIRECT INFUSION HIGH RESOLUTION MASS SPECTROMETRY CORRELATES WITH QUANTITATIVE DETECTION IN BLOOD OF PATIENTS WITH SICKLE CELL DISEASE

Background: Sickle cell disease (SCD) is a hereditary and chronic life-threatening disorder, characterized by haemolytic anaemia. Increased 2,3-diphosphoglycerate (2,3-DPG) concentrations, along with decreased oxygen affinity of hemoglobin, may be related to the variability of clinical outcomes in SCD. Furthermore, genomic health data holds promise to improve the prediction of disease severity in SCD. Based on the integration of genomics, metabolomics and clinical data from 1000 SCD patients, to be included in 2022, GenoMED4all aims to develop Artificial Intelligence (AI) based deep learning algorithms to improve the prediction of disease severity and phenotype in SCD. Aims: To correlate non-quantitative metabolomics data obtained from dried blood spots (DBS), one of the inputs for GenoMED4all, to quantitative measurement of 2,3-DPG. Aiming to improve the potential of non-quantitative metabolomics from DBS to asses 2,3-DPG. Methods: In snap frozen blood samples from 37 SCD patients and 29 healthy controls, 2,3-DPG was quantified by liquid chromatography mass spectrometry. 2,3-DPG was also detected in DBS from the same subjects by direct infusion high resolution mass spectrometry (DIHRMS). The oxygen tension at 50% Hb saturation (p50)was determined using a Hemox Analyzer (TCS). Statistical analysis were performed by Spearman’s correlation coefficients (SPSS v26.0.0.1) and Mann Whitney testing (GraphPad Prism v9.3.0). Results: After correcting for Hb, 2,3-DPG concentrations were higher in SCD patients than in controls (p<0.001) and Z-scores for 2,3-DPG, as assessed by DIHRMS, were similar in patients and controls. The Z-scores positively correlated with 2,3-DPG concentrations (Fig.1A, 0.353, p=0.004). Because of the anaemia in SCD, RBCs and plasma make up lower and higher volumes in the DBS, respectively, compared to healthy controls. DIHRMS detects a wide range of RBC and plasma metabolites, whereas the quantitative measurement is restricted to measuring 2,3-DPG of RBCs. To correct for those differences between methods, we applied a correction factor to the DIHRMS data using the formula (1/Ht)*(1-Ht/1), correcting for the RBC volume (1/Ht) and the plasma volume (1-Ht/1). This resulted in a trend towards higher Z-scores in patients than controls (p=0.0597). Moreover, the positive correlation with 2,3-DPG concentrations increased to 0.526 (Fig 1B, p<0.001). As 2,3-DPG affects the oxygen affinity of Hb, all measurements were correlated to p50. Expectedly, 2,3-DPG concentrations positively correlated with p50 (0.842, p<0.001). After applying the correction factor to the DIHRMS data, p50 correlations increased from 0.361 (p=0.003) to 0.529 (p<0.001). Image:Summary/Conclusion: Strongest correlation between quantitative and non-quantitative methods for 2,3-DPG detection were observed after correcting for both the RBC and plasma volume in non-quantitative metabolomics. After correction, DIHRMS can be used to assess 2,3-DPG concentrations in DBS. This translation of non-quantitative to quantitative metabolomics for 2,3-DPG and potentially other RBC metabolites adds significant value to the use of DIHRMS, as DIHRMS is far more efficient in obtaining extensive information about the total metabolome than quantitative methods. The large population size and the AI based deep learning algorithms of GenoMED4all will enable to evaluate the potential of non-quantitative metabolomics in SCD. Funding: Horizon2020, GenoMED4all (https://genomed4all.eu/), Agios Pharmaceuticals Inc., ERN-EuroBloodNet

Distinct Metabolomic Signatures in Preclinical and Obstructive Hypertrophic Cardiomyopathy.

Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype–phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics.

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

BackgroundGenetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies.MethodsWe characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci.ResultsWe identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci.ConclusionsOur findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

Reactions of plasma-generated atomic oxygen at the surface of aqueous phenol solution: Experimental and modeling study

A remote atmospheric pressure plasma source with He/O2 gas mixture, a so-called COST-Jet, is used for the treatment of aqueous phenol solutions. Phenol consumption and the formation of phenol oxidation products (catechol, hydroquinone, resorcinol, and pyrogallol) are measured with high-performance liquid chromatography/UV-VIS and direct-infusion high-resolution mass spectrometry. The variation of O2 admixture and phenol concentrations in combination with 2D axisymmetric modeling of species transport and reaction kinetics both in the gas and liquid phase allow us to obtain more information about atomic oxygen reactions at and transport across the liquid surface. The results show that most of the atomic oxygen reactions with phenol take place at the liquid surface, mainly due to the low value of Henry’s law solubility constant of atomic oxygen and the surfactant character of phenol molecules. This study indicates that other atomic oxygen reactions, e.g., the reaction with Cl− anions in phosphate-buffered saline or in saline solution to form ClO−, also take place predominantly at the surface of the liquid. The knowledge provided by this work has important implications for further development of plasma–liquid treatments involving atomic oxygen as a reactant.

Untargeted Metabolomic Fingerprinting As a Potential Tool in the Diagnostic Evaluation of Diamond Blackfan Anemia

Background: Diamond-Blackfan anemia (DBA) is a rare inherited bone marrow failure syndrome (IBMFS) marked by erythroid hypoplasia, reticulocytopenia and macrocytosis, and associated with congenital malformations and an increased risk of developing malignancies. From a clinical and molecular perspective this disease is highly heterogeneous, and no clear genotype-phenotype correlations can be found. Since the majority of molecular defects have been found in ribosomal protein (RP) genes, DBA is regarded a "ribosomopathy". While the molecular basis has been studied intensively, the pathophysiology of DBA is still not fully understood. One of the major unresolved issues is how RP gene mutations result in the specific erythroid defect seen in DBA, with macrocytic erythrocytes and increased adenosine deaminase activity. In addition, the highly heterogeneous and variable clinical presentation and disease course, even in patients with similar molecular defects, remains enigmatic. Hence, in order to investigate the cellular defect, and increase our understanding of disease pathophysiology and clinical heterogeneity, novel tools are needed. In this study, we explore the potential of untargeted metabolomics on dried blood spots and report for the first time a metabolic fingerprint for DBA. Aims: Defining a metabolic signature for DBA in order to: 1. Increase our understanding of cellular determinants of impaired ribosome biogenesis, and 2. Extend the toolbox for diagnostic evaluation. Methods: Untargeted metabolic profiling was performed on DBS samples obtained from 18 DBA patients and 45 healthy controls using direct infusion high resolution mass spectrometry following a previously established approach1. Statistical analysis was performed in MetaboAnalyst and predictive modeling was executed within R-software. Results: In total, 1917 unique metabolite features were identified in DBS samples from patients and controls. Multivariate analysis yielded distinct metabolic profiles, reflected by natural separation detected by principal component analysis (Figure 1A), and emphasized by clear distinction with partial least square discriminant analysis (Figure 1B). This 'metabolic fingerprint' was incorporated into a machine learning algorithm, and subsequently a binary classification (or prediction) model was constructed by randomly dividing patient and controls into 'training' (32 HC, 13 DBA) or 'test' set (13 HC, 5 DBA). Accurate class assignment was achieved for all patients and controls in the training set (Figure 1C). Prominent metabolites in the fingerprint and classification algorithm were a.o. menadione (a vitamin K precursor), 4-hydroxyproline (collagen component) and methylmalonylcarnitine (an acylcarnitine). In addition a large number of interesting metabolites were identified that could provide novel starting points for studying/understanding downstream effects of RP defects in DBA and therapeutic mechanisms (Figure 1D). Conclusion: In this study we performed untargeted metabolomics on dried blood spots from a substantial cohort of DBA patients and report for the first time a metabolic fingerprint of this disease. By incorporating this fingerprint in a machine learning algorithm we underlined the diagnostic potential of this approach. Moreover, the metabolites identified in this fingerprint, provide promising starting points for further studies to increase our insights in disease pathophysiology, including the mechanism involved in elevated eADA activity, as well as the development of new therapeutic strategies.

Ziziphus joazeiro, a Saponin-Rich Brazilian Medicinal Plant: Pharmacognostic Characterization of Bark and Leaves

Ziziphus joazeiro Mart., Rhamnaceae, is a plant native to northeast Brazil where it is known as “juazeiro.” Its bark, rich in saponins, is widely utilized in folk medicine and as a supply to produce cosmetics. Aiming at the preservation of the species and the search for an alternative vegetal raw material, the phytochemical profiles of bark and leaves were compared by chemical techniques. The foaming index and gravimetric studies indicated that, in general, bark values were approximately twice than those of the leaves. The same occurred with the hemolysis indexes. Extracts were dereplicated by liquid chromatography with diode array detection coupled to mass spectrometry analyses using an atmospheric pressure chemical ionization source, which indicated that at least 5 saponins were common to all bark and leaf samples. The direct infusion high-resolution mass spectrometry analyses revealed at least 44 different saponins distributed between the bark and leaves of the analyzed plant, of which 40 were described for the first time. The data showed that there were similarities between the studied plant organs as demonstrated by mass spectrometry, foaming index, and hemolysis indexes. Therefore, with the application of a suitable standardization procedure the leaves could be a promising source for obtaining “juazeiro-type” saponins.

Rapid determination of pharmaceuticals in wastewater by direct infusion HRMS using target and suspect screening analysis

A wide-scope screening of active pharmaceutical ingredients (APIs) and their transformation products (TPs) in wastewater can yield valuable insights and pinpoint emerging contaminants that have not been previously reported. Such information is relevant to investigate their occurrence and fate in various environmental compartments. In this study, we explored the applicability of direct infusion high resolution mass spectrometry (DI-HRMS) for comprehensive and rapid detection of APIs and their TPs in wastewater samples. The method was developed using a Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) system and incorporated both wide-scope suspect screening and semi-quantitative determination of selected analytes. The identification strategy was based on the following criteria: narrow accurate mass window (±1.25 ppm) for two most abundant full-MS signals, isotopic pattern fit and additional confirmation on the basis of MS2 spectra at three fragmentation levels. The tentative identification of suspects and target compounds relied on an in-house database containing more than 500 different APIs and TPs. The measured fragment spectra were matched against experimental MS2 patterns obtained from a publicly available spectral library (MassBank of North America) and in-silico generated fragmentation features (from the CFM-ID algorithm). In total, 79 suspects were identified and 24 target compounds were semi-quantified in 72 wastewater samples. The highest detection frequencies in treated wastewater effluents were observed for diclofenac, metoprolol and telmisartan, while hydroxydiclofenac, dextrorphan, and carbamazepine metabolites were the most frequently detected TPs. The obtained API profiles were in accordance with the national consumption statistics and the origin of wastewater samples. The developed method is suitable for rapid screening of APIs in wastewater and can be used as a complementary tool to characterize API emissions from wastewater treatment facilities and to identify problematic compounds that require more rigorous monitoring.

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.

Understanding acute metabolic decompensation in propionic and methylmalonic acidemias: a deep metabolic phenotyping approach

BackgroundPathophysiology of life-threatening acute metabolic decompensations (AMD) in propionic acidemia (PA) and isolated methylmalonic acidemia (MMA) is insufficiently understood. Here, we study the metabolomes of PA and MMA patients over time, to improve insight in which biochemical processes are at play during AMD.MethodsLongitudinal data from clinical chemistry analyses and metabolic assays over the life-course of 11 PA and 13 MMA patients were studied retrospectively. Direct-infusion high-resolution mass spectrometry was performed on 234 and 154 remnant dried blood spot and plasma samples of PA and MMA patients, respectively. In addition, a systematic literature search was performed on reported biomarkers. All results were integrated in an assessment of biochemical processes at play during AMD.ResultsWe confirmed many of the metabolite alterations reported in literature, including increases of plasma valine and isoleucine during AMD in PA patients. We revealed that plasma leucine and phenylalanine, and urinary pyruvic acid were increased during AMD in PA patients. 3-hydroxyisovaleric acid correlated positively with plasma ammonia. We found that known diagnostic biomarkers were not significantly further increased, while intermediates of the branched-chain amino acid (BCAA) degradation pathway were significantly increased during AMD.ConclusionsWe revealed that during AMD in PA and MMA, BCAA and BCAA intermediates accumulate, while known diagnostic biomarkers remain essentially unaltered. This implies that these acidic BCAA intermediates are responsible for metabolic acidosis. Based on this, we suggest to measure plasma 3-hydroxyisovaleric acid and urinary ketones or 3-hydroxybutyric acid for the biochemical follow-up of a patient’s metabolic stability.

Untargeted characterisation of dissolved organic matter contributions to rivers from anthropogenic point sources using direct-infusion and high-performance liquid chromatography/Orbitrap mass spectrometry.

RationaleAnthropogenic organic inputs to freshwaters can exert detrimental effects on aquatic ecosystems, raising growing concern for both environmental conservation and water security. Current regulation by the EU water framework directive (European Union, 2000/60/EC) relates to organic pollution by monitoring selected micropollutants; however, aquatic ecosystem responses require a comprehensive understanding of dissolved organic matter (DOM) composition. The introduction of high‐resolution mass spectrometry (HRMS) is set to greatly increase our understanding of the composition of DOM of both natural and anthropogenic origin derived from diffuse and point sources.MethodsDOM was extracted from riverine and treated sewage effluent using solid‐phase extraction (SPE) and analysed using dissolved organic carbon analysis, direct‐infusion high‐resolution mass spectrometry (DI‐HRMS) and high‐performance liquid chromatography (HPLC)/HRMS. The data obtained were analysed using univariate and multivariate statistics to demonstrate differences in background DOM, anthropogenic inputs and in‐river mixing. Compound identifications were achieved based on MS2 spectra searched against on‐line databases.ResultsDI‐HRMS spectra showed the highly complex nature of all DOM SPE extracts. Classification and visualisation of extracts containing many thousands of individual compounds were achieved using principal component analysis (PCA) and hierarchical cluster analysis. Kruskal‐Wallis analyses highlighted significant discriminating ions originating from the sewage treatment works for more in‐depth investigation by HPLC/HRMS. The generation of MS2 spectra in HPLC/HRMS provided the basis for identification of anthropogenic compounds including; pharmaceuticals, illicit drugs, metabolites and oligomers, although many thousands of compounds remain unidentified.ConclusionsThis new approach enables comprehensive analysis of DOM in extracts without any preconceived ideas of the compounds which may be present. This approach has the potential to be used as a high throughput, qualitative, screening method to determine if the composition of point sources differs from that of the receiving water bodies, providing a new approach to the identification of hitherto unrecognised organic contribution to water bodies.

Prediction of VLCAD deficiency phenotype by a metabolic fingerprint in newborn screening bloodspots

PurposeNewborns who test positive for very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) in newborn screening may have a severe phenotype with early onset of life-threatening symptoms but may also have an attenuated phenotype and never become symptomatic. The objective of this study is to investigate whether metabolomic profiles in dried bloodspots (DBS) of newborns allow early phenotypic prediction, permitting tailored treatment and follow-up. MethodsA metabolic fingerprint was generated by direct infusion high resolution mass spectrometry in DBS of VLCADD patients (n = 15) and matched controls. Multivariate analysis of the metabolomic profiles was applied to differentiate subgroups. ResultsConcentration of six acylcarnitine species differed significantly between patients and controls. The concentration of C18:2- and C20:0-carnitine, 13,14-dihydroretinol and deoxycytidine monophosphate allowed separation between mild and severe patients. Two patients who could not be prognosticated on early clinical symptoms, were correctly fitted for severity in the score plot based on the untargeted metabolomics. ConclusionDistinctive metabolomic profiles in DBS of newborns with VLCADD may allow phenotypic prognostication. The full potential of this approach as well as the underlying biochemical mechanisms need further investigation.

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy.