Feasibility of detecting prostate cancer by ultraperformance liquid chromatography-mass spectrometry serum metabolomics.

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

Prebiotic phosphate ester syntheses in a deep eutectic solvent.

We report a route to synthesize a wide range of organophosphates of biological significance in a deep eutectic solvent (2:1 urea and choline chloride), utilizing various orthophosphate sources. Heating an organic alcohol in the solvent along with a soluble phosphorus source yields phosphorus esters of choline as well as that of the added organic in yields between 15 to 99 %. In addition, phosphite analogs of biological phosphates and peptides were also formed by the simple mixing of reagents and heating at 60-70 °C in the deep eutectic solvent. The presented dehydration reactions are relevant to prebiotic and green chemistry in alternative solvents.

A plausible simultaneous synthesis of amino acids and simple peptides on the primordial Earth.

Following his seminal work in 1953, Stanley Miller conducted an experiment in 1958 to study the polymerization of amino acids under simulated early Earth conditions. In the experiment, Miller sparked a gas mixture of CH4, NH3, and H2O, while intermittently adding the plausible prebiotic condensing reagent cyanamide. For unknown reasons, an analysis of the samples was not reported. We analyzed the archived samples for amino acids, dipeptides, and diketopiperazines by liquid chromatography, ion mobility spectrometry, and mass spectrometry. A dozen amino acids, 10 glycine-containing dipeptides, and 3 glycine-containing diketopiperazines were detected. Miller's experiment was repeated and similar polymerization products were observed. Aqueous heating experiments indicate that Strecker synthesis intermediates play a key role in facilitating polymerization. These results highlight the potential importance of condensing reagents in generating diversity within the prebiotic chemical inventory.

Ion mobility and liquid chromatography/mass spectrometry strategies for exhaled breath condensate glucose quantitation in cystic fibrosis studies.

RATIONALE: Cystic fibrosis related diabetes (CFRD) is an important complication of cystic fibrosis (CF) because it causes acceleration in the decline in lung function. Monitoring concentrations of key metabolites such as glucose in airway lining fluid is necessary for improving our understanding of the biochemical mechanisms linking diabetes and CF. Targeted-metabolomic strategies for glucose quantitation in exhaled breath condensate (EBC) from healthy individuals are presented. METHODS: Three different electrospray ionization mass spectrometry (ESI-MS)-based methods were developed for EBC sample interrogation and glucose quantitation without derivatization. Two methods utilized ultra-high-performance liquid chromatography (UHPLC) coupled to either time-of-flight (TOF) MS or triple quadrupole (QqQ) tandem MS (MS/MS). A third approach involved direct-infusion traveling wave ion mobility spectrometry (TWIMS) with TOF-MS detection. UHPLC/QqQ-MS/MS was used for urea quantitation as the EBC dilution marker. Matrix effects were mitigated using isotopically labeled glucose and urea as internal standards. RESULTS: All the developed methods allowed glucose and urea quantitation in EBC with high accuracy and precision. The UHPLC/TOF-MS and UHPLC/QqQ-MS/MS methods provided similar analytical figures of merit. UHPLC/QqQ-MS/MS provided the highest sensitivity and the lowest limit of detection (LOD) of 1.5 nM in EBC for both glucose and urea. The TWIMS-TOF-MS-based method provided the highest sample throughput capability; however, the glucose LOD was ~3-fold higher than with the two chromatographic methods. CONCLUSIONS: Mass spectrometric methods for the quantitative analysis of trace EBC glucose levels are reported and compared for the first time. The analytical figures of merit demonstrate the applicability of these methods to metabolite analysis of airway samples for CF and CFRD research.

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines.

BACKGROUND: The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease. RESULTS: In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM. CONCLUSION: Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.

7.87 eV laser desorption postionization mass spectrometry of adsorbed and covalently bound bisphenol A diglycidyl methacrylate.

Bisphenol A diglycidyl methacrylate (Bis-GMA) was adsorbed onto or covalently bound to a porous silicon oxide surface. Laser desorption 10.5 eV postionization mass spectrometry (LDPI-MS) was previously demonstrated for surface analysis of adsorbed and surface bound Bis-GMA, but signal to noise levels were low and ion fragmentation was extensive. 7.87 eV postionization using the fluorine laser was demonstrated here for Bis-GMA. However, signal levels remained low for LDPI-MS of Bis-GMA as its ionization potential (IP) was only approximately 7.8 eV, near threshold for single photon ionization by the 7.87 eV fluorine laser. It is known that aromatic tagging of molecular analytes can lower the overall IP of the tagged molecular complex, allowing 7.87 eV single photon ionization. Therefore, Bis-GMA was also derivatized with several tags whose IPs were either below or above 7.87 eV: the tag with an IP below 7.87 eV enhanced single photon ionization while the tags with higher IPs did not. However, signal intensities were enhanced by resonant laser desorption for two of the derivatized Bis-GMAs. Intact ions of Bis-GMA and its derivatives were generally observed by 7.87 eV LDPI-MS, consistent with the formation of ions with relatively little internal energy upon threshold single photon ionization.

Organic overlayer model of a dental composite analyzed by laser desorption postionization mass spectrometry and photoemission.

Some dental composites consist of a polymerizable resin matrix bound to glass filler particles by silane coupling agents. The resin in these composites includes bisphenol A diglycidyl methacrylate (Bis-GMA) as well as other organic components. Silane coupling agents such as 3-(trimethoxysilyl) propyl methacrylate (MPS) have been used to improve the mechanical properties of the dental composites by forming a covalent bond between the glass filler particles and the resin. These resin-glass composites undergo material property changes during exposure to the oral environment, but degradation studies of the commercial composites are severely limited by their chemical complexity. A simplified model of the dental composite has been developed, which captures the essential chemical characteristics of the filler particle-silane-resin interface. This model system consists of the resin matrix compound Bis-GMA covalently bound via a methacryloyl overlayer to amorphous silicon oxide (SiO2) surface via a siloxane bond. Scanning electron microscopy shows the porous characteristic and elemental composition of the SiO2 film, which approximately mimics that of the glass filler particles used in dental composites. LDPI MS and XPS verify the chemistry and morphology of the Bis-GMA-methacryloyl overlayer. Preliminary results demonstrate that LDPI MS will be able to follow the chemical processes resulting from aging Bis-GMA-methacryloyl overlayers aged in water, artificial saliva, or other aging solutions.

Barium and strontium leaching from aged glass particle/resin matrix dental composites.

OBJECTIVES: This study characterizes the loss of Ba and Sr from glass particle/resin-matrix dental composites during simulated aging. METHODS: X-ray wavelength dispersive spectrometry and secondary ion mass spectrometry were used to analyze the Ba and Sr content from the surfaces of three commercial dental composites after aging for 4 and 8 months in humid air, artificial saliva, water, and 50% ethanol. RESULTS: Aging in artificial saliva caused the greatest leaching of Ba or Sr for all the specimens, compared with either lesser or no leaching for aging in ethanol and water. Differences in leaching were observed between the different composites. Composites aged in artificial saliva also picked up elements in the saliva solution and displayed crystallite formation on the surface. Samples aged in ethanol displayed cracking which was not observed for water or artificial saliva. SIGNIFICANCE: Dental composites display ion leaching from their surfaces over periods of four to eight months. Three mechanisms are proposed to explain differences in leaching for the various composites and aging solutions. Surface mineralization is also proposed to occur as a self-repair mechanism in artificial saliva.

A tiered analytical approach for investigating poor quality emergency contraceptives.

Reproductive health has been deleteriously affected by poor quality medicines. Emergency contraceptive pills (ECPs) are an important birth control method that women can use after unprotected coitus for reducing the risk of pregnancy. In response to the detection of poor quality ECPs commercially available in the Peruvian market we developed a tiered multi-platform analytical strategy. In a survey to assess ECP medicine quality in Peru, 7 out of 25 different batches showed inadequate release of levonorgestrel by dissolution testing or improper amounts of active ingredient. One batch was found to contain a wrong active ingredient, with no detectable levonorgestrel. By combining ultrahigh performance liquid chromatography-ion mobility spectrometry-mass spectrometry (UHPLC-IMS-MS) and direct analysis in real time MS (DART-MS) the unknown compound was identified as the antibiotic sulfamethoxazole. Quantitation by UHPLC-triple quadrupole tandem MS (QqQ-MS/MS) indicated that the wrong ingredient was present in the ECP sample at levels which could have significant physiological effects. Further chemical characterization of the poor quality ECP samples included the identification of the excipients by 2D Diffusion-Ordered Nuclear Magnetic Resonance Spectroscopy (DOSY 1H NMR) indicating the presence of lactose and magnesium stearate.

Conducting miller-urey experiments.

In 1953, Stanley Miller reported the production of biomolecules from simple gaseous starting materials, using an apparatus constructed to simulate the primordial Earth's atmosphere-ocean system. Miller introduced 200 ml of water, 100 mmHg of H2, 200 mmHg of CH4, and 200 mmHg of NH3 into the apparatus, then subjected this mixture, under reflux, to an electric discharge for a week, while the water was simultaneously heated. The purpose of this manuscript is to provide the reader with a general experimental protocol that can be used to conduct a Miller-Urey type spark discharge experiment, using a simplified 3 L reaction flask. Since the experiment involves exposing inflammable gases to a high voltage electric discharge, it is worth highlighting important steps that reduce the risk of explosion. The general procedures described in this work can be extrapolated to design and conduct a wide variety of electric discharge experiments simulating primitive planetary environments.

Biomarkers of whale shark health: a metabolomic approach.

In a search for biomarkers of health in whale sharks and as exploration of metabolomics as a modern tool for understanding animal physiology, the metabolite composition of serum in six whale sharks (Rhincodon typus) from an aquarium collection was explored using (1)H nuclear magnetic resonance (NMR) spectroscopy and direct analysis in real time (DART) mass spectrometry (MS). Principal components analysis (PCA) of spectral data showed that individual animals could be resolved based on the metabolite composition of their serum and that two unhealthy individuals could be discriminated from the remaining healthy animals. The major difference between healthy and unhealthy individuals was the concentration of homarine, here reported for the first time in an elasmobranch, which was present at substantially lower concentrations in unhealthy whale sharks, suggesting that this metabolite may be a useful biomarker of health status in this species. The function(s) of homarine in sharks remain uncertain but it likely plays a significant role as an osmolyte. The presence of trimethylamine oxide (TMAO), another well-known protective osmolyte of elasmobranchs, at 0.1-0.3 mol L(-1) was also confirmed using both NMR and MS. Twenty-three additional potential biomarkers were identified based on significant differences in the frequency of their occurrence between samples from healthy and unhealthy animals, as detected by DART MS. Overall, NMR and MS provided complementary data that showed that metabolomics is a useful approach for biomarker prospecting in poorly studied species like elasmobranchs.

Optimization of a direct analysis in real time/time-of-flight mass spectrometry method for rapid serum metabolomic fingerprinting.

Metabolomic fingerprinting of bodily fluids can reveal the underlying causes of metabolic disorders associated with many diseases, and has thus been recognized as a potential tool for disease diagnosis and prognosis following therapy. Here we report a rapid approach in which direct analysis in real time (DART) coupled with time-of-flight (TOF) mass spectrometry (MS) and hybrid quadrupole TOF (Q-TOF) MS is used as a means for metabolomic fingerprinting of human serum. In this approach, serum samples are first treated to precipitate proteins, and the volatility of the remaining metabolites increased by derivatization, followed by DART MS analysis. Maximum DART MS performance was obtained by optimizing instrumental parameters such as ionizing gas temperature and flow rate for the analysis of identical aliquots of a healthy human serum samples. These variables were observed to have a significant effect on the overall mass range of the metabolites detected as well as the signal-to-noise ratios in DART mass spectra. Each DART run requires only 1.2 min, during which more than 1500 different spectral features are observed in a time-dependent fashion. A repeatability of 4.1% to 4.5% was obtained for the total ion signal using a manual sampling arm. With the appealing features of high-throughput, lack of memory effects, and simplicity, DART MS has shown potential to become an invaluable tool for metabolomic fingerprinting.

Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy.

BACKGROUND: Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer. METHODS: We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations. RESULTS: The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations). CONCLUSION: The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (approximately 0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment. IMPACT: The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome.

Serum biomarker profiling by solid-phase extraction with particle-embedded micro tips and matrix-assisted laser desorption/ionization mass spectrometry.

One of the main challenges in high-throughput serum profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is the development of proteome fractionation approaches that allow the acquisition of reproducible profiles with a maximum number of spectral features and minimum interferences from biological matrices. This study evaluates a new class of solid-phase extraction (SPE) pipette tips embedded with different chromatographic media for fractionation of model protein digests and serum samples. The materials embedded include strong anion exchange (SAX), weak cation exchange (WCX), C18, C8, C4, immobilized metal affinity chromatography (IMAC) and zirconium dioxide particles. Simple and rapid serum proteome profiling protocols based on these SPE micro tips are described and tested using a variety of MALDI matrices. We show that different types of particle-embedded SPE micro tips provide complementary information in terms of the spectral features detected for beta-casein digests and control human serum samples. The effect of different sample pretreatments, such as serum dilution and ultrafiltration using molecular weight cut-off membranes, and the reproducibility observed for replicate experiments, are also evaluated. The results demonstrate the usefulness of these simple SPE tips combined with offline MALDI-TOF MS for obtaining information-rich serum profiles, resulting in a robust, versatile and reproducible open-source platform for serum biomarker discovery.