Gas Chromatography-mass Spectrometry Platform Research Articles

Abstract 892: Application of iterative sureindependence screening to improve urinary metabolomics-based prediction of survival in colorectal cancer patients

Abstract Objective: High-throughput metabolomics assays can generate thousands of biomarker measurements and provide novel opportunities for prognostic modeling in colorectal cancer (CRC) research. The high dimensionality of metabolic data brings unforeseen statistical challenges and traditional variable selection methods may not perform well due to simultaneous challenges of computational expediency, statistical accuracy, and algorithmic stability. The Iterative Sure Independence Screening (ISIS) has demonstrated superior theoretical properties in handling such situations, and may be a viable alternative. Methods: In a prospective study of 77 newly diagnosed CRC patients (stage I-IV), pre-surgical urinary samples were analyzed on a gas chromatography-mass spectrometry platform. After exclusion of metabolites with >30% coefficient of variation, 168 metabolites remained for statistical analysis. Raw measures were processed following a standard normalization pipeline. The primary outcome was overall survival (OS) as measured from date of cancer diagnosis. In addition to metabolomics data, the predictor set included baseline clinical characteristics, such as age, sex, body mass index, tumor site, tumor stage, and receipt of neo-adjuvant and/or adjuvant treatment. We applied the ISIS method with Lasso penalty on a Cox regression model (ISIS-Lasso) to identify features associated with OS. Cox models with either Lasso regularization or backward selection were also considered as competing methods. The performance of the models was assessed through two standard performance matrices: Uno's time-dependent Area Under the Curve (tAUC) and Brier's score, both with resample-based validation. Results: Based on bootstrapped tAUC curves, we demonstrated that the screening step in ISIS can significantly improve model performance, since its predicted mean (and median) AUC are larger across clinically relevant follow-up time points (2 and 5 years after diagnosis) relative to the corresponding measures from the other two models. The prediction error based on Brier's score with 0.632+ bootstrap from ISIS-Lasso was also noticeably lower compared to the model from backward selections. Based on the ISIS-Lasso model, we identified two features, that were predictive of OS in CRC patients: tumor stage and cystine. When fixing all other clinical measures, patients with early stage (I-III) had 52% lower risk of death, compared to late stage (IV); 1 standard deviation of increase of cystine level was associated with 62% increased risk of death. Conclusion: We have demonstrated the feasibility and effectiveness of an ISIS-based method to improve selection of prognostic models derived from metabolomics data. This may be especially useful for studies with moderate sample sizes. We have identified cystine as a potentially important prognostic biomarker. Citation Format: Tengda Lin, Biljana Gigic, Kenneth Boucher, Ben Haaland, David Liesenfeld, Robert Owen, Petra Schrotz-King, Jourgen Boehm, Anita Peoples, Augustin Scalbert, Martin Schneider, Jane Figueiredo, William Grady, Christopher Li, David Shibata, Erin Siegel, Adetunji Toriola, Alexis Ulrich, Neli Ulrich, Jincheng Shen, Jennifer Ose. Application of iterative sureindependence screening to improve urinary metabolomics-based prediction of survival in colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 892.

Gut Microbiota Combined With Metabolomics Reveals the Repeated Dose Oral Toxicity of β-Cyclodextrin in Mice.

Βeta-cyclodextrin (β-CD) with a hydrophobic cavity enables the formation of inclusion complexes with organic molecules. The formation of host–guest complexes makes the application of β-CD popular in many fields, but their interaction with organisms is poorly understood. In the present study, the effect of β-CD on gut microbiota (16S rRNA gene sequencing), serum metabolites (gas chromatography–mass spectrometry platform), and their correlation (Pearson correlation analysis) was investigated after 14 days repeated oral exposure in mice. β-CD did not significantly affect the α-diversity indexes, including Richness, Chao1, Shannon and Simpson indexes, but disturbed the structure of the gut bacteria according to the result of principal component analysis (PCA). After taxonomic assignment, 1 in 27 phyla, 2 in 48 classes, 3 in 107 orders, 6 in 192 families, and 8 in 332 genera were significantly different between control and β-CD treated groups. The serum metabolites were significantly changed after β-CD treatment according to the result of unsupervized PCA and supervised partial least squares-discriminant analysis (PLS-DA). A total of 112 differential metabolites (89 downregulated and 23 upregulated) were identified based on the VIP >1 from orthogonal PLS-DA and p <0.05 from Student’s t-test. The metabolic pathways, including ABC transporters, pyrimidine metabolism, purine metabolism, glucagon signaling pathway, insulin signaling pathway, and glycolysis/gluconeogenesis, were enriched by KEGG pathway analysis. Our study provides a general observation of gut microbiota, serum metabolites and their correlation after exposure to β-CD in mice, which will be helpful for future research and application of β-CD.

A metabolic study to decipher amino acid catabolism-directed biofuel synthesis in Acetoanaerobium sticklandii DSM 519

Acetoanaerobium sticklandii DSM 519 is a hyper-ammonia-producing anaerobe. It has the ability to produce organic solvents and acids from protein catabolism through Stickland reactions and specialized pathways. Nevertheless, its protein catabolism-directed biofuel production has not yet been understood. The present study aimed to decipher such growth-associated metabolic potential of this organism at different growth phases using metabolic profiling. A seed culture of this organism was grown separately in metabolic assay media supplemented with gelatin and or a mixture of amino acids. The extracellular metabolites produced by this organism were qualitatively analyzed by gas chromatography-mass spectrometry platform. The residual amino acids after protein degradation and amino acids assimilation were identified and quantitatively measured by high-performance liquid chromatography (HPLC). Organic solvents and acids produced by this organism were detected and the quantity of them determined with HPLC. Metabolic profiling data confirmed the presence of amino acid catabolic products including tyramine, cadaverine, methylamine, and putrescine in fermented broth. It also found products including short-chain fatty acids and organic solvents of the Stickland reactions. It reported that amino acids were more appropriate for its growth yield compared to gelatin. Results of quantitative analysis of amino acids indicated that many amino acids either from gelatin or amino acid mixture were catabolised at a log-growth phase. Glycine and proline were poorly consumed in all growth phases. This study revealed that apart from Stickland reactions, a specialized system was established in A. sticklandii for protein catabolism-directed biofuel production. Acetone-butanol-ethanol (ABE), acetic acid, and butyric acid were the most important biofuel components produced by this organism. The production of these components was achieved much more on gelatin than amino acids. Thus, A. sticklandii is suggested herein as a potential organism to produce butyric acid along with ABE from protein-based wastes (gelatin) in bio-energy sectors.

Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/ gas chromatography mass spectrometry platform

Lung cancer is one of the ten most common causes of death worldwide, so that the search for early diagnosis biomarkers is a very challenging task. Bronchoalveolar lavage fluid (BALF) provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Therefore, this fluid has been explored looking for new contributions in lung cancer metabolism. In this way, two complementary metabolomics techniques based on direct infusion high resolution mass spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC–MS) have been applied to compare statistically differences between lung cancer (LC) and control (C) BALF samples, using partial least square discriminant analysis (PLS-DA) in order to find and identify potential biomarkers of the disease. A total of 42 altered metabolites were found in BALF from LC. The metabolic pathway analysis showed that glutamate and glutamine metabolism pathway was mainly altered by this disease. In addition, we assessed the biomarker specificity and sensitivity according to the area under the receiver operator characteristic (ROC) curves, indicating that glycerol and phosphoric acid were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. Biological significanceThe search for early diagnosis of lung cancer is a very challenging task because of the high mortality associated to this disease and its critical linkage to the initiation of treatment. Bronchoalveolar lavage fluid provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Since BALF is in close interaction with lung tissue it is a more representative sample of lung status than other peripheral biofluids as blood or urine studied in previous works. Therefore, this study represents an innovative contribution in this topic that complement previous investigations about lung cancer, opening up new possibilities for understanding the pathogenesis of this disease and the use of efficient biomarkers.Therefore, this fluid has been explored looking for new contributions in lung cancer metabolism. In this way, two complementary metabolomic techniques based on direct infusion high resolution mass spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC–MS) have been applied to compare statistically significant differences between lung cancer (LC) and control (C) BALF samples, using partial least square discriminant analysis (PLS-DA) in order to find and identify potential biomarkers of the disease. A total of 42 altered metabolites were found in BALF from LC. The metabolic pathway analysis showed that glutamate and glutamine metabolism pathway was mainly altered by this disease. In addition, we assessed the biomarker specificity and sensitivity according to the area under the receiver operator characteristic (ROC) curves, indicating that glycerol and phosphoric acid were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis.

Maternal Early Pregnancy Serum Metabolites and Risk of Gestational Diabetes Mellitus.

Significant gaps remain in the understanding of genetic and environmental risk factors, as well as related mechanisms that contribute to gestational diabetes mellitus (GDM). This study aimed to investigate early pregnancy maternal serum metabolites and subsequent risk of GDM. Information on participant characteristics and GDM diagnosis was collected using in-person interviews and medical record abstraction, respectively. Early pregnancy serum samples were used for nontargeted metabolite profiling using a gas chromatography-mass spectrometry platform. Lasso regression was used to select a set of metabolites that are jointly associated with GDM case-control status. We evaluated the predictive performance of the set of selected metabolites using a receiver operating characteristics curve and area under the curve. A total of 178 GDM cases and 180 controls participated in a pregnancy cohort study. A set of 17 metabolites (linoleic acid, oleic acid, myristic acid, d-galactose, d-sorbitol, o-phosphocolamine, l-alanine, l-valine, 5-hydroxy-l-tryptophan, l-serine, sarcosine, l-pyroglutamic acid, l-mimosine, l-lactic acid, glycolic acid, fumaric acid, and urea) differentiated GDM cases from controls. Fold changes of relative abundance of these metabolites among GDM cases compared with controls ranged from 1.47 (linoleic acid) to 0.78 (5-hydroxy-l-tryptophan). Addition of these selected metabolites to a set of well-known GDM risk factors improved the area under the curve significantly from 0.71 to 0.87 (P = 3.97E-07). We identified combinations of metabolites in early pregnancy that are associated with subsequent risk of GDM. Replication of findings may improve understanding of GDM pathogenesis and may have implications for the design of GDM prevention and early diagnosis protocols.

Clinical Validation of a Highly Sensitive GC-MS Platform for Routine Urine Drug Screening and Real-Time Reporting of up to 212 Drugs

An important role of the clinical toxicology laboratory is to provide continuous diagnostic testing for patients with altered mental status and for other medical indications. To meet these needs, we have developed a new Gas Chromatography-Mass Spectrometry (GC-MS) platform that facilitates routine screening and automated reporting of 212 drugs by laboratory technologists around the clock without the need to sign out by an on-site mass spectrometry-trained toxicologist. The platform uses a programmable temperature vaporizer (PTV) injector for large sample volume injection and the free software Automated Mass Spectral Deconvolution and Identification System (AMDIS) for data reduction and spectral matching that facilitates rapid library searching and analyte identification. Method comparison with 118 patient samples demonstrated that this platform and data searching algorithm independently provided improvements in sensitivity compared to an established GC-MS platform. Further examination of the role of the data processing software and the in-house databases used in the established versus the new platform demonstrated that the improved analytical sensitivity of the new platform was attributed to both the technical superiority of the new GC-MS instrumentation and the use of AMDIS in conjunction with the newly generated in-house library for data processing.

Random forest in clinical metabolomics for phenotypic discrimination and biomarker selection.

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools. In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation, R 2/Q 2 plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed. RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis.

Effects of a Flavonoid-Rich Juice on Inflammation, Oxidative Stress, and Immunity in Elite Swimmers: A Metabolomics-Based Approach

The effects of a flavonoid-rich fresh fruit and vegetable juice (JUICE) on chronic resting and postexercise inflammation, oxidative stress, immune function, and metabolic profiles (metabolomics analysis, gas-chromatography mass-spectrometry platform) in elite sprint and middle-distance swimmers were studied. In a randomized, crossover design with a 3-wk washout period, swimmers (n = 9) completed 10-d training with or without 16 fl oz of JUICE (230 mg flavonoids) ingested pre- and postworkout. Blood samples were taken presupplementation, post-10-d supplementation, and immediately postexercise, with data analyzed using a 2 × 3 repeated-measures ANOVA. Prestudy blood samples were also acquired from nonathletic controls (n = 7, age- and weight-matched) and revealed higher levels of oxidative stress in the swimmers, no differences in inflammation or immune function, and a distinct separation in global metabolic scores (R2Y [cum] = .971). Swim workouts consisted of high-intensity intervals (1:1, 1:2 swim-to-rest ratio) and induced little inflammation, oxidative stress, or immune changes. A distinct separation in global metabolic scores was found pre- to postexercise (R2Y [cum] = .976), with shifts detected in a small number of metabolites related to substrate utilization. No effect of 10-d JUICE was found on chronic resting levels or postexercise inflammation, oxidative stress, immune function, and shifts in metabolites. In conclusion, sprint and middle-distance swimmers had a slight chronic elevation in oxidative stress compared with nonathletic controls, experienced a low magnitude of postworkout perturbations in the biomarkers included in this study, and received no apparent benefit other than added nutrient intake from ingesting JUICE pre- and postworkout for 10 days.

Effect of genotype, environment and genotype-by-environment interaction on metabolite profiling in durum wheat (Triticum durum Desf.) grain

The aim of this study was to assess the relative roles of genotype, environment and genotype-by-environment interactions in determining the metabolite profile of durum wheat grain. Four durum wheat cultivars were grown under conventional and organic farming systems over three consecutive years. The use of a high-throughput gas chromatography–mass spectrometry platform allowed the analysis of sets of different polar and non-polar compounds, including amino acids, sugars, organic acids, fatty acids (saturated and unsaturated), and sterols. Statistical analysis of the data showed a small impact of genotype and large effects of both year and genotype-by-environment interaction on the metabolite composition and quality of the wheat grain. Overall, the data from this study highlight the potential role of metabolic profiling in the analysis of durum wheat quality and production.

Extracellular metabolomics: A metabolic footprinting approach to assess fiber degradation in complex media

This work reports the implementation and optimization of a method for high-throughput analysis of metabolites produced by the breakdown of natural polysaccharides by microorganisms. Our simple protocol enables simultaneous separation and quantification of more than 40 different sugars and sugar derivatives, in addition to several organic acids in complex media, using 50-μl samples and a standard gas chromatography–mass spectrometry platform that was fully optimized for this purpose. As an implementation proof-of-concept, we assayed extracellular metabolite levels of three bacterial strains cultivated on complex medium rich in polysaccharides and under identical growth conditions. We demonstrate that the metabolic footprinting profile data distinguish among sample types such as typical metabolomics data. Moreover, we demonstrate that the differential metabolite-level data provide insight on specific fibrolytic activity of the different microbial strains and lay the groundwork for integrated proteome–metabolome studies of fiber-degrading microorganisms.