Untargeted Metabolomics Studies Research Articles

Gut permeability and cognitive decline: A pilot investigation in the Northern Manhattan Study

BackgroundGut microbiota may impact cognitive function and decline, though data are limited. This pilot study examines the associations between gut dysbiosis products, plasma lipopolysaccharide (LPS) and soluble CD14 (sCD14), with cognitive decline and immune molecule activation among 40 participants in the longitudinal population-based Northern Manhattan Study. MethodsWe selected stroke- and dementia-free participants at baseline with high activation levels of core components of the immune signaling pathways underlying microbiota metabolite-cognitive associations (IL-1, IL-17, TNF). Participants were followed with up to three complete neuropsychological assessments, at least 5 years apart. ResultsElevated sCD14 was associated with high levels of IL-1 pathway activation (p ​< ​0.05), whereas in samples where only those molecules within the IL-17 and TNF pathways were increased, LPS and sCD14 levels were not elevated. LPS was associated with decline in global cognitive performance over 2–3 assessments (adjusted β ​= ​-0.023 per SD per year, 95% CI:-0.036, −0.010). The association between sCD14 and cognitive decline was marginal (adjusted β ​= ​-0.018 per SD per year, 95% CI:-0.040, 0.004). ConclusionsThese preliminary data support the hypothesis that gut dysbiosis leads to systemic and neuro-inflammation, and subsequently cognitive decline. Further large targeted and untargeted gut microbiota-derived metabolomic studies are needed.

Steroid disulfates - Sulfation double trouble

Sulfation pathways have recently come into the focus of biomedical research. For steroid hormones and related compounds, sulfation represents an additional layer of regulation as sulfated steroids are more water-soluble and tend to be biologically less active. For steroid diols, an additional sulfation is possible, carried out by the same sulfotransferases that catalyze the first sulfation step. The steroid disulfates that are formed are the focus of this review. We discuss both their biochemical production as well as their putative biological function. Steroid disulfates have also been linked to various clinical conditions in numerous untargeted metabolomics studies. New analytical techniques exploring the biosynthetic routes of steroid disulfates have led to novel insights, changing our understanding of sulfation in human biology. They promise a bright future for research into sulfation pathways, hopefully too for the diagnosis and treatment of several associated diseases.

Affine Transformation of Negative Values for NMR Metabolomics Using the mrbin R Package

Data from untargeted metabolomics studies employing nuclear magnetic resonance (NMR) spectroscopy oftentimes contain negative values. These negative values hamper data processing and analysis algorithms and prevent the use of such data in multiomics integration settings. New methods to deal with such negative values are thus an urgent need in the metabolomics community. This study presents affine transformation of negative values (ATNV), a novel algorithm for replacement of negative values in NMR data sets. ATNV was implemented in the R package mrbin, which features interactive menus for user-friendly application and is available for free for various operating systems within the free R statistical programming language. The novel algorithms were tested on a set of human urinary NMR spectra and were able to successfully identify relevant metabolites.

Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Possibilities and Perspectives.

Capillary electrophoresis-mass spectrometry (CE-MS) is a very useful analytical technique for the selective and highly efficient profiling of polar and charged metabolites in a wide range of biological samples. Compared to other analytical techniques, the use of CE-MS in metabolomics is relatively low as the approach is still regarded as technically challenging and not reproducible. In this chapter, the possibilities of CE-MS for metabolomics are highlighted with special emphasis on the use of recently developed interfacing designs. The utility of CE-MS for targeted and untargeted metabolomics studies is demonstrated by discussing representative and recent examples in the biomedical and clinical fields. The potential of CE-MS for large-scale and quantitative metabolomics studies is also addressed. Finally, some general conclusions and perspectives are given on this stronganalytical separation technique for probing the polar metabolome.

Untargeted Metabolomics Studies on Drug-Incubated Phragmites australis Profiles.

Plants produce a huge number of functionally and chemically different natural products that play an important role in linking the plant with the adjacent environment. Plants can also absorb and transform external organic compounds (xenobiotics). Currently there are only a few studies concerning the effects of xenobiotics and their transformation products on plant metabolites using a mass spectrometric untargeted screening strategy. This study was designed to investigate the changes of the Phragmites australis metabolome following/after diclofenac or carbamazepine incubation, using a serial coupling of reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) combined with accurate high-resolution time-of-flight mass spectrometer (TOF-MS). An untargeted screening strategy of metabolic fingerprints was developed to purposefully compare samples from differently treated P. australis plants, revealing that P. australis responded to each drug differently. When solvents with significantly different polarities were used, the metabolic profiles of P. australis were found to change significantly. For instance, the production of polyphenols (such as quercetin) in the plant increased after diclofenac incubation. Moreover, the pathway of unsaturated organic acids became more prominent, eventually as a reaction to protect the cells against reactive oxygen species (ROS). Hence, P. australis exhibited an adaptive mechanism to cope with each drug. Consequently, the untargeted screening approach is essential for understanding the complex response of plants to xenobiotics.

Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation

Capillary electrophoresis coupled to mass spectrometry is a power tool in untargeted metabolomics studies to analyze charged and polar compounds. However, identification is a challenge due to the variability of migration times and the lack of MS/MS spectra in CE-TOF-MS, the type of instruments most frequently employed.We present here a CE-MS search platform incorporated in CEU Mass Mediator to annotate metabolites with a confidence level L2. For its the development we analyzed 226 compounds using two fragmentor voltages: 100 and 200 V. The information obtained, such as relative migration times (RMT) and in-source fragments, were incorporated into the platform. In addition, we validated the CE-MS search functionality using different types of biological samples such as plasma samples (human, rat, and rabbit), mouse macrophages, and human urine. The RMT tolerance percentage for the search of metabolites has been determined, establishing 5% for all compounds, except for the compounds migrating in the electro-osmotic flow, for which the tolerance should be of 10%.It has also been demonstrated the robustness of the in-source fragmentation, which makes possible the annotation of compounds by means of their fragmentation pattern. As an example, 3-methylhistidine and 1-methilhistidine, whose RMT are very close, have been annotated. Studies of the fragmentation mechanisms of acyl-L-carnitines have shown that in-source fragmentation follows the general fragmentation rules and is a suitable alternative to MS/MS.

TAMI-55. THE EVOLUTIONARY ENIGMA OF FATTY ACID DESATURATION IN GLIOBLASTOMA

Abstract Fatty acid desaturation is an enzymatic reaction in which a double bond is introduced into an acyl chain. Of the four functionally distinct desaturase subfamilies, the First Desaturase Family of enzymes introduce the first double bond into a saturated fatty acid, resulting in the synthesis of monounsaturated fatty acids (MUFA). MUFA are essential components of membrane and storage lipids and exert a profound influence on the fluidity of biological membranes. A disequilibrium in saturated to unsaturated fatty acid ratio alters cell growth, differentiation and response to external stimuli, and thus affects a range of pathologies including cancer. The most abundant and key First Desaturase Family enzyme is the delta 9 desaturate called Stearoyl Co-A Desaturase (SCD and SCD5 in humans, and SCD1-4 in mice). SCD desaturates Stearoyl-CoA (C18) and palmitoyl-CoA (C16) to oleoyl-CoA (C18:1) and palmitoyl-CoA (C16:1), respectively. Besides SCD, the only known First Desaturase in mammals with dual function is FADS2 which desaturates palmitate to Sapienate (C16:1, a positional isomer of palmitoleate) in skin cells. A recent study showed that some cancer cells can use FADS2 to bypass the SCD reaction. SCD and SCD5 are by far the most abundant desaturases expressed in the human brain. We made an unexpected discovery that SCD undergoes monoallelic codeletion with PTEN on chromosome 10, and is also highly methylated in glioblastoma (GBM). More surprisingly, all GBM cell lines with SCD codeletion/methylation (that expressed very little SCD protein) are completely resistant to SCD/SCD5 inhibition, yet their phospholipids contained abundant oleic acid. It is unknown if GBMs bypassed SCD, but retained the delta 9 desaturation reaction through a novel enzymatic activity. Our targeted and untargeted metabolomics studies revealed unexpected findings that cannot be explained by conventional wisdom, and may lead to identification of novel lipogenic targets in GBM.

A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows.

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.

Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color.

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

Optimization and validation of a HILIC-LC-ESI-MS/MS method for the simultaneous analysis of targeted metabolites: Cross validation of untargeted metabolomic studies for early diagnosis of breast cancer

The best preventive method for breast cancer, which accounts for 18% of the most common cancer-related deaths among women, is early diagnosis. Metabolomic analysis plays an important role in both early diagnosis and tracking of the disease progress. In this study, targeted metabolomic analyses were performed on twenty-six selected metabolites, which were proposed in the literature for use in the early diagnosis of breast cancer, by examining the plasma samples of 105 healthy volunteers, 172 early stage and 92 metastatic breast cancer patients. To this purpose, an LC-ESI-MS/MS method was developed, validated, and applied to the analysis of the above plasma samples. Measurements were performed on a Merck SeQuant ZIC-HILIC column (50 × 4.6 mm, 5 µm) with the mobile phase run in the gradient mode with 0.1% formic acid in water and 0.1% formic acid in acetonitrile at a flow rate of 0.3 mL min−1 and 40 °C. The correlation coefficient values between 0.991 and 0.999 in the study range where each metabolite is linear shows the linearity of the method. The LOD and LLOQ values of the metabolites were between 5.9 × 10−5-1.0 × 10−2 μg mL−1 and 1.8 × 10−4-5.3 × 10−2 μg mL−1, respectively. The validation studies revealed that the method was linear, sensitive, precise, accurate, and selective. Our results showed that the metabolite identification based exclusively on the m/z values obtained from untargeted metabolomic studies must conform with standards before their clinical usage. Since only nine (citrulline, malic acid, glyceric acid, 3-hydroxybutyric acid, glutamine, proline, choline, fumaric acid and lactic acid) out of the twenty-six metabolites changed significantly among breast cancer patients. Therefore untargeted metabolomic analyses should be confirmed by targeted analyses before suggesting metabolites as biomarkers to avoid possible mismatches.

Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data.

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches.

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

Charting Metabolism Heterogeneity by Nanostructure Imaging Mass Spectrometry: From Biological Systems to Subcellular Functions

The study of metabolism heterogeneity is essential to understand the role of metabolites in supporting and regulating biological functions. To this end, several mass spectrometry imaging (MSI) approaches have been proposed for the detection of small molecule metabolites. However, high noise from the ionization matrix and low metabolome coverage hinder their applicability for untargeted metabolomics studies across space. In this context, nanostructure imaging (/initiator) mass spectrometry (NIMS) and NIMS with fluorinated gold nanoparticles (f-AuNPs) are attractive strategies for comprehensive MSI of metabolites in biological systems, which can provide heterogeneous metabolome coverage, ultrahigh sensitivity, and high lateral resolution. In particular, NIMS with f-AuNPs permits the simultaneous detection of polar metabolites and lipids in a single and cohesive analytical session, thus allowing the systems-level interpretation of metabolic changes. In this Perspective article, we discuss the use of NIMS and f-AuNPs in the exploration of metabolism heterogeneity and provide a critical outlook on future applications of this technology for revealing the metabolic architecture that supports biological functions in health and disease, from whole organisms to tissues, single cells, and subcellular compartments.

Metabolic Dysfunction and Asthma: Current Perspectives.

The increasing knowledge of the mechanisms involved in metabolism is shifting the paradigms by which the pathophysiology of many pulmonary diseases is understood. Metabolic dysfunction is recognized in obesity-associated asthma, but other metabolic conditions have been shown to be independently related to asthma. Novel insights have also recently been brought by metabolomics in this filed. The purpose of this review is to discuss current perspectives regarding metabolic dysfunction in asthma, from obesity-related asthma to other metabolic conditions and the role of current pharmacological therapeutic strategies and lifestyle interventions. Obesity is a well-recognized risk factor for asthma across the lifespan, which is generally associated with poorer response to current available treatments, rendering a more severe, refractory disease status. Besides the epidemiological and clinical link, untargeted metabolomics studies have recently supported the obesity-associated asthma phenotype at the molecular level. Not only obesity-related, but also other aspects of metabolic dysregulation can be independently linked to asthma. These include hyperinsulinemia, dyslipidemia and hypertension, which need to be taken into account, even in the non-obese patient. Untargeted metabolomics studies have further highlighted several other metabolic pathways that can be altered in asthma, namely regarding oxidative stress and systemic inflammation, and also suggesting the importance of microbiota in asthma pathogenesis. Considering the reduced response to corticosteroids, other pharmacologic treatments have been shown to be effective regardless of body mass index. Non-pharmacologic treatments (namely weight reduction and dietary changes) may bring substantial benefit to the asthmatic patient. Taken together, this evidence points towards the need to improve our knowledge in this filed and, in particular, to address the influence of environmental factors in metabolic dysfunction and asthma development. Personalized medicine is definitely needed to optimize treatment, including a holistic view of the asthmatic patient in order to set accurate pharmacologic therapy together with dietary, physical exercise and lifestyle interventions.

Development and application of an LC-MS/MS method for determining methylsulfonylmethane (MSM) levels in human plasma

Methylsulfonylmethane (MSM) isan organosulfur phytochemical widely used as a dietary supplement carrying structure/function claims that includethe promotion of joint health. Untargeted metabolomics studies using nuclear magnetic resonance (NMR) have identified MSM in plasma and urine. However, MS-based methodology is more suitable for pharmacokinetic studies designed to explore MSM’s concentration-effect relationship for purposes of establishing dosing guidelines. To address this deficiency, an LC-MS/MS method using MSM, deuterated MSM (MSM-d6, internal standard), and liquid-liquid extraction was developed and validated in accordance with international guidelines. The method proved well-suited for determining MSM levels in human plasma following chronic oral administration (1, 2, or 3 grams daily) for four weeks. The availability of a robust LC-MS/MS method and MSM dose linearity data provides a framework for exploring MSM’s concentration-effect relationship to support various structure/function claims.

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

Global untargeted metabolomics (GUM) has entered clinical diagnostics for genetic disorders. We compared the clinical utility of GUM with traditional targeted metabolomics (TM) as a screening tool in patients with established genetic disorders and determined the scope of GUM as a discovery tool in patients with no diagnosis under investigation. We compared TM and GUM data in 226 patients. The first cohort (n = 87) included patients with confirmed inborn errors of metabolism (IEM) and genetic syndromes; the second cohort (n = 139) included patients without diagnosis who were undergoing evaluation for a genetic disorder. In patients with known disorders (n = 87), GUM performed with a sensitivity of 86% (95% CI: 78–91) compared with TM for the detection of 51 diagnostic metabolites. The diagnostic yield of GUM in patients under evaluation with no established diagnosis (n = 139) was 0.7%. GUM successfully detected the majority of diagnostic compounds associated with known IEMs. The diagnostic yield of both targeted and untargeted metabolomics studies is low when assessing patients with non-specific, neurological phenotypes. GUM shows promise as a validation tool for variants of unknown significance in candidate genes in patients with non-specific phenotypes.

Potential quality evaluation approach for the absolute growth years' wild and transplanted Astragali Radix based on anti-heart failure efficacy.

The quality of Astragali Radix (AR) was closely related to the growth period. However, the current commodity grades of AR were only divided by diameter but not directly related to the growth period, which leads to the contradiction between the grade standard and the quality evaluation index. Therefore, solving this problem will be the key for the quality evaluation of AR. The present study established a potential quality evaluation approach for the absolute growth years' wild Astragali Radix (WAR) and transplanted Astragali Radix (TAR) based on the chemical components and anti-heart failure efficacy through adopting a bare-handed sections approach to rapidly identify the growth years of WAR. In this study, the absolute growth years of WAR were obtained by identifying the growth rings of 1-6 growth years root through the methods. The contents of flavonoids and saponins in 2-6 growth years' WAR were determined by HPLC-UV-ELSD. The contents of 12 chemical components and the anti-fatigue failure effects of WAR (4-year-old) and TAR were compared on rat models of heart failure induced by doxorubicin. Meanwhile, NMR-based untargeted metabolomics studies were performed to investigate the regulative effects of WAR and TAR. The result shows that the numbers of growth rings were consistent with the actual growth periods of AR. The HPLC-UV-ELSD determination indicated that the content of total flavonoids in WAR was significantly higher than that in TAR. Pharmacodynamics analysis revealed that the effects of WAR on cardiac function parameters (EF, FS and LVIDs), contents of serum CK and BNP were superior to those of TAR. 13 metabolites of heart were identified that had a higher rate of change in WAR group than TAR. Overall, a rapid identification method for the growth years of WAR was established, and the fact that WAR were significantly better than TAR in the heart failure rats was first proved in the paper. This study provided a scientific basis for establishing a novel commodity specification and grade of AR for clinical rational drug use.

Development and validation of a rapid, robust and sensitive UPLC-QQQ-MS/MS method for simultaneous quantification of GSH metabolism in lung cancer cells

Changes in cellular metabolism accompany tumor therapeutic resistance. Metabolite concentrations specifically reflect the cellular state. Glutathione (GSH) metabolism maintains the redox homeostasis while also confers therapeutic resistance to cancer cells. However, analytical methods for studying GSH metabolism rely on high-resolution-based untargeted metabolomics. Since the aim of untargeted metabolomics studies is covering as much metabolites as possible, these methods lack sensitivity for simultaneous analysis of intracellular GSH-related metabolites with different polarities and structures. In this study, based on cultured lung cancer cells, we described a rapid, robust and sensitive ultra-performance liquid chromatography-triple quadrupole tandem mass spectrographic method (UPLC-QQQ-MS/MS) to simultaneously quantify a repertoire of GSH-related metabolites, including GSH, GSSG, glycine, cysteine, glutamine, glutamate, cystine, γ-glutamyl-cysteine and cysteinyl-glycine. This method avoided the use of derivatization and/or ion-pairing reagents and was validated according to United States Food and Drug Administration (US FDA) criteria. The lower limit of quantification was determined to be 0.5–100 ng/mL with lower limits of detection at 0.14–10.07 ng/mL. The intra- and inter-day precision values for all the analytes were <15% CV, and the accuracy ranged from 85.4% to 114% at three levels of quality control. This method combined simple preparation with rapid analytical procedure (8 min), allowed for high-throughput analysis of GSH metabolism in basic and therapeutic treatment conditions within cultured cells. Our data showed a significant alteration of GSH metabolism in two independent resistant cells compared to sensitive cells. This method monitored the impact of molecularly targeted drugs on GSH metabolism within lung cancer cells and therefore helped identifying potential metabolic vulnerability for the therapeutic resistance in lung cancer.

The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics.

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Integrated LC\u2013MS and GC\u2013MS-based untargeted metabolomics studies of the effect of azadirachtin on Bactrocera dorsalis larvae

Azadirachtin exhibits excellent bioactivities against several hundred arthropods. However, current knowlege of its biochemical effect on B. dorsalis larvae is not deep enough. In this study, integrated LC-MS and GC-MS-based untargeted metabolomics were used to analyze the changes of endogenous metabolites and the biochemical effects of azadirachtin on B. dorsalis larvae. Azadirachtin has excellent bioactivities against B. dorsalis larvae in this study, leading to a longer developmental duration, lower survival rate, and low pupa weight. The effect of azadirachtin was investigated on a total of 22 and 13 differentially abundant metabolites in the LC–MS and GC–MS-based metabolomics results, are selected respectively. Pathway analysis indicated that 14 differentially enriched metabolic pathways, including seven influential pathways, are worthy of attention. Further integrated key metabolic pathway analysis showed that histidine metabolism, d-glutamine and d-glutamate metabolism, biotin metabolism, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and alanine, aspartate and glutamate metabolism in B. dorsalis larvae are significantly relevant pathways affected by azadirachtin. Although extrapolating the bioactivity results in this study to the practical project of B. dorsalis pest management in the field has limitations, it was found that azadirachtin has a significant effect on the primary metabolism of B. dorsalis larvae.