Untargeted Approach Research Articles

Untargeted Metabolomics Unveils the Edaphic Stress Impact on Habanero Pepper Ripening Fruit

Habanero (Capsicum chinense) is appreciated for its aroma and pungency; however, little is known of the stress effects on Habanero fruits. This work, through untargeted metabolomics, measures changes in the Habanero fruit pericarp under increased salinity and nitrogen and phosphorus deficiency at three ripening stages. Responses to salinity and macronutrient deficiency are stress- and ripening stage-specific, with a few features (<1% in N and P deficit; ca. 1.5% in salinity) being consistently affected through maturation, with the most evident changes in ripe fruit. Results point to a threshold in salinity, between 4 and 7 dS·m–1, above which a measurable response is seen. Nitrogen deficiency has a symmetric effect on feature abundance, pointing at a metabolite substitution in the pericarp; in contrast, phosphorus deficiency leads to an overall reduction in metabolite diversity, which could negatively affect the postharvest shelf-life. This work shows that untargeted approaches help to improve our understanding of Habanero fruit metabolism under stress conditions beyond traditional metrics.

Type 2 Diabetes in Obesity: A Systems Biology Study on Serum and Adipose Tissue Proteomic Profiles

Obesity is associated with metabolic disorders such as insulin resistance and type 2 diabetes mellitus (T2DM), further increasing an already heightened cardiovascular risk. Here, amongst obese class III bariatric surgery patients, we have investigated the effect of T2DM in serum and in two, same patient, adipose tissue (AT) depots through proteomic profile expression analyses. Serum and AT samples from subcutaneous (SAT) and visceral (VAT) fat were collected during bariatric surgery. Bead-based targeted multiplex assay systems were used to simultaneously detect and quantify multiple targets in serum samples (targeted proteomics) and analyze changes in adipokine serum composition. AT samples were assessed through an untargeted proteomics approach. Through a systems biology analysis of the proteomic data, information on the affected biological pathways was acquired. In obese class III individuals, the presence of T2DM induced a significantly higher systemic release of ghrelin, GLP-1, glucagon, MMP3, BAFF, chitinase 3-like 1, TNF-R1 and TNF-R2, and a lower systemic release of IL-8. SAT and VAT proteomes belonging to the same patient showed significant differences in local protein content. While the proteins upregulated in VAT were indicative of metabolic dysregulation, SAT protein upregulation suggested adequate endocrine regulation. The presence of T2DM significantly affected VAT protein composition through the upregulation of dysregulating metabolic pathways, but SAT protein composition was not significantly modified. Our results show that T2DM induces metabolic dysregulation in obese individuals with changes in systemic marker levels and impairment of proteostasis in VAT but not in SAT.

Molecular Composition of Beijing PM2.5 Brown Carbon Revealed by an Untargeted Approach Based on Gas Chromatography and Time-of-Flight Mass Spectrometry.

The knowledge of the chemical composition of brown carbon (BrC) is limited to the categories of components or parts of specific organic components. In this paper, the light-absorbing properties and molecular compositions of lipid-soluble organic components in fine particulate matter of Beijing from 2016 to 2018, characterized by an ultraviolet-visible spectrometer and gas chromatography coupled with time-of-flight mass spectrometry, respectively, were combined to untargetedly screen the key BrC molecules by a partial least squares regression model for the first time. A total of 421 molecules were obtained, where 61 molecules were identified qualitatively and 22 molecules quantitatively. To the best of our knowledge, 11 molecules were newly identified BrC species. These qualitative molecules included polycyclic aromatic hydrocarbons with higher ambient concentrations and mass absorbing efficiencies (MAEs), as well as oxygen- and nitrogen-containing aromatic components with relatively lower concentrations and MAEs. The absorption contribution at 365 nm of quantified BrC species to lipid-soluble BrC during heating seasons was 39.1 ± 17.0%, which was about 5 times as high as previous studies. These results help establish a complete BrC molecular database and provide data support for better evaluating the climate effect of atmospheric carbonaceous aerosols and formulating air pollution control strategies.

Characterization of Gangliosides from Mouse Optic Nerve Samples Using Mass Spectrometry.

This chapter focuses on identifying gangliosides in the optic nerve of the mouse using mass spectrometry techniques. The described protocol will also permit the characterization of the sample's lipidome. Two deuterium-labeled ganglioside standards and a general lipid class standard will be utilized for extraction efficiency and quantification. Using reversed-phase high-performance liquid chromatography (HPLC) coupled to a Q Exactive mass spectrometer, the samples will be analyzed. The method will consist of both an untargeted approach and a targeted approach with a ganglioside-specific inclusion list.

Analyses and Localization of Phosphatidylcholine and Phosphatidylserine in Murine Ocular Tissue Using Imaging Mass Spectrometry.

Imaging mass spectrometry (IMS) allows for spatial visualization of proteins, lipids, and metabolite distributions in a tissue. Identifying these compounds through mass spectrometry, combined with mapping the compound distribution in the sample in a targeted or untargeted approach, renders IMS a powerful tool for lipidomics. IMS analysis for lipid species such as phosphatidylcholine and phosphatidylserine allows researchers to pinpoint areas of lipid deficiencies or accumulations associated with ocular disorders such as age-related macular degeneration and diabetic retinopathy. Here, we describe an end-to-end IMS approach from sample preparation to data analysis for phosphatidylcholine and phosphatidylserine analysis.

Characterization and Comparison of Lipid Profiles of Selected Chicken Eggs Based on Lipidomics Approach

The present study aimed to analyze the lipid profiles of three selected chicken eggs (Nixi, silky fowl, and ordinary eggs) from the market of China by an UPLC-Q-Exactive-MS based untargeted lipidomics approach. In total, 11 classes and 285 lipid molecular species were identified from the egg yolks. Glycerophospholipids (GPLs, 6 classes, 168 lipid species) are the most abundant lipids groups, followed by sphingolipids (3 classes, 50 lipid species), and two neutral lipid classes (TG and DG). Notably, two ethersubclass of GPLs (PC-e and PE-p) and 12 species of cerebrosides were firstly detected from the chicken eggs. Furthermore, multivariate statistical analysis was performed and the lipids profiles of the three types of eggs were well discriminated from each other by 30 predominant lipids species. The characteristic lipid molecules of the different kind of eggs were also screened out. This study provides a novel insight for better understanding the lipid profiles and nutritional values of different chicken eggs.

Combining the targeted and untargeted screening of environmental contaminants reveals associations between PFAS exposure and vitamin D metabolism in human plasma.

We have developed, validated, and applied a method for the targeted and untargeted screening of environmental contaminants in human plasma using liquid chromatography high-resolution mass spectrometry (LC-HRMS). The method was optimized for several classes of environmental contaminants, including PFASs, OH-PCBs, HBCDs, and bisphenols. One-hundred plasma samples from blood donors (19-75 years, men n = 50, women n = 50, from Uppsala, Sweden) were analyzed. Nineteen targeted compounds were detected across the samples, with 18 being PFASs and the 19th being OH-PCB (4-OH-PCB-187). Ten compounds were positively associated with age (in order of increasing p-values: PFNA, PFOS, PFDA, 4-OH-PCB-187, FOSA, PFUdA, L-PFHpS, PFTrDA, PFDoA, and PFHpA; p-values ranging from 2.5 × 10-5 to 4.67 × 10-2). Three compounds were associated with sex (in order of increasing p-values: L-PFHpS, PFOS, and PFNA; p-values ranging from 1.71 × 10-2 to 3.88 × 10-2), all with higher concentrations in male subjects compared with female subjects. Strong correlations (0.56-0.93) were observed between long-chain PFAS compounds (PFNA, PFOS, PFDA, PFUdA, PFDoA, and PFTrDA). In the non-targeted data analysis, fourteen unknown features correlating with known PFASs were found (correlation coefficients 0.48-0.99). Five endogenous compounds were identified from these features, all correlating strongly with PFHxS (correlation coefficients 0.59-0.71). Three of the identified compounds were vitamin D3 metabolites, and two were diglyceride lipids (DG 24:6;O). The results demonstrate the potential of combining targeted and untargeted approaches to increase the coverage of compounds detected with a single method. This methodology is well suited for exposomics to detect previously unknown associations between environmental contaminants and endogenous compounds that may be important for human health.

A data mining tool for untargeted biomarkers analysis: Grapes ripening application

In metabolomics, data generated by untargeted approaches can be very complex due to the typically extensive number of features in raw data (with and without chemical relevance), dependence on raw data preprocessing methods, and lack of selective data mining tools to appropriately interpret these data. Extraction of meaningful information from these data is still a significant challenge in metabolomics. Moreover, currently available tools may overprocess the data, eliminating useful information. This work aims at proposing a data mining tool capable of dealing with metabolomics data, specifically liquid chromatography-mass spectrometry (LC-MS) to enhance the extraction of meaningful chemical information. The algorithm construction intended to be as general as possible in highlighting chemically relevant features, discarding non-informative signals specially background features.The proposed algorithm was applied to an LC-MS data set generated from the analysis of grapes collected over a developmental period encompassing a 4-month period. The algorithm outcome is a short list of features from metabolites that are worth to be further investigated, for example by HRMS fragmentation for subsequent identification. The performance of the algorithm in estimating potentially interesting features was compared with the commercial MZmine software. For this case study, the MZmine output yielded a final set of 37 features (out of 1543 initially identified) with noise features while the proposed algorithm identified 99 systematic features without noise. Also, the algorithm required 2 times less user-defined parameters when compared to MZmine. Globally, the proposed algorithm demonstrated a higher ability to pin-point features that may be associated with grapes developmental and maturation processes requiring minimal parameters definition, thus preventing user uncertainty and the compromise of experimental information.

Investigating the Potential of Lipids for Use as Biomarkers for Glioblastoma via an Untargeted Lipidomics Approach.

The types and functions of lipids involved in glioblastoma (GB) are not well known. Lipidomics is a new field that examines cellular lipids on a large scale and novel aplication of lipidomics in the biomedical sciences have emerged. This study aimed to investigate the potential of blood lipids for use as biomarkers for the diagnosis of GB via untargated lipidomic approach. Gaining a deeper understanding of lipid metabolism in patients with GB can contribute to the early diagnosis with GB patiens and also development of novel and better therapeutic options. This study was performed using blood samples collected from 14 patients (eight females and six males) and 14 controls (eight females and six males). Lipids were extracted from blood samples and quantified using phosphorus assay. Lipid profiles of between patients with GB and controls were compared via an untargeted lipidomics approach using 6530 Accurate-Mass Q-TOF LC/MS mass spectrometer. According to the results obtained using the untargeted lipidomics approach, differentially regulated lipid species, including fatty acid (FA), glycerolipid (GL), glycerophospholipid (PG), saccharolipid (SL), sphingolipid (SP), and sterol lipid (ST) were identified between in patients with GB and controls. Differentially regulated lipids were identified in patients with GB, and these lipid species were predicted as potential biomarkers for diagnosis of GB.

A Targeted and an Untargeted Metabolomics Approach to the Volatile Aroma Profile of Young 'Maraština' Wines.

This study investigated the detailed volatile aroma profile of young white wines of Maraština, Vitis Vinifera L., produced by spontaneous fermentation. The wines were produced from 10 vineyards located in two Dalmatian subregions (Northern Dalmatia and Central and Southern Dalmatia). Volatile compounds from the wine samples were isolated by solid-phase extraction (SPE) and analyzed by an untargeted approach using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) and a targeted approach by gas chromatography-tandem mass spectrometry (GC-MS/MS). A comprehensive two-dimensional GC×GC analysis detailed the total volatile metabolites in the wines due to its excellent separation ability. More than 900 compounds were detected after untargeted profiling; 188 of them were identified or tentatively identified. A total of 56 volatile compounds were identified and quantified using GC-MS/MS analysis. The predominant classes in Maraština wines were acids, esters, and alcohols. The key odorants with odor activity values higher than one were β-damascenone, ethyl caprylate, ethyl isovalerate, ethyl 2-methylbutyrate, ethyl caproate, isopentyl acetate, ethyl butyrate, and phenylacetaldehyde. The metabolomics approach can provide a large amount of information and can help to anticipate variation in wines or change winemaking procedures.

Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS.

Volatile compounds play vital roles in food sensory attributes and food quality. An analysis of volatile compounds could illustrate the sensory attributes at the microscale level. Here, untargeted profiling approaches for volatiles in five most-consumed meat species were established using headspace SPME-GC/high resolution Orbitrap MS. An extended high-resolution database of meat volatile compounds was established to enhance the qualification accuracy. Using sulfur-containing compounds, aldehydes, and ketones as the research model, the parameters including fiber coating types, extraction temperature, extraction time, and desorbing time were optimized. Principle component analysis, volcano analysis and partial least squares discriminant analysis were applied to run the classification and the selection of discriminant markers between meat varieties, respectively. Different varieties could be largely distinguished according to the volatiles' profiles. 1-Octen-3-ol, 1-octen-3-one, 2-pentyl furan and some other furans degraded from n-6 fatty acids would contribute to distinguishing duck meat from other categories, while methyl esters mainly from oleic acid as well as dimethyl sulfoxide and carbon disulfide possibly produced from the sulfur-containing amino acids contributed to the discrimination of beef. Therefore, volatiles' profiling not only could interpret the aroma style in meat but also could be another promising method for meat differentiation and authentication.

Unraveling the impact of the capsule material on the aroma of brewed coffee by headspace analysis using a HiSorb probe followed by reverse fill/flush flow modulation GC×GC-MS.

The present paper discusses the use of a high-concentration-capacity tool, HiSorb, to investigate the impact of capsule material on the aroma profile of espresso-brewed coffee. The specific high-concentration-capacity probe used is characterized by a sorbent volume (63 μL) intermediate between the solid-phase microextraction (SPME) fiber (0.6 μL) and the stir-bar sorptive extraction rod (126 μL). The extraction performance of the HiSorb was compared, in terms of both absolute signal and compound coverage, with both an equivalent sorbent (polydimethylsiloxane) and a divinylbenzene/carboxen/polydimethylsiloxane SPME fiber using both targeted and untargeted approaches. The HiSorb showed superior extraction compared with the SPME fibers. The HiSorb was then optimized in terms of extraction time and temperature and used to investigate the volatile profile of 23 espresso-brewed coffees prepared with capsules made of different materials-aluminum, compostable, and aluminum multilayer pack-prepared using a refillable capsule. Comprehensive two-dimensional gas chromatography equipped with a reverse fill/flush flow modulator and coupled to mass spectrometry was used to obtain a chromatographic fingerprint of the volatile profile of the brewed coffee. The data were aligned and compared using a tile-based approach, and the results were obtained by performing raw data mining within the same software platform. The data mining enabled the extraction of informative features responsible for the differentiation between the different capsule materials, showing a significant depletion in aroma intensity in the compostable capsule.

Transcriptomic Analysis of Distal Parts of Roots Reveals Potentially Important Mechanisms Contributing to Limited Flooding Tolerance of Canola (Brassica napus) Plants.

Since most of the root metabolic activities as well as root elongation and the uptake of water and mineral nutrients take place in the distal parts of roots, we aimed to gain insight into the physiological and transcriptional changes induced by root hypoxia in the distal parts of roots in canola (Brassica napus) plants, which are relatively sensitive to flooding conditions. Plants were subject to three days of root hypoxia via lowering oxygen content in hydroponic medium, and various physiological and anatomical features were examined to characterize plant responses. Untargeted transcriptomic profiling approaches were also applied to investigate changes in gene expression that took place in the distal root tissues in response to hypoxia. Plants responded to three days of root hypoxia by reducing growth and gas exchange rates. These changes were accompanied by decreases in leaf water potential (Ψleaf) and root hydraulic conductivity (Lpr). Increased deposition of lignin and suberin was also observed in the root tissues of hypoxic plants. The transcriptomic data demonstrated that the effect of hypoxia on plant water relations involved downregulation of most BnPIPs in the root tissues with the exception of BnPIP1;3 and BnPIP2;7, which were upregulated. Since some members of the PIP1 subfamily of aquaporins are known to transport oxygen, the increase in BnPIP1;3 may represent an important hypoxia tolerance strategy in plants. The results also demonstrated substantial rearrangements of different signaling pathways and transcription factors (TFs), which resulted in alterations of genes involved in the regulation of Lpr, TCA (tricarboxylic acid) cycle-related enzymes, antioxidant enzymes, and cell wall modifications. An integration of these data enabled us to draft a comprehensive model of the molecular pathways involved in the responses of distal parts of roots in B. napus. The model highlights systematic transcriptomic reprogramming aimed at explaining the relative sensitivity of Brassica napus to root hypoxia.

Placental macrophage responses to viral and bacterial ligands and the influence of fetal sex.

Bacterial and viral infections of the placenta are associated with inflammation and adverse pregnancy outcomes. Hofbauer cells (HBCs) are fetal-origin macrophages in the placenta, proposed to protect the fetus from vertical pathogen transmission. We performed quantitative proteomics on term HBCs under resting conditions and following exposure to bacterial and viral pathogen-associated molecular patterns (PAMPs), and investigated the contribution of fetal sex. Resting HBCs expressed proteins pertinent to macrophage function, including chemokines, cytokines, Toll-like receptors, and major histocompatibility complex class I and II molecules. HBCs mounted divergent responses to bacterial versus viral PAMPs but exhibited protein expression changes suggestive of a more pro-inflammatoryphenotype. A comparison between male and female HBCs showed that the latter mounted a stronger and wider response. Here, we provide a comprehensive understanding of the sex-dependent responses of placental macrophages to infectious triggers, which were primarily associated with lipid metabolism in males and cytoskeleton organization in females.

Untargeted metabolism approach reveals difference of varieties of bud and relation among characteristics of grafting seedlings in Camellia oleifera.

Camellia oleifera is one of the essential wood oil trees in the world. C.oleifera was propagated by nurse seedling grafting. Since the scion of C.oleifera had a significant regulated effect on the properties of rootstock after grafting and impacted on the growth of the grafted seedlings, it was necessary to understand the characteristics of buds among varieties to cultivate high-quality grafted seedlings. The metabolome was thought to be a powerful tool for understanding connecting phenotype-genotype interactions, which has an important impact on plant growth and development. In this study, UPLC-MS was used to determine the metabolites of the apical buds of CL3, CL4, CL40, and CL53 spring shoots after 30 days of sprout and to measure the growth characteristics of roots and stems after grafting. Metabolomics analysis revealed 554 kinds of metabolites were significant differences among four varieties, and 29 metabolic pathways were identified to have significant changes (p< 0.05), including carboxylic acids and derivatives, fatty Acyls, organooxygen compounds, and prenol lipids metabolites. The metabolites appeared in all varieties, including phenethyl rutinoside in glycosyl compounds and hovenidulcioside A1 in terpene glycosides. Metabolite-metabolite correlations in varieties revealed more complex patterns in relation to bud and enabled the recognition of key metabolites (e.g., Glutamate, (±)Catechin, GA52, ABA, and cs-Zeatin) affecting grafting and growth ability. Each variety has a unique metabolite type and correlation network relationship. Differentiated metabolites showed different growth trends for development after grafting. Many metabolites regulate the growth of scions in buds before grafting, which plays a crucial role in the growth of seedlings after grafting. It not only regulates the growth of roots but also affects the development of this stem. Finally, those results were associated with the genetic background of each cultivar, showing that metabolites could be potentially used as indicators for the genetic background, indicating that metabolites could potentially be used as indicators for seedling growth characteristics. Together, this study will enrich the theoretical basis of seedling growth and lay a foundation for further research on the molecular regulation mechanism interaction between rootstock and scion, rootstock growth, and the development of grafted seedlings after grafting.

Pathways to Identify Electrophiles In Vivo Using Hemoglobin Adducts: Hydroxypropanoic Acid Valine Adduct and Its Possible Precursors.

Analytical methods and tools for the characterization of the human exposome by untargeted mass spectrometry approaches are advancing rapidly. Adductomics methods have been developed for untargeted screening of short-lived electrophiles, in the form of adducts to proteins or DNA, in vivo. The identification of an adduct and its precursor electrophile in the blood is more complex than that of stable chemicals. The present work aims to illustrate procedures for the identification of an adduct to N-terminal valine in hemoglobin detected with adductomics, and pathways for the tracing of its precursor and possible exposure sources. Identification of the adduct proceeded via preparation and characterization of standards of adduct analytes. Possible precursor(s) and exposure sources were investigated by measurements in blood of adduct formation by precursors in vitro and adduct levels in vivo. The adduct was identified as hydroxypropanoic acid valine (HPA-Val) by verification with a synthesized reference. The HPA-Val was measured together with other adducts (from acrylamide, glycidamide, glycidol, and acrylic acid) in human blood (n = 51, schoolchildren). The HPA-Val levels ranged between 6 and 76 pmol/g hemoglobin. The analysis of reference samples from humans and rodents showed that the HPA-Val adduct was observed in all studied samples. No correlation of the HPA-Val level with the other studied adducts was observed in humans, nor was an increase in tobacco smokers observed. A small increase was observed in rodents exposed to glycidol. The formation of the HPA-Val adduct upon incubation of blood with glycidic acid (an epoxide) was shown. The relatively high adduct levels observed in vivo in relation to the measured reactivity of the epoxide, and the fact that the epoxide is not described as naturally occurring, suggest that glycidic acid is not the only precursor of the HPA-Val adduct identified in vivo. Another endogenous electrophile is suspected to contribute to the in vivo HPA-Val adduct level.

Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection.

This study was performed to characterize the metabolic alteration of non-small-cell lung cancer (NSCLC) and discover blood-based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics-based approach was applied in a case-control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one-fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91-0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC=0.95, 95% CI: 0.92-0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.

Plasma Metabolomic and Lipidomic Profiling of Metabolic Dysfunction-Associated Fatty Liver Disease in Humans Using an Untargeted Multiplatform Approach.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies.

230 (PB-054) Poster - GC-MS based untargeted approach reveals metabolic perturbation in Tamoxifen resistant breast cancer cells

Background: Breast cancer is the most common cancer among females. The estrogen receptor (ER) is expressed in approximately 70% of breast tumors. Tamoxifen is an ER antagonist that is used as adjuvant therapy in the treatment of ER-positive cancers. Approximately 40% of patients develop tamoxifen resistance during treatment. The metabolic state of cancer cells dictates how responsive they are to chemotherapy, and cancer cells can rewire their metabolism to produce resistant phenotypes. We aim to identify altered metabolites and associated pathways in tamoxifen-resistant (TAMR) breast cancer by using gas chromatography-mass spectrometry (GC/MS).

Untargeted MSn-Based Monitoring of Glucuronides in Fish: Screening Complex Mixtures for Contaminants with Biological Relevance.

The complexity of contaminant mixtures in surface waters has presented long-standing challenges to the assessment of risks to human health and the environment. As a result, novel strategies for both identifying contaminants that have not been routinely monitored through targeted methods and prioritizing detected compounds with respect to their biological relevance are needed. Tracking biotransformation products in biofluids and tissues in an untargeted fashion facilitates the identification of chemicals taken up by the resident species (e.g., fish), so by default ensuring that detected compounds are biologically relevant in terms of exposure. In this study, we investigated xenobiotic glucuronidation, which is arguably the most important phase II metabolism pathway for many pharmaceuticals, pesticides, and other environmental contaminants. The application of an untargeted high-resolution mass spectrometry-based approach tentatively revealed the presence of over 70 biologically relevant xenobiotics in bile collected from male and female fathead minnows exposed to wastewater treatment plant effluents. The majority of these were not targets of conventional contaminant monitoring. These results highlight the utility of biologically based untargeted screening methods when evaluating chemical contaminants in complex environmental mixtures.