Relevant bibliographies by topics / Targeted and untargeted metabolomics

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  2. Dissertations / Theses
  3. Books
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  5. Conference papers
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Academic literature on the topic 'Targeted and untargeted metabolomics'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Targeted and untargeted metabolomics"

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Zhou, Jinna, Donghai Hou, Weiqiu Zou, Jinhu Wang, Run Luo, Mu Wang, and Hong Yu. "Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps Sinensis." Molecules 27, no. 11 (June 6, 2022): 3645. http://dx.doi.org/10.3390/molecules27113645.

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The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.
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Chen, Li, Fanyi Zhong, and Jiangjiang Zhu. "Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches." Metabolites 10, no. 9 (August 27, 2020): 348. http://dx.doi.org/10.3390/metabo10090348.

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This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.
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Gross, Thomas, Mark Mapstone, Ricardo Miramontes, Robert Padilla, Amrita K. Cheema, Fabio Macciardi, Howard J. Federoff, and Massimo S. Fiandaca. "Toward Reproducible Results from Targeted Metabolomic Studies: Perspectives for Data Pre-processing and a Basis for Analytic Pipeline Development." Current Topics in Medicinal Chemistry 18, no. 11 (August 28, 2018): 883–95. http://dx.doi.org/10.2174/1568026618666180711144323.

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Contemporary metabolomics experiments generate a rich array of complex high-dimensional data. Consequently, there have been concurrent efforts to develop methodological standards and analytical workflows to streamline the generation of meaningful biochemical and clinical inferences from raw data generated using an analytical platform like mass spectrometry. While such considerations have been frequently addressed in untargeted metabolomics (i.e., the broad survey of all distinguishable metabolites within a sample of interest), this methodological scrutiny has seldom been applied to data generated using commercial, targeted metabolomics kits. We suggest that this may, in part, account for past and more recent incomplete replications of previously specified biomarker panels. Herein, we identify common impediments challenging the analysis of raw, targeted metabolomic abundance data from a commercial kit and review methods to remedy these issues. In doing so, we propose an analytical pipeline suitable for the pre-processing of data for downstream biomarker discovery. Operational and statistical considerations for integrating targeted data sets across experimental sites and analytical batches are discussed, as are best practices for developing predictive models relating pre-processed metabolomic data to associated phenotypic information.
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Wei, Yiping, Paniz Jasbi, Xiaojian Shi, Cassidy Turner, Jonathon Hrovat, Li Liu, Yuri Rabena, Peggy Porter, and Haiwei Gu. "Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics." Journal of Proteome Research 20, no. 6 (May 25, 2021): 3124–33. http://dx.doi.org/10.1021/acs.jproteome.1c00019.

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Szeremeta, Michal, Karolina Pietrowska, Anna Niemcunowicz-Janica, Adam Kretowski, and Michal Ciborowski. "Applications of Metabolomics in Forensic Toxicology and Forensic Medicine." International Journal of Molecular Sciences 22, no. 6 (March 16, 2021): 3010. http://dx.doi.org/10.3390/ijms22063010.

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Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.
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Allwood, James William, Alex Williams, Henriette Uthe, Nicole M. van Dam, Luis A. J. Mur, Murray R. Grant, and Pierre Pétriacq. "Unravelling Plant Responses to Stress—The Importance of Targeted and Untargeted Metabolomics." Metabolites 11, no. 8 (August 22, 2021): 558. http://dx.doi.org/10.3390/metabo11080558.

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Climate change and an increasing population, present a massive global challenge with respect to environmentally sustainable nutritious food production. Crop yield enhancements, through breeding, are decreasing, whilst agricultural intensification is constrained by emerging, re-emerging, and endemic pests and pathogens, accounting for ~30% of global crop losses, as well as mounting abiotic stress pressures, due to climate change. Metabolomics approaches have previously contributed to our knowledge within the fields of molecular plant pathology and plant–insect interactions. However, these remain incredibly challenging targets, due to the vast diversity in metabolite volatility and polarity, heterogeneous mixtures of pathogen and plant cells, as well as rapid rates of metabolite turn-over. Unravelling the systematic biochemical responses of plants to various individual and combined stresses, involves monitoring signaling compounds, secondary messengers, phytohormones, and defensive and protective chemicals. This demands both targeted and untargeted metabolomics approaches, as well as a range of enzymatic assays, protein assays, and proteomic and transcriptomic technologies. In this review, we focus upon the technical and biological challenges of measuring the metabolome associated with plant stress. We illustrate the challenges, with relevant examples from bacterial and fungal molecular pathologies, plant–insect interactions, and abiotic and combined stress in the environment. We also discuss future prospects from both the perspective of key innovative metabolomic technologies and their deployment in breeding for stress resistance.
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Lim, Vuanghao, Sara Ghorbani Gorji, Venea Dara Daygon, and Melissa Fitzgerald. "Untargeted and Targeted Metabolomic Profiling of Australian Indigenous Fruits." Metabolites 10, no. 3 (March 19, 2020): 114. http://dx.doi.org/10.3390/metabo10030114.

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Selected Australian native fruits such as Davidson’s plum, finger lime and native pepperberry have been reported to demonstrate potent antioxidant activity. However, comprehensive metabolite profiling of these fruits is limited, therefore the compounds responsible are unknown, and further, the compounds of nutritional value in these native fruits are yet to be described. In this study, untargeted and targeted metabolomics were conducted using the three fruits, together with assays to determine their antioxidant activities. The results demonstrate that targeted free and hydrolysed protein amino acids exhibited high amounts of essential amino acids. Similarly, important minerals like potassium were detected in the fruit samples. In antioxidant activity, Davidson’s plum reported the highest activity in ferric reducing power (FRAP), finger lime in antioxidant capacity (ABTS), and native pepperberry in free radical scavenging (DPPH) and phosphomolybdenum assay. The compounds responsible for the antioxidant activity were tentatively identified using untargeted GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS metabolomics. A clear discrimination into three clusters of fruits was observed using principal component analysis (PCA) and partial least squares (PLS) analysis. The correlation study identified a number of compounds that provide the antioxidant activities. GC×GC-TOFMS detected potent aroma compounds of limonene, furfural, and 1-R-α-pinene. Based on the untargeted and targeted metabolomics, and antioxidant assays, the nutritional potential of these Australian bush fruits is considerable and supports these indigenous fruits in the nutraceutical industry as well as functional ingredients for the food industry, with such outcomes benefiting Indigenous Australian communities.
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Martin, Malia J., Ryan S. Pralle, Isabelle R. Bernstein, Michael J. VandeHaar, Kent A. Weigel, Zheng Zhou, and Heather M. White. "Circulating Metabolites Indicate Differences in High and Low Residual Feed Intake Holstein Dairy Cows." Metabolites 11, no. 12 (December 14, 2021): 868. http://dx.doi.org/10.3390/metabo11120868.

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Selection for more feed efficient dairy cows is key to improving sustainability and profitability of dairy production; however, underlying mechanisms contributing to individual animal feed efficiency are not fully understood. The objective of this study was to identify circulating metabolites, and pathways associated with those metabolites, that differ between efficient and inefficient Holstein dairy cows using targeted metabolite quantification and untargeted metabolomics. The top and bottom fifteen percent of cows (n = 28/group) with the lowest and highest residual feed intake in mid-lactation feed efficiency trials were grouped retrospectively as high-efficient (HE) and low-efficient (LE). Blood samples were collected for quantification of energy metabolites, markers of hepatic function, and acylcarnitines, in addition to a broader investigation using untargeted metabolomics. Short-chain acylcarnitines, C3-acylcarnitine, and C4-acylcarntine were lower in HE cows (n = 18/group). Untargeted metabolomics and multivariate analysis identified thirty-nine differential metabolites between HE and LE (n = 8/group), of which twenty-five were lower and fourteen were higher in HE. Pathway enrichment analysis indicated differences in tryptophan metabolism. Combined results from targeted metabolite quantification and untargeted metabolomics indicate differences in fatty acid and amino acid metabolism between HE and LE cows. These differences may indicate post-absorptive nutrient use efficiency as a contributor to individual animal variation in feed efficiency.
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CREEK, DARREN J., and MICHAEL P. BARRETT. "Determination of antiprotozoal drug mechanisms by metabolomics approaches." Parasitology 141, no. 1 (June 5, 2013): 83–92. http://dx.doi.org/10.1017/s0031182013000814.

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SUMMARYThe discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.
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Lee, Yu Ra, Ki-Yong An, Justin Jeon, Nam Kyu Kim, Ji Won Lee, Jongki Hong, and Bong Chul Chung. "Untargeted Metabolomics and Polyamine Profiling in Serum before and after Surgery in Colorectal Cancer Patients." Metabolites 10, no. 12 (November 27, 2020): 487. http://dx.doi.org/10.3390/metabo10120487.

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Colorectal cancer is one of the most prevalent cancers in Korea and globally. In this study, we aimed to characterize the differential serum metabolomic profiles between pre-operative and post-operative patients with colorectal cancer. To investigate the significant metabolites and metabolic pathways associated with colorectal cancer, we analyzed serum samples from 68 patients (aged 20–71, mean 57.57 years). Untargeted and targeted metabolomics profiling in patients with colorectal cancer were performed using liquid chromatography-mass spectrometry. Untargeted analysis identified differences in sphingolipid metabolism, steroid biosynthesis, and arginine and proline metabolism in pre- and post-operative patients with colorectal cancer. We then performed quantitative target profiling of polyamines, synthesized from arginine and proline metabolism, to identify potential polyamines that may serve as effective biomarkers for colorectal cancer. Results indicate a significantly reduced serum concentration of putrescine in post-operative patients compared to pre-operative patients. Our metabolomics approach provided insights into the physiological alterations in patients with colorectal cancer after surgery.
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Dissertations / Theses on the topic "Targeted and untargeted metabolomics"

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Cichon, Morgan Julienne. "Investigating the Role of Tomato Phytochemicals through Targeted and Untargeted Metabolomics." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1449226913.

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Yang, Kundi. "Assessing and Evaluating Biomarkers and Chemical Markers by Targeted and Untargeted Mass Spectrometry-based Metabolomics." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1605044640528563.

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Zhong, Fanyi. "DEVELOPMENT AND APPLICATIONS OF HPLC-MS/MS BASED METABOLOMICS." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524792637748877.

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Tamimi, Sara <1987&gt. "Metabolomics investigations towards formulated natural complex products by untargeted and targeted mass spectrometry-based approaches." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8559/1/Tamimi_Sara_tesi.pdf.

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Although the last decade has witnessed a marked growth in the market of plant-based natural products, their high complexity in terms of composition makes a challenging task the guarantee of quality, efficacy and safety requirements. In such scenario, the recently approved European regulation EU 2017/745 has created a breaking point in the regulation of medical devices forcing the manufactures to investigate the overall qualitative composition of devices made of substances and quantitative information for the main constituents or for the components responsible for the desired effect. In view of this stricter standardization of plant-based medical devices, an intense research towards advanced technologies and robust analytical protocols is required. Based on these considerations, Grintuss® syrup was selected as a case study of formulated natural complex products for quali-quantitative metabolomics investigations. In particular, in the first part, an untargeted metabolomic approach for monitoring the batch-quality of Grintuss® syrup by electrospray ionization mass spectrometry flow injection analysis was developed. After having assessed the validity of the model by using multivariate statistical process control, the quality verification of new batches under investigation was achieved by comparing their profile with the fingerprinting profile of batches prepared according to the optimized and validated manufacturing process. The second task was aimed at profiling the metabolites contained in Grintuss® syrup by applying a targeted metabolomic LC-MS-based approach. Thus, an in-house database of high-purity standard reference compounds was built. Then, after the acquisition of MS/MS spectra of standard compounds of interest, the matching between the detected metabolites from samples and standard reference compounds from the database was assessed. Finally, the quantitative analysis of the recognized metabolites was performed and the results thus achieved have been combined with information deriving from the analysis of additional compounds allowing to reach the knowledge of >99.9% of the overall composition of Grintuss® syrup.
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Torres, Gené Sònia. "Advances on thirdhand smoke using targeted and untargeted approaches." Doctoral thesis, Universitat Rovira i Virgili, 2021. http://hdl.handle.net/10803/672209.

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El fum de tabac residual (thirdhand smoke en anglès, THS) és una via d'exposició a compostos tòxics de fum tabac poc estudiada fins ara. El THS es produeix per la deposició de parEcules i gasos en superFcies i pols, on es poden reemetre i/o reaccionar produint nous compostos tòxics, alguns d'ells carcinògens. Malgrat les creixents evidències, els riscos inherents a l'exposició a THS encara no s'han descrit completament. L'objecKu principal d'aquesta tesi és avançar en la caracterització química del THS i dels efectes per a la salut derivats d'aquesta exposició mitjançant l'aplicació de mètodes analíKcs dirigits i no dirigits. Aquesta tesi presenta el desenvolupament de un nou mètode analíKc per determinar simultàniament tòxics del tabac en pols domèsKca, mitjançant cromatografia líquida (UHPLC). En aquesta tesi, també s'ha desenvolupat un mètode d'anàlisi no dirigit basat en l'adquisició de mostres per UHPLC acoblada a espectrometria de masses d'alta resolució (HR-MS), amb l'aplicació d'estratègies avançades de processament de dades, la priorització estadísKca d’ions rellevants i una nova estratègia per a l'anotació de compostos. La combinació d'aquests dos mètodes va proporcionar per primera vegada l'anotació de dotzenes de tòxics relacionats amb la contaminació per THS adherits a la pols domèsKca. Pel que fa als efectes sobre la salut, presentem el primer estudi metabolòmic no dirigit en fetge de ratolins exposats a THS. L'aplicació de les tècniques UHPLC-HRMS i ressonància magnèKca nuclear (RMN) va permetre idenKficar dotzenes de metabòlits hepàKcs alterats, mentre que les imatges d'espectrometria de masses (MSI) van mostrar la distribució espacial diferencial de lípids en fetge induïda per THS. Aquests resultats confirmen els perills de l'exposició a THS i el paper clau de la introducció de noves estratègies metodològiques en la invesKgació en salut ambiental.
El humo de tabaco residual (thirdhand smoke en inglés, THS) es una vía de exposición a compuestos tóxicos del humo del tabaco poco estudiada hasta la fecha. El THS se produce por la deposición de parBculas y gases en superficies y polvo, dónde se pueden reemiEr y/o reaccionar produciendo nuevos compuestos tóxicos, algunos de ellos carcinógenos. A pesar de las crecientes evidencias, los riesgos inherentes a la exposición a THS aún no se han descrito por completo. El objeEvo principal de esta tesis es avanzar en la caracterización química del THS y de los efectos para la salud derivados de esta exposición mediante la aplicación de métodos analíEcos dirigidos y no dirigidos. Esta tesis presenta el desarrollo de un nuevo método analíEco para determinar simultáneamente tóxicos del tabaco en polvo domésEco mediante cromatograMa líquida (UHPLC). En esta tesis, también se ha desarrollado un método de análisis no dirigido basado en la adquisición de muestras por UHPLC acoplada a espectrometría de masas de alta resolución (HR-MS), con la aplicación de estrategias avanzadas de procesamiento de datos, la priorización estadísEca de iones relevantes y una nueva estrategia para la anotación de compuestos. La combinación de estos dos métodos proporcionó por primera vez la anotación de docenas de tóxicos relacionados con la contaminación por THS adheridos al polvo domésEco.Respecto a los efectos sobre la salud, presentamos el primer estudio metabolómico no dirigido en hígado de ratones expuestos a THS. La aplicación de las técnicas UHPLC-HRMS y resonancia magnéEca nuclear (RMN) permiEó idenEficar docenas de metabolitos hepáEcos alterados, mientras que las imágenes de espectrometría de masas (MSI) mostraron la distribución espacial diferencial de lípidos en hígado inducida por THS. Estos resultados confirman los peligros de la exposición a THS y el papel clave de nuevos enfoques metodológicos en la invesEgación en salud ambiental.
Thirdhand tobacco smoke (THS) is a novel and poorly understood pathway of tobacco exposure produced by the deposi=on and ageing of tobacco smoke par=cles and toxicants in surfaces and dust. This aged tobacco smoke could reemit into the air or react with other atmospheric chemicals to yield new toxicants, including carcinogens and becoming increasingly toxic with age. Although growing evidences of THS hazards, its chemical characteriza=on and the related health effects remain to be fully elucidated. Hence, this thesis aims to advance on the current knowledge on THS chemical characteriza=on and on the health effects derived from THS exposure by applying novel targeted and untargeted approaches. To advance on THS chemical characteriza=on, we have developed an efficient, quick and robust analy=cal method for simultaneously determining tobacco-specific compounds in household dust by ultra-highperformance liquid-chromatography coupled to tandem mass spectrometry (UHPLC-MSMS). We applied this target method in combina=on with untargeted strategies for a comprehensive characteriza=on of THS toxicants aNached to household dust. The developed untargeted workflow combines the sample acquisi=on by UHPLC coupled to high-resolu=on mass spectrometry (HR-MS) with the applica=on of advanced data processing strategies, the sta=s=cal priori=za=on of relevant features and a novel strategy for compound annota=on. The combina=on of these two approaches provided for the first =me the annota=on of dozens of toxicants related to THS contamina=on. To advance on the health effects, this thesis presents the first mul=plaQorm untargeted metabolomics study to unravel the molecular altera=ons of liver from mice exposed to THS. UHPLC-HRMS and nuclear magne=c resonance (NMR) revealed dozens of hepa=c metabolites altered in THS-exposed mice whilst mass spectrometry imaging (MSI) showed the differen=al spa=al distribu=on of lipids induced by THS. The results presented here confirm the hazards of THS exposure and the key role of combined untargeted and targeted methods in environmental health research.
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POLONIATO, GABRIELE. "Approccio metabolomico untargeted e targeted per lo studio della sepsi neonatale." Doctoral thesis, Università degli studi di Padova, 2022. https://hdl.handle.net/11577/3464351.

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La sepsi è una delle principali problematiche in ambito neonatologico. La sepsi neonatale è una sindrome da risposta infiammatoria sistemica indotta da infezione comune nei neonati prematuri e a termine. È una delle principali cause di morte e morbilità neonatale e si ritiene che abbia un ruolo chiave nella maggior parte dei disturbi infiammatori che causano o contribuiscono allo sviluppo delle principali morbilità che colpiscono il prematuro (displasia broncopolmonare, danno della sostanza bianca cerebrale, enterocolite necrotizzante e retinopatia del prematuro). La sepsi nel neonato è solitamente classificata come sepsi ad esordio precoce (EOS), quando l'infezione si manifesta entro tre giorni dalla nascita, o sepsi ad esordio tardivo (LOS) se si sviluppa in seguito. La diagnosi precoce della sepsi neonatale e la pronta somministrazione di una terapia antibiotica ad ampio spettro possono prevenirne il decorso clinico verso lo shock settico e la morte, ma non è facile diagnosticare precocemente la sepsi neonatale. L'emocoltura è ancora considerata il gold standard, anche se ci vuole tempo per ottenere i risultati e i falsi negativi non sono rari perché la batteriemia neonatale è spesso intermittente e il trattamento antibiotico intrapartum può limitare il valore diagnostico della coltura. La sepsi neonatale viene quindi ipotizzata principalmente sulla base di segni e sintomi clinici non specifici; per di più, nessuno dei biomarcatori più utilizzati è un indicatore del tutto affidabile di sepsi nei neonati. Pertanto, l'identificazione di nuovi biomarcatori per EOS è di cruciale importanza. Inoltre, mentre le terapie di supporto promuovono la sopravvivenza dei neonati settici, non esistono terapie per alterare la fisiopatologia sottostante, e ciò è in parte dovuto alla parziale conoscenza delle complesse vie biologiche alla base della fisiopatologia della sepsi. Lo scopo dello studio era confrontare i profili metabolici di campioni di plasma e urina raccolti alla nascita da neonati pretermine con e senza sepsi ad esordio precoce (EOS) per identificare le perturbazioni metaboliche che potrebbero orientare la ricerca di nuovi biomarcatori precoci. Tutti i neonati prematuri ammessi all'unità di terapia intensiva neonatale erano eleggibili per questo studio prospettico caso-controllo. I neonati sono stati arruolati come "casi" se hanno sviluppato EOS e come "controlli" in caso contrario. I campioni di plasma raccolti alla nascita e i campioni di urina raccolti entro 24 ore dalla nascita sono stati sottoposti ad analisi metabolomica targeted e untargeted tramite spettrometria di massa accoppiata a cromatografia liquida UPLC. Sono state poi applicate analisi statistiche univariate e multivariate. Di 123 neonati idonei, 15 hanno sviluppato EOS. Questi 15 neonati corrispondevano ai controlli per età gestazionale e peso. L'analisi UPLC-MS dei campioni di urina ha rivelato un cluster di casi di EOS rispetto ai neonati sani. Inoltre, esiste una differenza metabolica per distinguere i neonati che sviluppano sepsi dai soggetti sani e sono stati scoperti marcatori putativi che discriminano tra casi di EOS e controlli. L'analisi dei pathways ha evidenziato gli squilibri metabolici maggiormente coinvolti in caso di EOS. Le vie metaboliche più significative sono state studiate utilizzando un'analisi targetd su campioni di plasma prelevati dagli stessi neonati, confermando l’evidente perturbazione delle vie metaboliche del triptofano e del glutatione nei neonati con EOS. In conclusione, i neonati con EOS presentavano un profilo metabolico alla nascita che li distingueva chiaramente da quelli senza sepsi, e i metaboliti delle vie del glutatione e del triptofano sono promettenti come nuovi biomarcatori della sepsi neonatale.
Sepsis is a major concern in neonatology. Neonatal sepsis is an infection-induced, systemic inflammatory response syndrome common in premature and term neonates. It is one of the leading causes of neonatal death and morbidity and is believed to have a key role in most inflammatory disorders that cause or enhance the main morbidities affecting the preterm (bronchopulmonary dysplasia, white matter injury, necrotizing enterocolitis, and retinopathy of prematurity). Sepsis in the newborn is typically classified as either early-onset sepsis (EOS), when the infection occurs within three days after birth, or late-onset sepsis (LOS) if it develops afterward. Early detection of neonatal sepsis and prompt administration of broad-spectrum antibiotic therapy can prevent its clinical course towards septic shock and death, but it is not easy to diagnose neonatal sepsis early on. Blood culture is still considered the gold standard, even though it takes time to obtain the results, and false-negative findings are not uncommon because neonatal bacteremia is often intermittent, and intrapartum antibiotic treatment may limit the culture’s diagnostic value. Neonatal sepsis is therefore mainly suspected on the grounds of non-specific clinical signs and symptoms; moreover, none of the most widely used biomarkers are entirely reliable indicators of sepsis in newborns. Hence, identifying new biomarkers for EOS is of crucial importance. Furthermore, while supportive therapies promote the survival of septic neonates, there are no mechanistic therapies to alter the underlying pathophysiology, and this is partly due to partial knowledge of the complex biological pathways underlying the pathophysiology of sepsis. The aim of the study was to compare the metabolic profiles of plasma and urine samples collected at birth from preterm neonates with and without early-onset sepsis (EOS) to identify metabolic perturbations that might orient the search for new early biomarkers. All preterm newborns admitted to the neonatal intensive care unit were eligible for this proof-of-concept, prospective case-control study. Infants were enrolled as “cases” if they developed EOS, and as “controls” if they did not. Plasma samples collected at birth and urine samples collected within 24 h of birth underwent untargeted and targeted metabolomic analysis using mass spectrometry coupled with ultra-performance liquid chromatography. Univariate and multivariate statistical analyses were applied. Of 123 eligible newborns, 15 developed EOS. These 15 newborns matched controls for gestational age and weight. UPLC–MS analysis of urine samples revealed a clustering of cases of EOS compared with healthy neonates. Furthermore, a metabolic signature exists to distinguish neonates that develop sepsis and healthy subjects and putative markers discriminating between EOS cases and controls were discovered. Pathway analysis showed metabolic derangements most involved in EOS. The most significant metabolic pathways were investigated using a targeted analysis on plasma samples collected from the same neonates, confirming the marked disruption of the tryptophan and glutathione metabolic pathways in the neonates with EOS. In conclusion, neonates with EOS had a metabolic profile at birth that clearly distinguished them from those without sepsis, and metabolites of glutathione and tryptophan pathways are promising as new biomarkers of neonatal sepsis.
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Abily-Donval, Lénaïg. "Exploration des mécanismes physiopathologiques des mucopolysacharidoses et de la maladie de Fabry par approches "omiques" et modulation de l'autophagie. Urinary metabolic phenotyping of mucopolysaccharidosis type I combining untargeted and targeted strategies with data modeling Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR108.

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Les pathologies lysosomales sont des maladies liées au déficit quantitatif ou qualitatif d’une hydrolase ou d’un transporteur à l’origine d’une atteinte multiviscérale potentiellement sévère. Certaines de ces pathologies sont accessibles à des traitements mais ces thérapeutiques sont uniquement symptomatiques et ne guérissent pas les patients. Même si le phénomène de surcharge peut expliquer entre autres la symptomatologie observée, la physiopathologie de ces maladies est complexe et non précisément connue. Une meilleure connaissance de ces pathologies pourrait permettre d’améliorer leur prise en charge globale. L’objectif de ce travail était dans un premier temps d’appliquer des techniques « omiques » dans deux groupes de maladies : les mucopolysaccharidoses et la maladie de Fabry. Cette étude a permis la mise en place d’une méthodologie métabolomique non ciblée basée sur une stratégie analytique multidimensionnelle comportant la spectrométrie de masse à haute résolution couplée à la chromatographie liquide ultra-haute performance et la mobilité ionique. Dans les mucopolysaccharidoses, l’étude des voies métaboliques a mis en évidence des modifications dans le métabolisme de plusieurs acides aminés et du système oxydatif du glutathion. Dans la maladie de Fabry, des modifications ont été observées dans l’expression de l’interleukine 7 et du facteur de croissance FGF2. La deuxième partie du travail s’est intéressée à la modulation de l’autophagie dans la maladie de Fabry. Notre étude a montré une diminution du flux autophagique avec un retard d’adressage de l’enzyme au lysosome dans les cellules Fabry. L’inhibition de l’autophagie permet de diminuer l’accumulation du substrat accumulé (Gb3) et améliore l’efficacité de l’enzymothérapie substitutive. En conclusion ce travail a permis une meilleure compréhension des mécanismes physiopathologiques des pathologies lysosomales et a montré la complexité du fonctionnement du lysosome. Ces données permettent d’espérer l’amélioration des stratégies thérapeutiques et diagnostiques dans ces maladies
Lysosomal diseases caused by quantitative or qualitative hydrolase or transporter defect induce multiorgan features. Some specific symptomatic treatments are available but they do not cure patients. Pathophysiological bases of lysosomal disease are poorly understood and cannot be due to storage only. A better knowledge of these pathologies could improve their management. The first aim of this study was to apply “omics” strategies in mucopolysaccharidosis and in Fabry disease. This thesis allowed the implementation of an untargeted metabolomic methodology based on a multidimensional analytical strategy including high-resolution mass spectrometry coupled with ultra-high-performance liquid chromatography and ion mobility. Analysis of metabolic pathways showed a major remodeling of the amino acid metabolisms as well as oxidative stress via glutathione metabolism. In Fabry disease, changes were observed in expression of interleukin 7 and FGF2. The second study focused on modulation of autophagy in Fabry disease. In this work, we have shown a disruption of the autophagic process and a delay in enzyme targeting to the lysosome in Fabry disease. Autophagic inhibition reduced accumulation of accumulated substrate (Gb3) and improved the efficiency of enzyme replacement therapy. This work allowed a better knowledge of the physiopathological mechanisms implicated in lysosomal diseases and showed the complexity of lysosome. These data could ameliorate management of these disease and are associated with hope for patients
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Jones, Christina Michele. "Applications and challenges in mass spectrometry-based untargeted metabolomics." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54830.

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Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. 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 prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.
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Miller, Jenna Lauren. "Discovering potential urinary biomarkers of tomato consumption using untargeted metabolomics." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594632758364348.

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Mendez, Kevin Milton. "Untargeted metabolomics of childhood asthma exacerbations from retrospectively collected serum samples." Thesis, Mendez, Kevin Milton (2017) Untargeted metabolomics of childhood asthma exacerbations from retrospectively collected serum samples. Honours thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/40063/.

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Asthma is a common chronic inflammatory disease of the airways, affecting 360 million people globally. It is characterised by chronic inflammation, recurrent episodes of bronchoconstriction and mucosal hypersecretion. Symptoms include chest tightness, shortness of breath, coughing and wheezing. Oral corticosteroids used for the treatment of asthma have adverse effects, including bone mineralisation and adrenal suppression. Not all children with acute asthma-like symptoms will have persistent asthma in the future. This is particularly common at a pre-school age. This persistence is only known retrospectively. Identifying children early in their disease course can better direct treatment. Additionally, further understanding of the underlying disease mechanisms can direct novel therapies. Metabolomics is the systematic study of metabolites in a biological system. Metabolites are low molecular weight biochemical that are reactants, intermediates and end products of biological reactions. They are highly sensitive and are a snapshot of a particular biochemistry and/or pathophysiology. However, they are also highly sensitive to analytical variation. Thus, there were three aims in this study: to assess the impact of potentially limiting factors of retrospectively collected serum samples on metabolomic analysis; to determine whether metabolomics can identify potential biomarkers to distinguish wheeze/asthma exacerbation and control groups; and to determine whether metabolomics-derived biomarkers can identify differences between preschool-aged and school-aged phenotypes. Serum samples were curated from the Mechanisms of Acute Viral Respiratory Infections in Children (MAVRIC) study. This cohort study recruited children upon presentation to the emergency department at Princess Margaret Hospital with acute lower respiratory illnesses including wheeze/asthma. One–hundred and sixty-one samples were from children with acute wheeze/asthma, and 51 were from healthy controls. Samples were previously stored between 0.8 to 7.9 years at −80 °C. Samples were extracted, derivatised and subsequently analysed using GC-QTOF-MS. SpectralWorks’ AnalyzerPro® was used for the deconvolution and untargeted processing of the metabolite data. The quality control-robust spline correction (QC-RSC) algorithm was used for inter- and intra- batch correction. Putative identification of metabolites was made using the NIST (v2.0) library. Fifty-two metabolites were included in the data analysis, with 24 putatively identified metabolites. The effects of storage time on metabolites were determined via Spearman’s correlation. There was a significant difference (p-value < 0.05) in metabolite abundances for succinate, serine and tryptophan; however, the correlation was weak. A two-way Analysis of Variance was performed to compare acute wheeze/asthma vs. healthy, pre-school vs. school age and the associated interactions. Twenty-nine metabolites were found to be significantly different (p-value < 0.05) between the acute wheeze/asthma and the control group. The sub-classes of metabolites include amino acids, fatty acids and sugars. Principal Component Analysis showed a difference in spread between the acute wheeze/asthma and control group. However, there was no clear difference between preschool and school-aged brackets for each group. Arabinofuranose and creatinine were significantly different (p-value < 0.05) between pre-school and school-aged subjects with acute wheeze/asthma. Creatinine is potentially being indicative of higher ASM stress and damage in the school-aged subjects with acute wheeze/asthma.
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Books on the topic "Targeted and untargeted metabolomics"

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Castro-Puyana, María, Miguel Herrero, and María Luisa Marina, eds. Capillary Electrophoresis in Food Analysis. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150361521220201.

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This reference describes recent advances and applications of capillary electrophoresis in the field of food science. The first two chapters are devoted to the fundamentals of capillary electrophoresis, and to the main sample preparation techniques used for food analysis using this miniaturized separation technique, respectively. These two introductory chapters are followed by several chapters focused on the different strategies for analyzing specific food components, including lipids, carbohydrates, proteins, peptides, amino acids, vitamins, polyphenols, and food additives. The information provided in these chapters helps readers to understand and develop appropriate methods to carry out a deep characterization of food samples. Relevant concepts such as food authentication, chemical food safety or the control of the quality and safety of dietary supplements, and food metabolomics are also covered, where appropriate. The big potential of capillary electrophoresis to achieve chiral separations and the determination of enantiomers in food samples or to develop targeted and non-targeted metabolomics strategies to ensure food safety and quality is also described. As an additional step towards analytical miniaturization, a chapter devoted to food analysis by microchip electrophoresis is also included in this book. All 14 chapters are contributed by highly experienced researchers in the field. Capillary Electrophoresis in Food Analysis is a key source of information for food chemists and analytical chemists in industry (quality control laboratories) and academia (research labs and training courses).
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Book chapters on the topic "Targeted and untargeted metabolomics"

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Martineau, Estelle, and Patrick Giraudeau. "Fast Quantitative 2D NMR for Untargeted and Targeted Metabolomics." In NMR-Based Metabolomics, 365–83. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9690-2_20.

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Karnovsky, Alla, and Shuzhao Li. "Pathway Analysis for Targeted and Untargeted Metabolomics." In Computational Methods and Data Analysis for Metabolomics, 387–400. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_19.

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Reisz, Julie A., Connie Zheng, Angelo D’Alessandro, and Travis Nemkov. "Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics." In High-Throughput Metabolomics, 121–35. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_8.

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Brunius, Carl, Huaxing Wu, and Rikard Landberg. "Targeted and Untargeted Metabolomics for Specific Food Intake Assessment." In Advances in the Assessment of Dietary Intake, 315–36. Boca Raton : CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315152288-18.

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Artati, Anna, Cornelia Prehn, Dominik Lutter, and Kenneth Allen Dyar. "Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS)." In Methods in Molecular Biology, 311–27. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2249-0_21.

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Papadimitropoulos, Matthaios-Emmanouil P., Catherine G. Vasilopoulou, Christoniki Maga-Nteve, and Maria I. Klapa. "Untargeted GC-MS Metabolomics." In Methods in Molecular Biology, 133–47. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7643-0_9.

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Patel, Manish Kumar, Avinash Mishra, and Bhavanath Jha. "Untargeted Metabolomics of Halophytes." In Marine OMICS, 307–25. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315372303-18.

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Shandilya, Dev Kant. "Targeted and Untargeted Analyses." In Mass Spectrometry in Food Analysis, 19–27. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003091226-3.

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Gevi, Federica, Giuseppina Fanelli, Lello Zolla, and Sara Rinalducci. "Untargeted Metabolomics of Plant Leaf Tissues." In High-Throughput Metabolomics, 187–95. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_12.

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Chalikiopoulou, Constantina, José Carlos Gómez-Tamayo, and Theodora Katsila. "Untargeted Metabolomics for Disease-Specific Signatures." In Mass Spectrometry for Metabolomics, 71–81. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2699-3_7.

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Conference papers on the topic "Targeted and untargeted metabolomics"

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Laiakis, Evagelia C., Evan L. Pannkuk, Siddheshwar Chauthe, Yi-Wen Wang, Ming Lian, Tytus D. Mak, Christopher A. Barker, Giuseppe Astarita, and Albert J. Fornace. "Abstract 2506: Untargeted and targeted multiplatform metabolomic and lipidomic approaches for monitoring biological effects in serum from total body irradiated humans." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-2506.

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Porokhin, Vladimir, Xinmeng Li, and Soha Hassoun. "Pathway Enrichment Analysis for Untargeted Metabolomics." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3107411.3108233.

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Hsu, Ping-Ching, Renny S. Lan, Theodore Brasky, Catalin Marian, Amrita K. Cheema, Habtom W. Ressom, Christopher A. Loffredo, Wallace Pickworth, and Peter G. Shields. "Abstract 1830: Untargeted metabolomics reveals smokers’ characteristic profiles." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-1830.

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Mannochio-Russo, H., R. F. de Ameida, Bueno PCP, A. Bauermeister, A. M. Caraballo-Rodríguez, P. C. Dorrestein, and V. S. Bolzani. "Untargeted metabolomics sheds light on the secondary metabolism of Malpighiaceae family." In GA – 69th Annual Meeting 2021, Virtual conference. Georg Thieme Verlag, 2021. http://dx.doi.org/10.1055/s-0041-1736863.

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Joo, J., E. Ho, S. Hofbauer, T. Penning, J. Steltz, A. Vachani, C. Mesaros, and B. E. Himes. "Untargeted Metabolomics Analysis of Non-Small Lung Cancer in Never Smokers." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a3480.

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Manach, Claudine, Natalia Vázquez-Manjarrez, Christopher Weinert, Marynka Ulaszewska, Mélanie Pétéra, Carina Mack, Sabine Kulling, et al. "Applying an untargeted metabolomics approach using two complementary platforms for the discovery and validation of banana intake biomarkers." In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05849.

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Ghosh, Nilanjana, Sivakumar Raju Rathnakaram, Sandeep R. Kaushik, Rakesh Arya, Ranjan Nanda, Parthasarathi Bhattacharyya, Sushmita Roy Chowdhury, Rintu Banerjee, and Koel Chaudhury. "GC MS based untargeted metabolomics for understanding the pathophysiology of asthma COPD overlap (ACO)." In ERS International Congress 2018 abstracts. European Respiratory Society, 2018. http://dx.doi.org/10.1183/13993003.congress-2018.pa5471.

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Lin, Yangfei, Peng Qiao, and Yong Dou. "Untargeted attack on targeted-label for multi-label image classification." In Twelfth International Conference on Graphics and Image Processing, edited by Zhigeng Pan and Xinhong Hei. SPIE, 2021. http://dx.doi.org/10.1117/12.2589445.

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Pomeranz, Irith, and Sudhakar M. Reddy. "Test vector chains for increased targeted and untargeted fault coverage." In 2008 Asia and South Pacific Design Automation Conference (ASPDAC). IEEE, 2008. http://dx.doi.org/10.1109/aspdac.2008.4484034.

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González-Domínguez, Raúl, Ana Sayago, and Ángeles Fernández-Recamales. "Application of targeted and non-targeted approaches to investigate the effect of genotype and growing conditions on the strawberry metabolome." In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05838.

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Reports on the topic "Targeted and untargeted metabolomics"

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Matthews, Lisa, Guanming Wu, Robin Haw, Timothy Brunson, Nasim Sanati, Solomon Shorser, Deidre Beavers, Patrick Conley, Lincoln Stein, and Peter D'Eustachio. Illuminating Dark Proteins using Reactome Pathways. Reactome, October 2022. http://dx.doi.org/10.3180/poster/20221027matthews.

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Diseases are often the consequence of proteins or protein complexes that are non-functional or that function improperly. An active area of research has focused on the identification of molecules that can interact with defective proteins and restore their function. While 22% percent of human proteins are estimated to be druggable, less than fifteen percent are targeted by FDA-approved drugs, and the vast majority of untargeted proteins are understudied or so-called "dark" proteins. Elucidation of the function of these dark proteins, particularly those in commonly drug-targeted protein families, may offer therapeutic opportunities for many diseases. Reactome is the most comprehensive, open-access pathway knowledgebase covering 2585 pathways and including 14246 reactions, 11088 proteins, 13984 complexes, and 1093 drugs. Placing dark proteins in the context of Reactome pathways provides a framework of reference for these proteins facilitating the generation of hypotheses for experimental biologists to develop targeted experiments, unravel the potential functions of these proteins, and then design drugs to manipulate them. To this end, we have trained a random forest with 106 protein/gene pairwise features collected from multiple resources to predict functional interactions between dark proteins and proteins annotated in Reactome and then developed three scores to measure the interactions between dark proteins and Reactome pathways based on enrichment analysis and fuzzy logic simulations. Literature evidence via manual checking and systematic NLP-based analysis support predicted interacting pathways for dark proteins. To visualize dark proteins in the context of Reactome pathways, we have also developed a new website, idg.reactome.org, by extending the Reactome web application with new features illustrating these proteins together with tissue-specific protein and gene expression levels and drug interactions.
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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.

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Fruit quality is determined by numerous genetic factors that affect taste, aroma, ‎color, texture, nutritional value and shelf life. To unravel the genetic components ‎involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.
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